bluuebunny/crossref_metadata_2025 huggingface dataset:
Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract)
Authors: Byun Junghun , Cho Yong-Ho , Im Tae-Ho , Ko Hak-Lim , Shin Kyung-Seop , Jo Ohyun
Published: June 2020

This paper describes an iterative learning framework consisting of multi-layer prediction processes for underwater link adaptation. To obtain a dataset in real underwater environments, we implemented OFDM (Orthogonal Frequency Division Multiplexing)-based acoustic communications testbeds for the first time. Actual underwater data measured in Yellow Sea, South Korea, were used for training the iterative learning model. Remarkably, the iterative learning model achieves up to 25% performance improvement over the conventional benchmark model.
CORAL-DMOEA: Correlation Alignment-Based Information Transfer for Dynamic Multi-Objective Optimization (Student Abstract)
Authors: Chen Li , Xu Hua
Published: June 2020

One essential characteristic of dynamic multi-objective optimization problems is that Pareto-Optimal Front/Set (POF/POS) varies over time. Tracking the time-dependent POF/POS is a challenging problem. Since continuous environments are usually highly correlated, past information is critical for the next optimization process. In this paper, we integrate CORAL methodology into a dynamic multi-objective evolutionary algorithm, named CORAL-DMOEA. This approach employs CORAL to construct a transfer model which transfer past well-performed solutions to form an initial population for the next optimization process. Experimental results demonstrate that CORAL-DMOEA can effectively improve the quality of solutions and accelerate the evolution process.
When Low Resource NLP Meets Unsupervised Language Model: Meta-Pretraining then Meta-Learning for Few-Shot Text Classification (Student Abstract)
Authors: Deng Shumin , Zhang Ningyu , Sun Zhanlin , Chen Jiaoyan , Chen Huajun
Published: June 2020

Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used meta-learning to simulate the few-shot task, thus negating implicit common linguistic features across tasks. This paper addresses such problems using meta-learning and unsupervised language models. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. We show that our approach is not only simple but also produces a state-of-the-art performance on a well-studied sentiment classification dataset. It can thus be further suggested that pretraining could be a promising solution for few-shot learning of many other NLP tasks. The code and the dataset to replicate the experiments are made available at https://github.com/zxlzr/FewShotNLP.
Efficient Spatial-Temporal Rebalancing of Shareable Bikes (Student Abstract)
Authors: Deng Zichao , Tu Anqi , Liu Zelei , Yu Han
Published: June 2020

Bike sharing systems are popular worldwide now. However, these systems are facing a problem - rebalancing of shareable bikes among different docking stations. To address this challenge, we propose an approach for the spatial-temporal rebalancing of shareable bikes which allows domain experts to optimize the rebalancing operation with their knowledge and preferences without relying on learning by trial-and-error.
American Sign Language Recognition Using an FMCW Wireless Sensor (Student Abstract)
Authors: Du Yuanqi , Dang Nguyen , Wilkerson Riley , Pathak Parth , Rangwala Huzefa , Kosecka Jana
Published: June 2020

In today's digital world, rapid technological advancements continue to lessen the burden of tasks for individuals. Among these tasks is communication across perceived language barriers. Indeed, increased attention has been drawn to American Sign Language (ASL) recognition in recent years. Camera-based and motion detection-based methods have been researched extensively; however, there remains a divide in communication between ASL users and non-users. Therefore, this research team proposes the use of a novel wireless sensor (Frequency-Modulated Continuous-Wave Radar) to help bridge the gap in communication. In short, this device sends out signals that detect the user's body positioning in space. These signals then reflect off the body and back to the sensor, developing thousands of cloud points per second, indicating where the body is positioned in space. These cloud points can then be examined for movement over multiple consecutive time frames using a cell division algorithm, ultimately showing how the body moves through space as it completes a single gesture or sentence. At the end of the project, 95% accuracy was achieved in one-object prediction as well as 80% accuracy on cross-object prediction with 30% other objects' data introduced on 19 commonly used gestures. There are 30 samples for each gesture per person from three persons.
Action Graphs for Goal Recognition Problems with Inaccurate Initial States (Student Abstract)
Authors: Harman Helen , Simoens Pieter
Published: June 2020

Goal recognisers attempt to infer an agent's intentions from a sequence of observations. Approaches that adapt classical planning techniques to goal recognition have previously been proposed but, generally, they assume the initial world state is accurately defined. In this paper, a state is inaccurate if any fluent's value is unknown or incorrect. To cope with this, a cyclic Action Graph, which models the order constraints between actions, is traversed to label each node with their distance from each hypothesis goal. These distances are used to calculate the posterior goal probabilities. Our experimental results, for 15 different domains, demonstrate that our approach is unaffected by an inaccurately defined initial state.
A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract)
Authors: He Baokun , Wan Guihong , Schweitzer Haim
Published: June 2020

Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a “bias trick” that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.
A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract)
Authors: Jain Minni , Leekha Maitree , Goswami Mononito
Published: June 2020

