bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Efficiency of Volunteer-Based Food Rescue Operations
Authors: Shi Zheyuan Ryan , Yuan Yiwen , Lo Kimberly , Lizarondo Leah , Fang Fei
Published: June 2020

Food waste and food insecurity are two challenges that coexist in many communities. To mitigate the problem, food rescue platforms match excess food with the communities in need, and leverage external volunteers to transport the food. However, the external volunteers bring significant uncertainty to the food rescue operation. We work with a large food rescue organization to predict the uncertainty and furthermore to find ways to reduce the human dispatcher's workload and the redundant notifications sent to volunteers. We make two main contributions. (1) We train a stacking model which predicts whether a rescue will be claimed with high precision and AUC. This model can help the dispatcher better plan for backup options and alleviate their uncertainty. (2) We develop a data-driven optimization algorithm to compute the optimal intervention and notification scheme. The algorithm uses a novel counterfactual data generation approach and the branch and bound framework. Our result reduces the number of notifications and interventions required in the food rescue operation. We are working with the organization to deploy our results in the near future.
AISpace2: An Interactive Visualization Tool for Learning and Teaching Artificial Intelligence
Authors: Zhou Chenliang , Kuang Dominic , Liu Jingru , Yang Hanbo , Zhang Zijia , Mackworth Alan , Poole David
Published: June 2020

AIspace is a set of tools used to learn and teach fundamental AI algorithms. The original version of AIspace was written in Java. There was not a clean separation of the algorithms and visualization; it was too complicated for students to modify the underlying algorithms. Its next generation, AIspace2, is built on AIPython, open source Python code that is designed to be as close as possible to pseudocode. AISpace2, visualized in JupyterLab, keeps the simple Python code, and uses hooks in AIPython to allow visualization of the algorithms. This allows students to see and modify the high-level algorithms in Python, and to visualize the output in a graphical form, aiming to better help them to build confidence and comfort in AI concepts and algorithms. So far we have tools for search, constraint satisfaction problems (CSP), planning and Bayesian network. In this paper we outline the tools and give some evaluations based on user feedback.
Model AI Assignments 2020
Authors: Neller Todd W. , Keeley Stephen , Guerzhoy Michael , Hoenig Wolfgang , Li Jiaoyang , Koenig Sven , Soni Ameet , Thomason Krista , Zhang Lisa , Sebastian Bibin , Resnick Cinjon , Oliver Avital , Bhupatiraju Surya , Agrawal Kumar Krishna , Allingham James , Yoon Sejong , Chen Jonathan , Larsen Tom , Neumann Marion , Norouzi Narges , Hausen Ryan , Evett Matthew
Published: June 2020

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.
Back to the Future for Dialogue Research
Authors: Cohen Philip R
Published: June 2020

This “blue sky” paper argues that future conversational systems that can engage in multiparty, collaborative dialogues will require a more fundamental approach than existing technology. This paper identifies significant limitations of the state of the art, and argues that our returning to the plan-based approach to dialogue will provide a stronger foundation. Finally, I suggest a research strategy that couples neural network-based semantic parsing with plan-based reasoning in order to build a collaborative dialogue manager.
Cognitive Compliance: Assessing Regulatory Risk in Financial Advice Documents
Authors: Sherchan Wanita , Chen Sue Ann , Harris Simon , Alam Nebula , Tran Khoi-Nguyen , Butler Christopher J.
Published: June 2020

This paper describes Cognitive Compliance - a solution that automates the complex manual process of assessing regulatory compliance of personal financial advice. The solution uses natural language processing (NLP), machine learning and deep learning to characterise the regulatory risk status of personal financial advice documents with traffic light rating for various risk factors. This enables comprehensive coverage of the review and rapid identification of documents at high risk of non-compliance with government regulations.
Combining Machine Learning Models Using combo Library
Authors: Zhao Yue , Wang Xuejian , Cheng Cheng , Ding Xueying
Published: June 2020

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or {https://github.com/yzhao062/combo}.
Interactive Scene Generation via Scene Graphs with Attributes
Authors: Ashual Oron , Wolf Lior
Published: June 2020