Consumer reviews online may contain suggestions useful for improving the target products and services. Mining suggestions is challenging because the field lacks large labelled and balanced datasets. Furthermore, most prior studies have only focused on mining suggestions in a single domain. In this work, we introduce a novel up-sampling technique to address the problem of class imbalance, and propose a multi-task deep learning approach for mining suggestions from multiple domains. Experimental results on a publicly available dataset show that our up-sampling technique coupled with the multi-task framework outperforms state-of-the-art open domain suggestion mining models in terms of the F-1 measure and AUC.
A Critique of the Smooth Inverse Frequency Sentence Embeddings (Student Abstract)
Authors: Karipbayeva Aidana , Sorokina Alena , Assylbekov Zhenisbek
Published: June 2020

We critically review the smooth inverse frequency sentence embedding method of Arora, Liang, and Ma (2017), and show inconsistencies in its setup, derivation and evaluation.
Who Are Controlled by The Same User? Multiple Identities Deception Detection via Social Interaction Activity (Student Abstract)
Authors: Li Jiacheng , Yuan Chunyuan , Zhou Wei , Wang Jingli , Hu Songlin
Published: June 2020

Social media has become a preferential place for sharing information. However, some users may create multiple accounts and manipulate them to deceive legitimate users. Most previous studies utilize verbal or behavior features based methods to solve this problem, but they are only designed for some particular platforms, leading to low universalness.In this paper, to support multiple platforms, we construct interaction tree for each account based on their social interactions which is common characteristic of social platforms. Then we propose a new method to calculate the social interaction entropy of each account and detect the accounts which are controlled by the same user. Experimental results on two real-world datasets show that the method has robust superiority over state-of-the-art methods.
Travel Time Prediction on Un-Monitored Roads: A Spatial Factorization Machine Based Approach (Student Abstract)
Authors: Li Lile , Liu Wei
Published: June 2020

Real-time traffic monitoring is one of the most important factors for route planning and estimated time of arrival (ETA). Many major roads in large cities are installed with live traffic monitoring systems, inferring the current traffic congestion status and ETAs to other locations. However, there are also many other roads, especially small roads and paths, that are not monitored. Yet, live traffic status on such un-monitored small roads can play a non-negligible role in personalized route planning and re-routing when road incident happens. How to estimate the traffic status on such un-monitored roads is thus a valuable problem to be addressed. In this paper, we propose a model called Spatial Factorization Machines (SFM) to address this problem. A major advantage of the SFM model is that it incorporates physical distances and structures of road networks into the estimation of traffic status on un-monitored roads. Our experiments on real world traffic data demonstrate that the SFM model significantly outperforms other existing models on ETA of un-monitored roads.
Selecting Portfolios Directly Using Recurrent Reinforcement Learning (Student Abstract)
Authors: Li Lin
Published: June 2020

Portfolio selection has attracted increasing attention in machine learning and AI communities recently. Existing portfolio selection using recurrent reinforcement learning (RRL) heavily relies on single asset trading system to heuristically obtain the portfolio weights. In this paper, we propose a novel method, the direct portfolio selection using recurrent reinforcement learning (DPS-RRL), to select portfolios directly. Instead of trading single asset one by one to obtain portfolio weights, our method learns to quantify the asset allocation weight directly via optimizing the Sharpe ratio of financial portfolios. We empirically demonstrate the effectiveness of our method, which is able to outperform state-of-the-art portfolio selection methods.
Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)
Authors: Liu Chang , Yu Han , Dong Yi , Shen Zhiqi , Yu Yingxue , Dixon Ian , Gao Zhanning , Wang Pan , Ren Peiran , Xie Xuansong , Cui Lizhen , Miao Chunyan
Published: June 2020

There is an emerging trend for sellers to use videos to promote their products on e-commerce platforms such as Taobao.com. Current video production workflow includes the production of visual storyline by human directors. We propose a system to automatically generate visual storyline based on the input set of visual materials (e.g. video clips or still images) and then produce a promotional video. In particular, we propose an algorithm called Shot Composition, Selection and Plotting (ShotCSP), which generates visual storylines leveraging film-making principles to improve viewing experience and perceived persuasiveness.
Gifting in Multi-Agent Reinforcement Learning (Student Abstract)
Authors: Lupu Andrei , Precup Doina
Published: June 2020

This work performs a first study on multi-agent reinforcement learning with deliberate reward passing between agents. We empirically demonstrate that such mechanics can greatly improve the learning progression in a resource appropriation setting and provide a preliminary discussion of the complex effects of gifting on the learning dynamics.
An Analytical Workflow for Clustering Forensic Images (Student Abstract)
Authors: Mousavi Sara , Lee Dylan , Griffin Tatianna , Steadman Dawnie , Mockus Audris
Published: June 2020

Large collections of images, if curated, drastically contribute to the quality of research in many domains. Unsupervised clustering is an intuitive, yet effective step towards curating such datasets. In this work, we present a workflow for unsupervisedly clustering a large collection of forensic images. The workflow utilizes classic clustering on deep feature representation of the images in addition to domain-related data to group them together. Our manual evaluation shows a purity of 89% for the resulted clusters.
A QSAT Benchmark Based on Vertex-Folkman Problems (Student Abstract)
Authors: Narváez David E.
Published: June 2020

The purpose of this paper is to draw attention to a particular family of quantified Boolean formulas (QBFs) stemming from encodings of some vertex Folkman problems in extremal graph theory. We argue that this family of formulas is interesting for QSAT research because it is both conceptually simple and parametrized in a way that allows for a fine-grained diversity in the level of difficulty of its instances. Additionally, when coupled with symmetry breaking, the formulas in this family exhibit backbones (unique satisfying assignments) at the top-level existential variables. This benchmark is thus suitable for addressing questions regarding the connection between the existence of backbones and the hardness of QBFs.
MUSIC COLLAB: An IoT and ML Based Solution for Remote Music Collaboration (Student Abstract)
Authors: Nayar Nishtha , Lohani Divya
Published: June 2020