We introduce a simple yet expressive image generation method. On the one hand, it does not require the user to paint the masks or define a bounding box of the various objects, since the model does it by itself. On the other hand, it supports defining a coarse location and size of each object. Based on this, we offer a simple, interactive GUI, that allows a layman user to generate diverse images effortlessly.From a technical perspective, we introduce a dual embedding of layout and appearance. In this scheme, the location, size, and appearance of an object can change independently of each other. This way, the model is able to generate innumerable images per scene graph, to better express the intention of the user.In comparison to previous work, we also offer better quality and higher resolution outputs. This is due to a superior architecture, which is based on a novel set of discriminators. Those discriminators better constrain the shape of the generated mask, as well as capturing the appearance encoding in a counterfactual way.Our code is publicly available at https://www.github.com/ashual/scene_generation.
Constraint Programming for an Efficient and Flexible Block Modeling Solver
Authors: Mattenet Alex Lucía , Davidson Ian , Nijssen Siegfried , Schaus Pierre
Published: June 2020

Constraint Programming (CP) is a powerful paradigm for solving combinatorial problems. In CP, the user creates a model by declaring variables with their domains and expresses the constraints that need to be satisfied in any solution. The solver is then in charge of finding feasible solutions—a value in the domain of each variable that satisfies all the constraints. The discovery of solutions is done by exploring a search tree that is pruned by the constraints in charge of removing impossible values. The CP framework has the advantage of exposing a rich high-level declarative constraint language for modeling, as well as efficient purpose-specific filtering algorithms that can be reused in many problems. In this work, we harness this flexibility and efficiency for the Block Modeling problem. It is a variant of the graph clustering problem that has been used extensively in many domains including social science, spatio-temporal data analysis and even medical imaging. We present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches. We show its use in the analysis of real datasets.
Abstraction and Refinement in Games with Dynamic Weighted Terrain
Authors: Sturtevant Nathan R. , Sigurdson Devon , Taylor Bjorn , Gibson Tim
Published: June 2020

This abstract looks at one version of the pathfinding problem in games and discusses how it motived our recent work at the AIIDE 2019 conference.
Towards Adversarially Robust Knowledge Graph Embeddings
Authors: Bhardwaj Peru
Published: June 2020

Knowledge graph embedding models enable representation learning on multi-relational graphs and are used in security sensitive domains. But, their security analysis has received little attention. I will research security of these models by designing adversarial attacks against them, improving their adversarial robustness and evaluating the effect of proposed improvement on their interpretability.
Abstract Rule Based Pattern Learning with Neural Networks
Authors: Kopparti Radha Manisha
Published: June 2020

In this research work, the problem of learning abstract rules using neural networks is studied and a solution called ‘Relation Based Patterns’ (RBP) which model abstract relationships based on equality is proposed.
LatRec: Recognizing Goals in Latent Space (Student Abstract)
Authors: Amado Leonardo , Meneguzzi Felipe
Published: June 2020

Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. We overcome these limitations by combining goal recognition techniques from automated planning, and deep autoencoders to carry out unsupervised learning to generate domain theories from data streams and use the resulting domain theories to deal with incomplete and noisy observations. Moving forward, we aim to develop a new data-driven goal recognition technique that infers the domain model using the same set of observations used in recognition itself.
Optimizing the Feature Selection Process for Better Accuracy in Datasets with a Large Number of Features (Student Abstract)
Authors: Chen Xi , Doryab Afsaneh
Published: June 2020

Most feature selection methods only perform well on datasets with relatively small set of features. In the case of large feature sets and small number of data points, almost none of the existing feature selection methods help in achieving high accuracy. This paper proposes a novel approach to optimize the feature selection process through Frequent Pattern Growth algorithm to find sets of features that appear frequently among the top features selected by the main feature selection methods. Our experimental evaluation on two datasets containing a small and very large number of features shows that our approach significantly improves the accuracy results of the dataset with a very large number of features.
Multi-Agent Pattern Formation with Deep Reinforcement Learning (Student Abstract)
Authors: Diallo Elhadji Amadou Oury , Sugawara Toshiharu
Published: June 2020