Communication using mediums like video and audio is essential for a lot of professions. In this paper, interaction with real-time audio transmission is looked upon using the tools in the domains of IoT and machine learning. Two transport layer protocols - TCP and UDP are examined for audio transmission quality. Further, different RNN models are examined for their efficiency in predicting music and being used as a substitute in case of loss of packets during transmission.
Attribute Noise Robust Binary Classification (Student Abstract)
Authors: Petety Aditya , Tripathi Sandhya , Hemachandra N.
Published: June 2020

We consider the problem of learning linear classifiers when both features and labels are binary. In addition, the features are noisy, i.e., they could be flipped with an unknown probability. In Sy-De attribute noise model, where all features could be noisy together with same probability, we show that 0-1 loss (l0−1) need not be robust but a popular surrogate, squared loss (lsq) is. In Asy-In attribute noise model, we prove that l0−1 is robust for any distribution over 2 dimensional feature space. However, due to computational intractability of l0−1, we resort to lsq and observe that it need not be Asy-In noise robust. Our empirical results support Sy-De robustness of squared loss for low to moderate noise rates.
ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract)
Authors: Saphal Rohan , Ravindran Balaraman , Mudigere Dheevatsa , Avancha Sasikanth , Kaul Bharat
Published: June 2020

Reinforcement learning algorithms are sensitive to hyper-parameters and require tuning and tweaking for specific environments for improving performance. Ensembles of reinforcement learning models on the other hand are known to be much more robust and stable. However, training multiple models independently on an environment suffers from high sample complexity. We present here a methodology to create multiple models from a single training instance that can be used in an ensemble through directed perturbation of the model parameters at regular intervals. This allows training a single model that converges to several local minima during the optimization process as a result of the perturbation. By saving the model parameters at each such instance, we obtain multiple policies during training that are ensembled during evaluation. We evaluate our approach on challenging discrete and continuous control tasks and also discuss various ensembling strategies. Our framework is substantially sample efficient, computationally inexpensive and is seen to outperform state of the art (SOTA) approaches
Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract)
Authors: Silva Felipe Leno Da , Hernandez-Leal Pablo , Kartal Bilal , Taylor Matthew E.
Published: June 2020

The sample-complexity of Reinforcement Learning (RL) techniques still represents a challenge for scaling up RL to unsolved domains. One way to alleviate this problem is to leverage samples from the policy of a demonstrator to learn faster. However, advice is normally limited, hence advice should ideally be directed to states where the agent is uncertain on the best action to be applied. In this work, we propose Requesting Confidence-Moderated Policy advice (RCMP), an action-advising framework where the agent asks for advice when its uncertainty is high. We describe a technique to estimate the agent uncertainty with minor modifications in standard value-based RL methods. RCMP is shown to perform better than several baselines in the Atari Pong domain.
SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract)
Authors: Singhal Shivangi , Kabra Anubha , Sharma Mohit , Shah Rajiv Ratn , Chakraborty Tanmoy , Kumaraguru Ponnurangam
Published: June 2020

In recent years, there has been a substantial rise in the consumption of news via online platforms. The ease of publication and lack of editorial rigour in some of these platforms have further led to the proliferation of fake news. In this paper, we study the problem of detecting fake news on the FakeNewsNet repository, a collection of full length articles along with associated images. We present SpotFake+, a multimodal approach that leverages transfer learning to capture semantic and contextual information from the news articles and its associated images and achieves the better accuracy for fake news detection. To the best of our knowledge, this is the first work that performs a multimodal approach for fake news detection on a dataset that consists of full length articles. It outperforms the performance shown by both single modality and multiple-modality models. We also release the pretrained model for the benefit of the community.
Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract)
Authors: Słowik Agnieszka , Mangla Chaitanya , Jamnik Mateja , Holden Sean B. , Paulson Lawrence C.
Published: June 2020

Modern theorem provers utilise a wide array of heuristics to control the search space explosion, thereby requiring optimisation of a large set of parameters. An exhaustive search in this multi-dimensional parameter space is intractable in most cases, yet the performance of the provers is highly dependent on the parameter assignment. In this work, we introduce a principled probabilistic framework for heuristic optimisation in theorem provers. We present results using a heuristic for premise selection and the Archive of Formal Proofs (AFP) as a case study.
Improving First-Order Optimization Algorithms (Student Abstract)
Authors: Ange Tato , Roger Nkambou
Published: June 2020

This paper presents a simple and intuitive technique to accelerate the convergence of first-order optimization algorithms. The proposed solution modifies the update rule, based on the variation of the direction of the gradient and the previous step taken during training. Results after tests show that the technique has the potential to significantly improve the performance of existing first-order optimization algorithms.
Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract)
Authors: Vinogradova Kira , Dibrov Alexandr , Myers Gene
Published: June 2020

Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks. The interpretation of their predictions, however, is an active area of research. Whereas various interpretation methods have been suggested for image classification, the interpretation of image segmentation still remains largely unexplored. To that end, we propose seg-grad-cam, a gradient-based method for interpreting semantic segmentation. Our method is an extension of the widely-used Grad-CAM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentation.
Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model (Student Abstract)
Authors: Wan Qing , Choe Yoonsuck
Published: June 2020

Consider a natural language sentence describing a specific step in a food recipe. In such instructions, recognizing actions (such as press, bake, etc.) and the resulting changes in the state of the ingredients (shape molded, custard cooked, temperature hot, etc.) is a challenging task. One way to cope with this challenge is to explicitly model a simulator module that applies actions to entities and predicts the resulting outcome (Bosselut et al. 2018). However, such a model can be unnecessarily complex. In this paper, we propose a simplified neural network model that separates action recognition and state change prediction, while coupling the two through a novel loss function. This allows learning to indirectly influence each other. Our model, although simpler, achieves higher state change prediction performance (67% average accuracy for ours vs. 55% in (Bosselut et al. 2018)) and takes fewer samples to train (10K ours vs. 65K+ by (Bosselut et al. 2018)).
Optimal Exploration Algorithm of Multi-Agent Reinforcement Learning Methods (Student Abstract)
Authors: Qisheng Wang , Qichao Wang , Xiao Li
Published: June 2020

Exploration efficiency challenges for multi-agent reinforcement learning (MARL), as the policy learned by confederate MARL depends on the interaction among agents. Less informative reward also restricts the learning speed of MARL in comparison with the informative label in supervised learning. This paper proposes a novel communication method which helps agents focus on different exploration subarea to guide MARL to accelerate exploration. We propose a predictive network to forecast the reward of current state-action pair and use the guidance learned by the predictive network to modify the reward function. An improved prioritized experience replay is employed to help agents better take advantage of the different knowledge learned by different agents. Experimental results demonstrate that the proposed algorithm outperforms existing methods in cooperative multi-agent environments.
Combining Fine-Tuning with a Feature-Based Approach for Aspect Extraction on Reviews (Student Abstract)
Authors: Wang Xili , Xu Hua , Sun Xiaomin , Tao Guangcan
Published: June 2020

One key task of fine-grained sentiment analysis on reviews is to extract aspects or features that users have expressed opinions on. Generally, fine-tuning BERT with sophisticated task-specific layers can achieve better performance than only extend one extra task-specific layer (e.g., a fully-connected + softmax layer) since not all tasks can easily be represented by Transformer encoder architecture and special task-specific layer can capture task-specific features. However, BERT fine-tuning may be unstable on a small-scale dataset. Besides, in our experiments, directly fine-tuning BERT on extending sophisticated task-specific layers did not take advantage of the features of task-specific layers and even restrict the performance of BERT module. To address the above consideration, this paper combines Fine-tuning with a feature-based approach to extract aspect. To the best of our knowledge, this is the first paper to combine fine-tuning with a feature-based approach for aspect extraction.
Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)
Authors: Yao Riheng , Song Shuangyong , Li Qiudan , Wang Chao , Chen Huan , Chen Haiqing , Zeng Daniel Dajun
Published: June 2020

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.
e‐learning comes of age: Web‐based education provided by the International League Against Epilepsy
Authors: Beniczky Sándor , Blümcke Ingmar , Rampp Stefan , Shisler Priscilla , Biesel Eva , Wiebe Samuel
Published: June 2020

Education tools and programs using interactive digital content, distributed on the internet, are increasingly becoming an integral part of postgraduate medical education. The coronavirus pandemic and global lockdown hoisted a major challenge for traditional teaching courses. A timely solution is to focus attention and reinforce web‐based teaching programs. For more than 15 years, the ILAE has been developing and managing a wide range of e‐learning programs. This paper provides an overview on the e‐learning portfolio of the ILAE, including tutored e‐courses, self‐paced interactive e‐courses and online multimedia resources, all linked to the ILAE curriculum and learning objectives addressing specific levels of professional experience. All e‐learning programs will become available through the new ILAE Academy platform (www.ilae-academy.org), in July 2020. E‐learning is an important tool for reaching the global educational mission of the ILAE.
Sensitivity of magnetoencephalography as a diagnostic tool for epilepsy: a prospective study
Authors: Koster Imte , Ossenblok Pauly , Brekelmans Geert J. , van der Linden Inge , Hillebrand Arjan , Wijnen Ben FM. , Colon Albert J.
Published: June 2020

Aim. The diagnostic process for epilepsy can be lengthy and stressful, which may delay the start of treatment. The objective of this study was to determine the benefit of routine magnetoencephalography (MEG) with regard to diagnostic gain, compared to routine electroencephalography (EEG), EEG following sleep deprivation (EEGsd), and 24‐hour EEG.

Methods. In this prospective study, patients were included from two centres (Academic Centre for Epileptology Kempenhaeghe, Heeze and Elisabeth‐Twee Steden Hospital, Tilburg) and MEG recording took place at a single centre (Amsterdam University Medical Centre, Vrije Universiteit Amsterdam) in The Netherlands. Consecutively referred patients from peripheral hospitals were included between August 2013 and March 2016. Patients were offered routine MEG in addition to EEG examination and MRI for the diagnosis of epilepsy. The final clinical diagnosis was based on all available clinical data and test results at the end of the diagnostic process. Sensitivity, specificity, and positive and negative predictive values were calculated for routine EEG, routine EEG plus additional EEG and MEG. In addition, diagnostic gain associated with MEG, relative to the other modalities, was calculated. Secondary outcome was congruence of localization of epileptiform discharges between MEG and MRI or final clinical diagnosis.