We propose a decentralized multi-agent deep reinforcement learning architecture to investigate pattern formation under the local information provided by the agents' sensors. It consists of tasking a large number of homogeneous agents to move to a set of specified goal locations, addressing both the assignment and trajectory planning sub-problems concurrently. We then show that agents trained on random patterns can organize themselves into very complex shapes.
Hypergraph Convolutional Network for Multi-Hop Knowledge Base Question Answering (Student Abstract)
Authors: Han Jiale , Cheng Bo , Wang Xu
Published: June 2020

Graph convolutional networks (GCN) have been applied in knowledge base question answering (KBQA) task. However, the pairwise connection between nodes of GCN limits the representation capability of high-order data correlation. Furthermore, most previous work does not fully utilize the semantic relation information, which is vital to reasoning. In this paper, we propose a novel multi-hop KBQA model based on hypergraph convolutional network. By constructing a hypergraph, the form of pairwise connection between nodes and nodes is converted to the high-level connection between nodes and edges, which effectively encodes complex related data. To better exploit the semantic information of relations, we apply co-attention method to learn similarity between relation and query, and assign weights to different relations. Experimental results demonstrate the effectivity of the model.
Leveraging BERT with Mixup for Sentence Classification (Student Abstract)
Authors: Jindal Amit , Gnaneshwar Dwaraknath , Sawhney Ramit , Shah Rajiv Ratn
Published: June 2020

Good generalization capability is an important quality of well-trained and robust neural networks. However, networks usually struggle when faced with samples outside the training distribution. Mixup is a technique that improves generalization, reduces memorization, and increases adversarial robustness. We apply a variant of Mixup called Manifold Mixup to the sentence classification problem, and present the results along with an ablation study. Our methodology outperforms CNN, LSTM, and vanilla BERT models in generalization.
Determining the Possibility of Transfer Learning in Deep Reinforcement Learning Using Grad-CAM (Student Abstract)
Authors: Joo Ho-Taek , Kim Kyung-Joong
Published: June 2020

Humans are usually good at guessing whether the two games are similar to each other and easily estimate how much time to master new games based on the similarity. Although Deep Reinforcement Learning (DRL) has been successful in various domains, it takes much training time to get a successful controller for a single game. Therefore, there has been much demand for the use of transfer learning to speed up reinforcement learning across multiple tasks. If we can automatically determine the possibility of transfer learning in DRL domain before training, it could efficiently transfer knowledge across multiple games. In this work, we propose a simple testing method, Determining the Possibility of Transfer Learning (DPTL), to determine the transferability of models based on Grad-CAM visualization of the CNN layer from the source model. Experimental results on Atari games show that the transferability measure is successfully suggesting the possibility of transfer learning.
Multidimensional Analysis of Trust in News Articles (Student Abstract)
Authors: Kaur Avneet , Leekha Maitree , Chawla Utkarsh , Agarwal Ayush , Saxena Mudit , Madaan Nishtha , Kannan Kalapriya , Mehta Sameep
Published: June 2020

The advancements in the field of Information Communication Technology have engendered revolutionary changes in the journalism industry, not only on the part of the journalists and the media personnel, but also on the people consuming these news stories, who today, are only a click away from all the updates they need. However, these advances have also exposed the prevailing venality, wearying off the trust of the public in news media. How then, does an individual discern that which, out of the countless news stories for an incident, should be trusted? This work introduces a system that presents the user a multidimensional analysis for trust in news from various media sources based on the textual content of the articles, assessment of the journalists' perspectives and the temporal diversity of the issues being covered by the media houses publishing the news articles. Our experiments on a self-collected dataset confirm that the system aids in a comprehensive analysis of trust.
Learning to Classify the Wrong Answers for Multiple Choice Question Answering (Student Abstract)
Authors: Kim Hyeondey , Fung Pascale
Published: June 2020

Multiple-Choice Question Answering (MCQA) is the most challenging area of Machine Reading Comprehension (MRC) and Question Answering (QA), since it not only requires natural language understanding, but also problem-solving techniques. We propose a novel method, Wrong Answer Ensemble (WAE), which can be applied to various MCQA tasks easily. To improve performance of MCQA tasks, humans intuitively exclude unlikely options to solve the MCQA problem. Mimicking this strategy, we train our model with the wrong answer loss and correct answer loss to generalize the features of our model, and exclude likely but wrong options. An experiment on a dialogue-based examination dataset shows the effectiveness of our approach. Our method improves the results on a fine-tuned transformer by 2.7%.
Towards Minimal Supervision BERT-Based Grammar Error Correction (Student Abstract)
Authors: Li Yiyuan , Anastasopoulos Antonios , Black Alan W.
Published: June 2020

Current grammatical error correction (GEC) models typically consider the task as sequence generation, which requires large amounts of annotated data and limit the applications in data-limited settings. We try to incorporate contextual information from pre-trained language model to leverage annotation and benefit multilingual scenarios. Results show strong potential of Bidirectional Encoder Representations from Transformers (BERT) in grammatical error correction task.
Towards Consistent Variational Auto-Encoding (Student Abstract)
Authors: Liu Yijing , Lin Shuyu , Clark Ronald
Published: June 2020

Variational autoencoders (VAEs) have been a successful approach to learning meaningful representations of data in an unsupervised manner. However, suboptimal representations are often learned because the approximate inference model fails to match the true posterior of the generative model, i.e. an inconsistency exists between the learnt inference and generative models. In this paper, we introduce a novel consistency loss that directly requires the encoding of the reconstructed data point to match the encoding of the original data, leading to better representations. Through experiments on MNIST and Fashion MNIST, we demonstrate the existence of the inconsistency in VAE learning and that our method can effectively reduce such inconsistency.
Transformer-Capsule Model for Intent Detection (Student Abstract)
Authors: Obuchowski Aleksander , Lew Michał
Published: June 2020

Intent recognition is one of the most crucial tasks in NLU systems, which are nowadays especially important for designing intelligent conversation. We propose a novel approach to intent recognition which involves combining transformer architecture with capsule networks. Our results show that such architecture performs better than original capsule-NLU network implementations and achieves state-of-the-art results on datasets such as ATIS, AskUbuntu ,and WebApp.
Opening the Black Box: Automatically Characterizing Software for Algorithm Selection (Student Abstract)
Authors: Pulatov Damir , Kotthoff Lars
Published: June 2020

Meta-algorithmics, the field of leveraging machine learning to use algorithms more efficiently, has achieved impressive performance improvements in many areas of AI. It treats the algorithms to improve on as black boxes – nothing is known about their inner workings. This allows meta-algorithmic techniques to be deployed in many applications, but leaves potential performance improvements untapped by ignoring information that the algorithms could provide. In this paper, we open the black box without sacrificing the universal applicability of meta-algorithmic techniques by automatically analyzing the source code of the algorithms under consideration and show how to use it to improve algorithm selection performance. We demonstrate improvements of up to 82% on the standard ASlib benchmark library.
A Multi-Task Learning Approach to Sarcasm Detection (Student Abstract)
Authors: Savini Edoardo , Caragea Cornelia
Published: June 2020

Sarcasm detection plays an important role in natural language processing as it has been considered one of the most challenging subtasks in sentiment analysis and opinion mining applications. Our work aims to detect sarcasm in social media sites and discussion forums, exploiting the potential of deep neural networks and multi-task learning. Specifically, relying on the strong correlation between sarcasm and (implied negative) sentiment, we explore a multi-task learning framework that uses sentiment classification as an auxiliary task to inform the main task of sarcasm detection. Our proposed model outperforms many previous baseline methods on an existing large dataset annotated with sarcasm.
Using Chinese Glyphs for Named Entity Recognition (Student Abstract)
Authors: Song Chan Hee , Sehanobish Arijit
Published: June 2020

Most Named Entity Recognition (NER) systems use additional features like part-of-speech (POS) tags, shallow parsing, gazetteers, etc. Adding these external features to NER systems have been shown to have a positive impact. However, creating gazetteers or taggers can take a lot of time and may require extensive data cleaning. In this work instead of using these traditional features we use lexicographic features of Chinese characters. Chinese characters are composed of graphical components called radicals and these components often have some semantic indicators. We propose CNN based models that incorporate this semantic information and use them for NER. Our models show an improvement over the baseline BERT-BiLSTM-CRF model. We present one of the first studies on Chinese OntoNotes v5.0 and show an improvement of + .64 F1 score over the baseline. We present a state-of-the-art (SOTA) F1 score of 71.81 on the Weibo dataset, show a competitive improvement of + 0.72 over baseline on the ResumeNER dataset, and a SOTA F1 score of 96.49 on the MSRA dataset.
Supervised Discovery of Unknown Unknowns through Test Sample Mining (Student Abstract)
Authors: Wang Zheng , Abrahao Bruno , Kamar Ece
Published: June 2020