Results. Based on a cohort of 138 patients, sensitivity and specificity was shown to be 31.6% and 78.4% for routine MEG, 31.6% and 100% for routine EEG, and 52.6% and 97.3% for routine EEG plus additional EEG, respectively. Routine MEG demonstrated a diagnostic gain of 16.8% compared to routine EEG and 9.5% compared to routine EEG plus additional EEG. In 35.7% of patients with a lesion on MRI that was consistent with the final clinical diagnosis, MEG showed epileptiform discharges in the same area.

Conclusion. Routine MEG may provide additional value during the initial diagnosis of epilepsy.
Mujeres científicas en la economía circular: impulsando iniciativas educativas
Authors: Fernández Cézar Raquel , Sevilla Vera Yolanda , Solano Pinto Natalia
Published: June 2020

La economía circular consiste en aprovechar al máximo una materia prima que entra al ciclo de producción. Se concretaen la revalorización de los residuos industriales y domésticos. La mujerestá muy presente en el desarrollo de las tecnologías que fomentan ese cambio de modelo. Por ello, este proyecto trabaja con la hipótesis de que la combinación de mujer y economía circular en la creación de material didáctico audiovisual para el profesorado contribuirá positivamente a la concienciación de un amplio colectivo de ciudadanos sobre ambos temas.Así, propone, por un lado, identificar entre 8 y 12 investigadoras y/o tecnólogas españolas que estén trabajando en el ámbito de la sostenibilidad y/o la economía circular; se elaborarán videos que incluyan entrevistas en las que se recogerán sus historias de vida, y breves descripciones o esquemas de sus investigaciones, enfatizando los elementos químicos implicados. Estas se alojarán en un canal de youtube que se embeberá en una página web creada para el proyecto. Sobre estas entrevistas, se creará un material didáctico para el profesorado de educación primaria y secundaria, que se impartirá mediante el Centro Regional de Formación del Profesorado de Castilla La Mancha. La guía didáctica incluirá una actividad final que supondrá la elaboración de un video con el que alumnado y profesorado participarán en un concurso. El jurado estará compuesto por algunos investigadores e investigadoras miembros del equipo. Se medirá en el profesorado y alumnado participante su nivel de concienciación antes y tras participar en el proyecto (curso-concurso). Hasta este momento está concluida la primera fase, que puede verse en https://bit.ly/CientíficasEC . La segunda está en marcha https://bit.ly/CursoCientíficasEC, y retenida por las circunstancias actuales. Esperamos poder concluir el proyecto para poder medir el alcance de la acción desarrollada.
La satisfacción subjetiva del abuelo y la abuela "canguro"
Authors: García Díaz Verónica , Álvarez Blanco Lucía , Iglesias García Mª Teresa
Published: June 2020

Obligado a adaptarse al entramado social que lo rodea, el núcleo familiar actual enfrenta múltiples situaciones sociofamiliares que le hacen ampliar su red de apoyo y dar cabida a los abuelos como cuidadores e, incluso, educadores auxiliares de sus nietos y nietas. Tal implicación por su parte es generalmente considerada fuente de sentimientos positivos, si bien no puede obviarse su posible incompatibilidad con el descanso y disfrute esperados para la etapa evolutiva en la que se encuentran. Al respecto, y por la variedad de circunstancias personales que hoy rodean a abuelos y abuelas, el presente trabajo tiene como objetivo conocerlas e indagar en su grado de satisfacción con ellas. Para ello se ha realizado un estudio ex post facto con una muestra de 350 abuelos y abuelas, a los que se ha aplicado un cuestionario de elaboración propia. Su respuesta revela que la mayoría tiene una situación personal que facilita la asunción de este cometido, y con la que unos y otras están altamente satisfechos. Ser relativamente joven, no trabajar fuera de casa, no tener problemas de salud que impidan su actividad diaria, o disponer de tiempo para sí mismos y aprovecharlo, son algunos factores que evidencian tal bienestar en la muestra consultada. No obstante, cabe aludir también a la otra parte que comparte una visión más negativa de su realidad y que, por ende, hace que surja la ineludible necesidad de apoyar y orientar socioeducativamente a estos abuelos y abuelas.
El trabajo por proyectos en el currículo educativo: principios metodológicos que lo fundamentan
Authors: Martín Sánchez Beatríz , Pacheco Sanz Deilis Ivonne , Canedo Garcí Alejandro , Bleye Varona Yvet , Gago Morate Ángela
Published: June 2020

Aunque desde años atrás, pedagogos/as y maestros/as han defendido metodologías más innovadoras que las que se estaban desarrollando en las escuelas, es, en este momento, cuando de manera más ferviente, se está removiendo algo en gran parte del profesorado que está llevando a querer cambiar los procesos de enseñanza-aprendizaje actuales, queriendo innovar y probando nuevos enfoques pedagógicos. El trabajo por proyectos (TP) es un claro ejemplo de esas nuevas perspectivas pedagógicas que se están ejecutando cada vez más en las aulas. Dar fundamento teórico a esta metodología activa y de innovación, sustentar teóricamente la práctica docente en torno al trabajo por proyectos y proponer un modelo de intervención a partir de los principios metodológicos que lo fundamentan, son los objetivos generales o resultados que se esperan al finalizar el proceso de revisión de la evidencia disponible, sobre la relación entre los principios metodológicos que orientan la práctica docente para el desarrollo del Trabajo por Proyectos (construcción de aprendizajes significativos, funcionalidad del aprendizaje, capacidad de aprender a aprender, modificación de los esquemas de conocimiento que el alumno/a ya posee, actividad intensa por parte del alumno/a -observación, experimentación, investigación-, individualización, socialización, autoconcepto positivo, motivación, juego, globalización) y el éxito del proceso de enseñanza-aprendizaje. Se concentró la búsqueda en artículos publicados entre 1998 y 2017, los cuales se sintetizan en una tabla en la que se examinan intervenciones que se han realizado con el objetivo de promover el TP con la finalidad ulterior de que el mismo mejore el proceso de enseñanza-aprendizaje. El cuerpo de evidencias revisado arroja resultados de intervenciones que han comprobado ser efectivas en promover el TP dentro del currículo de cualquier nivel/modalidad educativa, sugiriendo que existe una relación de causalidad donde el TP predice el éxito del proceso educativo (Se recibieron fondos de la MOVILIDAD INVESTIGADORES Uva BANCO SANTANDER 2020).
Ansiedad, depresión y apatía en relación a la situación de fragilidad
Authors: Santamaría Peláez Mirian , González Bernal Jerónimo , Soto Cámara Raúl , Sánchez Iglesias Ana Isabel , Jahouh Maha
Published: June 2020

Esta investigación descriptiva transversal estudia la relación de la situación de fragilidad en adultos mayores institucionalizados y la ansiedad, depresión y apatía en los mismos con una muestra de 193 personas mayores institucionalizadas en cuatro residencias para personas mayores.La fragilidad se mide con la herramienta Short Physical Performance Battery; la ansiedad y la depresión con la escala Goldberg; y la apatía con la escala Dementhia Apathy Interview and Rating.Se trata de un estudio transversal y descriptivo que no realiza ningún tipo de intervención sobre las personas participantes y pretende establecer si existe o no relación entre las variables, pero no una relación de causalidad entre ellas.Se observa que las tres variables correlacionan con la puntuación total de la escala de fragilidad y con cada una de sus subescalas; de manera que, a mayor situación de fragilidad y/o dependencia, mayores niveles de ansiedad, depresión y apatía.Aparecen además diferencias significativas entre el grupo de personas frágiles y prefrágiles en lo referente a la apatía; y entre los grupos dependiente y prefrágil en relación a la ansiedad y la depresión.
Actitudes hacia el aprendizaje cooperativo y enfoque histórico
Authors: Haro Pontón Laura , Reyes Haro Lenin , León del Barco Benito
Published: June 2020

La educación de la personalidad mediante procesos de enseñanza aprendizaje (PEA) sustentados en el enfoque histórico cultural, estimulan la zona de desarrollo próximo (ZDP) y promueven el desarrollo humano mediante particulares formas de organización de los individuos y del conocimiento. El desarrollo de sentimientos, actitudes, valores, pensamientos, imaginación, atención, entre otros, es su preocupación esencial. Este estudio tiene como objetivo demostrar que la enseñanza con el enfoque histórico cultural desarrolla actitudes favorables al trabajo en equipo. La investigación es de corte cuasi-experimental con un diseño pretest-postest con grupo control.Se seleccionaron para la intervención a estudiantes de los colegios con quienes trabajaron los docentes en formación en la práctica pre-profesional. Intervinieron en la investigación 835 estudiantes, 430 conforman el grupo experimental integrado por estudiantes de Básica Superior en una media de edad de 13,4 (±0,95) años y de Bachillerato de 16,4 (±1,6)años. El grupo control integran 405 estudiantes de Básica y Bachillerato, cuyas edades son similares al grupo experimental. En el PEA del grupo experimental se utilizó un diseño de clase que, en las formas organizativas pone énfasis en técnicas de trabajo cooperativo. Se aplicó el cuestionario de Predisposición y Actitudes ante el trabajo en equipo (CACTE). Tras la intervención, el grupo experimental tuvo un incremento significativo (p0,001) respecto al grupo control, su tamaño del efecto fue mediano (r=0,27), situación que permite afirmar que trabajar en equipo con técnicas de aprendizaje cooperativo desarrolla sociabilidad, eleva la autoestima y garantiza aprendizajes significativos para la persona, aminora el individualismo y la competencia.
Acciones educativas innovadoras para el día internacional de la mujer y la niña en la ciencia infantil y primaria
Authors: Sevilla Vera Yolanda , Solano Pinto Natalia , Garrido Dunia , Fernández Cézar Raquel
Published: June 2020