Given a fixed hypothesis space, defined to model class structure in a particular domain of application, unknown unknowns (u.u.s) are data examples that form classes in the feature space whose structure is not represented in a trained model. Accordingly, this leads to incorrect class prediction with high confidence, which represents one of the major sources of blind spots in machine learning. Our method seeks to reduce the structural mismatch between the training model and that of the target space in a supervised way. We illuminate further structure through cross-validation on a modified training model, set up to mine and trap u.u.s in a marginal training class, created from examples of a random sample of the test set. Contrary to previous approaches, our method simplifies the solution, as it does not rely on budgeted queries to an Oracle whose outcomes inform adjustments to training. In addition, our empirically results exhibit consistent performance improvements over baselines, on both synthetic and real-world data sets.
Pitfalls in the diagnosis of Jeavons syndrome: a study of 32 cases and review of the literature
Authors: Reyhani Aylin , Özkara Çiğdem
Published: June 2020

Aims. Jeavons syndrome (JS) is mainly characterized by eyelid myoclonia with or without absences. It is thought to be underdiagnosed rather than have a rare prevalence. We aimed to investigate the electroclinical features of JS to determine possible factors influencing the diagnosis.

Methods. We retrospectively identified the medical records of 32 cases (0.55%) from 5,796 patients with epilepsy. The inclusion criteria were: (1) eyelid myoclonia with or without absences; (2) generalized paroxysmal activity on EEG; and (3) discharges triggered by eyelid closure and/or intermittent photic stimulation.

Results. Eighteen (56.2%) of the patients were female. The mean age at seizure onset was 8.7±5.3 years and the mean age at admission to hospital was 17.8±10.7 years. A family history of epilepsy was present in 15 (46.8%) patients. Eyelid myoclonias were noticed in six (18.7%) patients by themselves. Based on the analysis of video‐EEG recordings, 26 (81.2%) patients were sensitive to eye closure, 22 (68.7%) had photoparoxysmal responses, and 16 (50%) presented with absence seizures. Ten (31.2%) patients had focal epileptic discharges. Eight (25%) patients were on monotherapy. Seven (21.8%) patients achieved seizure freedom. Three patients underwent ketogenic diet therapy, which was effective in two patients. A vagus nerve stimulator was implanted into three patients, one of whom reported seizure reduction.

Conclusions. Eyelid myoclonias are the main seizure type of JS but are usually overlooked. The time interval between seizure onset and clinical diagnosis suggests that this syndrome continues to be under‐recognized. The genetic heterogeneity and phenotypic variability are likely to be more extensive than currently recognized, making the diagnosis more phalangine. [Published with video sequence].
ATP6V1B2‐related epileptic encephalopathy
Authors: Inuzuka Luciana Midori , Macedo‐Souza Lúcia Inês , Della‐Rippa Bruno , Monteiro Fabiola Paoli , Delgado Daniel de Souza , Godoy Luis Filipe , Ramos Luiza , de Athayde Costa Larissa Sampaio , Garzon Eliana , Kok Fernando
Published: June 2020

ATP6V1B2 encodes a subunit of the lysosomal transmembrane proton pump necessary for adequate functioning of several acid hydrolases. De novo monoallelic variants of this gene have been associated with two distinct phenotypes: Zimmermann‐Laband syndrome 2 (ZLS2), an intellectual deficiency/multiple malformation syndrome, and dominant deafness onychodystrophy (DDOD), a multiple malformation syndrome without cognitive involvement. Epilepsy is not observed in DDOD, is variably present in ZLS2, but is a common feature in Zimmermann‐Laband syndrome 1 (ZLS1) (caused by monoallelic pathogenic variants in KCNH1) and Zimmermann‐Laband syndrome‐like (ZLSL) (associated with KCNK4 variants).

Herein, we report a case of an infant with severe epileptic encephalopathy with microcephaly and profound developmental delay, associated with a novel de novo loss‐of‐function variant in ATP6V1B2, diagnosed by whole‐exome sequencing. This finding expands the spectrum of ATP6V1B2‐associated disorders and adds ATP6V1B2 as a new member for the growing list of early‐onset epileptic encephalopathy genes. [Published with video sequence].
De novo truncating mutation in SCN1A as a cause of febrile seizures plus (FS+)
Authors: Jaimes Alex , Guerrero‐López Rosa , González‐Giráldez Beatriz , Serratosa Jose M.
Published: June 2020

SCN1A is one of the most relevant epilepsy genes. In general, de novo severe mutations, such as truncating mutations, lead to a classic form of Dravet syndrome (DS), while missense mutations are associated with both DS and milder phenotypes within the GEFS+ spectrum, however, these phenotype‐genotype correlations are not entirely consistent.

Case report. We report an 18‐year‐old woman with a history of recurrent febrile generalized tonic‐clonic seizures (GTCS) starting at age four months and afebrile asymmetric GTCS and episodes of arrest, suggestive of focal impaired awareness seizures, starting at nine months. Her psychomotor development was normal. Sequencing of SCN1A revealed a heterozygous de novo truncating mutation (c.5734C>T, p.Arg1912X) in exon 26.

Conclusion. Truncating mutations in SCN1A may be associated with milder phenotypes within the GEFS+ spectrum. Accordingly, SCN1A gene testing should be performed as part of the assessment for sporadic patients with mild phenotypes that fit within the GEFS+ spectrum, since the finding of a mutation has diagnostic, therapeutic and genetic counselling implications.
Tarihçe-i Ayasofya
Authors: ÇAM Mevlüt , HACIÖMEROĞLU Beyhan
Published: June 2020

Son yıllarda en fazla tartışılan konulardan birisi olan Ayasofya Camisi, belki de hakkında en fazla kitap ve yazı yazılan ibadethanelerdendir. Zira Türkiye’de muhtelif kütüphanelerde onlarca “Tarihçe-i Ayasofya” adıyla kitap ve risale bulunmaktadır. İstanbul’un fethinden önce kilise olarak hizmet vermiş olan yapı, fetihle birlikte camiye tahvil edilerek 479 yıl kesintisiz cami olarak hizmet vermiş bir vakıf eserdir. Bu çalışmada Mecmua-i Fünun’da Fardis ve Kostantinidi’ye ait iki makaleye yer vererek vakfiye üzerinden Ayasofya meselesi ile ilgili bir değerlendirme yapmaya çalıştık.
Hacı Bektaş Velî Dergâhı Ziyâretçi Defteri 1921-1925
Authors: KURT Yılmaz
Published: June 2020

Bu bölümde, Hasan Demirtaş’ın yazarı olduğu ve Vakıflar Genel Müdürlüğü tarafından 2020 yılı içerisinde basımı yapılan Hacı Bektaş Velî Dergâhı Ziyâretçi Defteri 1921-1925 adlı kitap Yılmaz Kurt’un kaleminden okuyucuya tanıtılmaktadır. In this section, a book named Hacı Bektaş Velî Dergâhı Ziyâretçi Defteri 1921-1925 written by Hasan Demirtaş and published by the General Directorate of Foundations in 2020 is introduced to the reader from Yılmaz Kurt’s pen.Hasan Demirtaş, Hacı Bektaş Velî Dergâhı Ziyaretçi Defteri (1921- 1925), Vakıflar Genel Müdürlüğü Yayınları, Ankara 2020,17,5x27,5 cm, XCI+ 219 sayfa, ISBN: 978- 975-17- 4431-9.
La educación alimentaria de los niños
Authors: Russo Sabina
Published: June 2020