Antecedentes. El conocimiento científico y la igualdad de género son cruciales para alcanzar los Objetivos de Desarrollo Sostenible. Las iniciativas de innovación educativa son las que acercan estos a toda la comunidad educativa, inspirando y promoviendo la participación de las mujeres y las niñas en la ciencia, eliminando la brecha de género en el ámbito científico y otros sectores. Objetivos. Fomentar la organización de actividades que promuevan el desarrollo científico y tecnológico en la infancia con la colaboración de la familia, desarrollando una mirada curiosa y transformadora. Metodología. La muestra estuvo formada por 182 participantes (85 niñas y 97 niños) de infantil y primaria; 16 miembros del profesorado y familias. Instrumento y procedimiento. En el marco del “Día Internacional de la mujer y la niña en la ciencia”, se llevó acabo en el CEIP un amplio programa de actividades acerca de la ciencia y conocer la importancia de la mujer en esta materia, intentando sensibilizar y fomentar el pensamiento científico y tecnológico en toda la comunidad educativa. Resultados. El alumnado de los cursos más altos manifestaba una desigualdad de género y en algunos casos les costaba identificar dicha desigualdad en el desempeño de tareas, responsabilidades y profesiones, observándose lo contrario en los más pequeños. Conclusiones. Los datos arrojan la necesidad de seguir fomentando este tipo de actividades y programas.
Protective and risk factors of toddlers' sleep and parental stress
Authors: De Stasio Simona , Ragni Benedetta , Boldrini Francesca
Published: June 2020

En el presente estudio se examinó la red de relaciones entre la calidad del sueño y la regulación de las emociones de los niños pequeños, las prácticas y la participación de los padres a la hora de acostarse, el apoyo social y el estrés de los padres, integrando una novedosa combinación de dimensiones psicosociales en un modelo predictivo de la calidad del sueño y el estrés de los padres en una muestra de 80 familias con niños de 2 a 3 años de edad. Los resultados indicaron que la regulación de las emociones de los lactantes, así como las variables de contexto próximo, teniendo en cuenta el funcionamiento psicosocial tanto materno como paterno, predecían la varianza de los niveles de estrés de los padres. Además, los resultados mostraron que el número de vigilancias nocturnas del niño comunicadas por las madres y el tiempo solicitado por los niños pequeños para dormirse, estaban significativamente relacionados con los niveles de estrés de los padres según lo referido por ambos padres.
Evaluación del funcionamiento familiar con el faces IV en adolescentes con problemática familiar
Authors: Vegas Miguel María Isabel , De la Fuente Anuncibay Raquel
Published: June 2020

Introducción: Las familias multiproblemáticas han recibido creciente atención por los poderes públicos, aunque hay una carencia de instrumentos específicos para evaluar su funcionamiento. En este estudio se propone el modelo Circunflejo de Olson (paquete FACES) como una herramienta de evaluación eficaz para este colectivo. Método: se seleccionaron 49 adolescentes entre 14 y 18 años, con problemática familiar y se compararon sus resultados en funcionamiento familiar (FACES-IV), en comunicación y satisfacción familiar (mediante FCS y FSS), tiempo que comparten padres e hijos, número de veces que se come en familia, factores familiares estresantes y maltrato físico recibido, con un grupo general, compuesto por 857 adolescentes, seleccionados de 22 centros educativos de Burgos (España), mediante muestreo en dos etapas. En total: 46,5% varones y 53,5% mujeres. Resultados: los adolescentes pertenecientes a familias problemáticas presentan menos cohesión familiar, menos flexibilidad, más desapego, más caos, menos comunicación y menos satisfacción familiar que la población general. Se caracterizan también por la falta de disponibilidad del padre, por comer menos veces en familia, haber sufrido más maltrato físico y ser las dificultades económicas y la conflictividad familiar, los principales estresores familiares. Conclusión: el paquete FACES IV es un instrumento eficaz para la detección y evaluación de familias problemáticas y puede ser aplicado a adolescentes españoles de amplio espectro social y familiar.
SPOR LİGLERİNDE TÜKETİCİ ODAKLI MARKA ÇAĞRIŞIMLARI
Authors: YAĞIZ Kadir
Published: June 2020

Spor tüketicileri olarak kabul edilen spor seyircileri tarafından takip edilen lig markalarıyla ilgili algılanan marka çağrışımlarının bilinçli yönetimi, başta marka sadakati olmak üzere liglerin marka sermayesine önemli katkılar sağlamaktadır. Bu güne kadar Türkiye’de spor liglerinin marka çağrışımlarına yönelik bir çalışma yapılmamıştır. Dolayısıyla, bu çalışmanın amacı spor lig marka çağrışımlarını incelemek ve lig marka çağrışımlarını futbol ve basketbol lig izleyicilerine göre farklı olup olmadığını test etmektir. Çalışmaya 176 futbol ligi izleyicisi, 184 basketbol ligi izleyicisi olmak üzere toplam 360 lig izleyicisi gönüllü olarak katılmıştır. Lig marka çağrışımlarını ölçmek için, literatürden uyarlanan marka çağrışım boyutları kullanılmıştır. Tek örneklem t-testi sonucuna göre kaçış, logo, stadyum atmosferi, nostalji, rekabet, gelenek, ürün sunumu, rekabetçi denge, sosyalleşme, yıldız oyuncu, oyunun temsili, özel takım ilgisi ve eğitim lig izleyicileri için önemli olabilecek çağrışımlar olarak bulunmuştur. Bağımsız örneklemler t-testi sonucuna göre, futbol ligi izleyicilerinin rekabetçi denge, oyunun temsili, özel takım ilgisi, rekabet, gelenek, lig özdeşleşmesi, yıldız oyuncu çağrışımlarını değerlendirme düzeyleri, basketbol ligi izleyicilerinin değerlendirme düzeylerinden daha büyük bulunurken; futbol ligi izleyicilerinin lig yönetimi çağrışımını değerlendirme düzeyleri, basketbol ligi izleyicilerinin değerlendirme düzeylerinden daha düşük bulunmuştur.
OECD ÜLKELERİNİN SAĞLIK GÖSTERGELERİ AÇISINDAN BULANIK KÜMELEME ANALİZİ VE TOPSIS YÖNTEMİNE GÖRE DEĞERLENDİRİLMESİ
Authors: DEĞİRMENCİ Nurdan , YAKICI AYAN Tuba
Published: June 2020

Bu çalışmanın amacı Bulanık Kümeleme Analizi ve TOPSIS yöntemi ile sağlık göstergelerine göre Ekonomik İşbirliği ve Kalkınma Örgütü ülkelerini sınıflandırmak, sıralamak ve Türkiye’nin bu sınıflandırma içerisindeki konumunu değerlendirmektir. Çalışmada kullanılan sağlık göstergeleri her biri kişi başına olmak üzere sağlık harcamaları, ilaç harcamaları, doktor sayısı, hemşire sayısı ve hastane yatak sayısıdır. 2015 yılı verileri Ekonomik İşbirliği ve Kalkınma Örgütü’nün web sitesinden elde edilmiştir. Uygulanan Bulanık Kümeleme Analizi sonucunda Türkiye’nin Kore, Meksika ve Polonya ile aynı kümede yer aldığı görülmüştür. TOPSIS analizine göre ise Amerika Birleşik Devletleri, Japonya, Kanada ilk üç sırayı alırken Türkiye ise Meksika’nın ardından son sırada yer almıştır. Diğer Ekonomik İşbirliği ve Kalkınma Örgütü ülkeleri ile kıyaslandığında Türkiye’nin kişi başına hastane yatak sayısı dışındaki sağlık kaynaklarının yetersiz olduğu ve bu açıdan düzeltme ve iyileştirmeler yapması gerektiği anlaşılmıştır.
TÜKETİCİLERİN KÜRESELLEŞMEYE VE KÜRESEL MARKALARA YÖNELİK TUTUMLARININ MARKA GÜVENİ ÇERÇEVESİNDE İNCELENMESİ
Authors: ÖZKAN TEKTAŞ Oznur , KONYA İşıl
Published: June 2020

Küreselleşmenin etkilerinin artan hızla devam etmesi, tüketicilerin küresel markalara yönelik olumlu tutum ve güven geliştirmelerinin önemini daha da artırmaktadır. Bu önemden hareketle, çalışmanın amacı, tüketicilerin küresel markaya yönelik tutum ve küreselleşmeye yönelik tutumlarının satın alma niyetleri üzerindeki doğrudan etkileriyle küresel marka güveninin bu ilişkideki aracılık rolünün incelenmesidir. Çalışmanın uygulaması sigortacılık sektöründe yapılmıştır. Çalışmanın modeli, 198 bireysel sigorta tüketicisinden anket aracılığıyla toplanan veri kullanılarak test edilmiştir. Uygulanan analizler sonucunda, küresel markaya ve küreselleşmeye yönelik olumlu tutumun, satın alma niyetini olumlu yönde etkilediği görülmüştür. Ayrıca, küresel marka güveninin, küresel markaya yönelik tutumun satın alma niyetine etkisine ve küreselleşmeye yönelik tutumun satın alma niyetine etkisine aracılık ettiği tespit edilmiştir. Çalışma, markaya duyulan güvenin, küreselleşmeye ve markaya yönelik tutum ile satın alma niyeti arasındaki aracılık etkisini incelemesi açısından literatüre katkıda bulunmaktadır.
Protection of minors in the European digital audiovisual context: a necessary dialogue between parents, academy, regulators and industry
Authors: Labio-Bernal Aurora , Romero-Domínguez Lorena R. , García-Orta María José
Published: June 2020

This paper represents the initial phase of a larger project being developed by the “Media,

communication policy and democracy in the European Union” research group, which is currently

working on the study “Communication policies, SVOD platforms and values education for

minors in the single digital market (2020-2022)”. We wish to pursue in this study that, beyond

technological considerations, it is necessary to expand the scope of child protection by establishing

mutual collaboration between regulators, distributors and video on demand services, as well

as consumers and parents’ organisations, in an effort to further enhance cooperation and mutual

understanding (European Regulators Group for Audiovisual Media Services, 2017b, p. 75). It is

for this reason that we believe that the academic sphere can also be invited into this wide-ranging

discussion on child protection to contribute reflections on a key aspect: audiovisual and media

education, an essential pillar of protection in addition to filters, external limits, and electronic

labelling. We thus uphold a vision that not only considers the “digital stuff” but also highlights

the need for “ethos stuff” (Goggin, 2008, p.89). In this respect, we have considered it essential a

literature review on the concept of media literacy. Secondly, our qualitative methodology involves

an analysis of the instructions issued by the European Union and their implementation in Spain.

In this stage, we have conducted desk research based on a narrative analysis of the documents

and programs of different institutions in order to chart the evolution of the question in recent

years, at a time when the digital environment has changed more quickly than ever before. This

same type of analysis is also conducted on the initiatives of European and Spanish companies to

determine whether they are implementing child protection strategies.