A lo largo de los años, se ha producido un aumento progresivo del número de personas con sobrepeso y obesas de todas las edades y realidades sociales en Europa y en todo el mundo, lo que hace de la obesidad uno de los problemas de salud pública más acuciantes. La obesidad es un problema que no perdona ni siquiera a los niños más pequeños. Los últimos datos publicados muestran que los niños de los países del sur de Europa son los que tienen mayores problemas de peso (entre el 18% y el 21%); en Italia uno de cada cinco niños es obeso. Esos datos dieron lugar a una investigación en Ribera, un país de Sicilia (Italia), destinada a promover la mejora de la nutrición infantil mediantelasensibilización sobre los malos hábitosalimentariosrelacionadoscon el consumo de desayunos y refrigerios en la escuela. Concretamente, la investigación, partiendo de la idea de que los maestros y los padres deben ayudar conjuntamente a los niños a desarrollar el conocimiento y la conciencia de los hábitos alimentarios sanos y a promoverlos, quería demostrar que el uso por parte de los niños de un diario semanal de aperitivos en la escuela contribuye a la mejora de los hábitos alimentarios.
Actividad física y efectos psicológicos del confinamiento por covid-19
Authors: Andreu Cabrera Eliseo
Published: June 2020

En diciembre de 2019, la Comisión Municipal de Salud y Sanidad de Wuhan (provincia de Hubei, China), las autoridades chinas identificaron un nuevo tipo de virus denominado COVID 19. La OMS declaró el estado de pandemia mundial y muchos gobiernos decretaron un período de confinamiento prorrogable. La cuarentena que estamos viviendo, se caracteriza por dos aspectos fundamentales: el aislamiento de nuestros familiares y la restricción de movimiento, tan necesario para el ser humano. Este tiempo de reclusión obligatoria, se ha comprobado que produce daños psicológicos temporales y permanentes. Estrés, irritabilidad y tristeza aparecen ante una situación de soledad, incomunicación e incertidumbre. Este artículo recoge las conclusiones de otros estudios realizados sobre mutaciones del virus SARS y los efectos psicológicos. Algunas investigaciones sugieren que, las duraciones más largas de la cuarentena, se asocian específicamente con síntomas de estrés postraumático, conductas de evitación y enojo. Se analiza la actividad física en el hogar como fenómeno mediático, para luchar contra los desequilibrios emocionales del confinamiento y de mantenimiento de la salud. Ante esta situación de encierro, han proliferado diferentes programas de ejercicios de todo tipo y en diferentes plataformas y medios de comunicación. Algunas de estas plataformas han visto incrementado el número de usuarios y reproducciones, siendo estos valores, indicadores objetivos del interés por la práctica del ejercicio físico en momentos de reclusión obligatoria en el hogar.
Una experiencia de aprendizaje servicio en asociación proyecto autismo de Valencia
Authors: Salcedo Mateu Amparo
Published: June 2020

Se presenta una Experiencia de aprendizaje-Servicio realizada por alumnas de primer curso de Educación Social de la Universidad Católica de Valencia San Vicente Mártir, en una agrupación de familias (ASPAU) pertenecientes a la Comunidad Valenciana, bajo la necesidad de mejorar la calidad de vida de los niños y niñas con autismo, así como la de sus padres. Las alumnas, para poder realizar el servicio, pusieron en práctica las competencias académicas de la asignatura: Necesidades Específicas de Apoyo Educativo.El objetivo principal del proyecto era fomentar la participación, social y educativa de este colectivo, mediante la elaboración de talleres una vez reconocidas las necesidades que presentaban con más frecuencia.La idea global, era detectar las principales necesidades específicas y poner en marcha un plan de acción que pudiera facilitar la adquisición y mejora de estas, al proporcionar talleres prácticos y entrenamiento a las familias Las observaciones llevadas a cabo por este grupo de estudiantes dieron como resultado una serie de acciones encaminadas a mejorar las dificultades en la atención, capacidad de reacción, baja socialización, desarrollo cognitivo y motor, así como el aumento de la memoria y la capacidad cognitiva. Como conclusión, resaltar que las actuaciones realizadas, incrementaron la capacidad de las alumnas de poner en práctica las habilidades adquiridas a lo largo de su formación, aumentando sus destrezas para desarrollar estrategias sociales, así como programar las acciones necesarias para el buen funcionamiento del trabajo en equipo.
Mapas conceptuales como instrumento de coordinación docente en estudios de posgrado
Authors: García Tormo José Vicente
Published: June 2020

Con el actual Espacio Europeo de Educación Superior, el trabajo en equipo y la coordinación son estrategias esenciales para mejorar la calidad de la enseñanza, implicando tomar conciencia de la importancia de entender la docencia como una responsabilidad compartida en la que participarán tanto docentes como estudiantes. Para ello es importante establecer mecanismos y recursos para procurar una óptima coordinación (intra-materia e inter-materias). La Coordinación Académica, entre otras tareas, persigue que no existan solapamientos de contenidos entre las distintas asignaturas, garantizando que se cubra el abanico de competencias propias del título. En elcontexto del Máster Universitario en Entrenamiento y Rendimiento Deportivo dela Universidad de León, se ha visto la necesidad de optimizar la coordinación inter e intra-asignatura, para evitar solapamiento de contenidos detectados en el programa de Plan de Acción Tutorial en el que participan los estudiantes. Con este fin, se está llevando a cabo un proyecto de innovación docente para introducir un instrumento de coordinación basado en mapas conceptuales, que permitiese de manera visual, controlar y ubicar los contenidos de cada materia. Los responsables de cada materia han participado en una jornada de familiarización con el instrumento que se mostrará a los estudiantes en la primera sesión presencial y en cada uno de los bloques de contenidos, contextualizando tanto la materia como los contenidos. Este instrumento será valorado tanto por el profesorado como por los estudiantes mediante un cuestionario on-line(Googleforms),con el quese obtendrá un Feedback que permitiráajustaryvalorarsu utilidad paracursos posteriores y en otras titulaciones. Este trabajo se ha desarrollado dentro del Plan de apoyo a la innovación docente de la Universidad de León (PAID 2019), bajo el título “Aplicación de mapas conceptuales como instrumento de coordinación docente en estudios de posgrado”
Estudiantes con discapacidad y la orientación vocacional en secundaria: una revisión sistemática con vosviewer
Authors: González Cedeño Greisy , Hernández Sánchez Brizeida
Published: June 2020

El proceso de orientación debe facilitar que la persona desarrolle estrategia para comprender la ambigüedad y la aceleración de los cambios del entorno laboral. El propósito de este artículo es analizar la información existente y determinar a partir de las revisiones sistemáticas de la literatura que se han realizado sobre los programas de orientación vocacional y la relación con estudiantes con discapacidad, a que conclusiones se ha llegado, las lagunas que persisten y cuáles son los siguientes pasos para seguir a fin de encontrar mejoras en dichos programas, así como identificar las barreras a las que estudiantes e instituciones pueden enfrentarse. Por tanto, los principales objetivos están enmarcados en identificar las diferentes perspectivas teóricas que han sido estudiadas sobre los programas de orientación vocacional educativa y destacar las conexiones entre ellos; buscar tendencias emergentes y determinar las lagunas al comparar las diferentes investigaciones, y finalmente, determinar de las conclusiones a las que se ha llegado, si posteriormente ha habido algún cambio importante que permitiera el avance científico en esta área. Además, se van a analizar algunos datos bibliométricos, para situar la investigación dentro de la educación inclusiva secundaria. Se recopilaron los datos bibliográficos y se analizamos a través de técnicas de co-cita con los siguientes objetivos específicos: - Identificar las diferentes perspectivas teóricas que han sido estudiadas sobre la los programas de orientación vocacional y destacar las conexiones entre ellos; - Buscar tendencias emergentes y determinar las lagunas al comparar los diferentes investigaciones, y - Determinar posibles factores críticos que permitan el avance científico en el área. En este sentido, este artículo integra las revisiones sistemáticas que se han realizado alrededor del tema de la orientación vocacional.
Realización de criterios de valoración sobre el trabajo por proyectos a partir de la observación directa de la metodología en un caso real de un aula de educación infantil
Authors: Gago Morate Ángela , Pacheco Sanz Deilis Ivonne , Canedo García Alejandro , Martín Sánchez Beatriz , Bleye Varona Yvet
Published: June 2020

Los estudios de formación para la docencia, en Educación Primaria y Educación Infantil, han permitido conocer diferentes metodologías y modelos educativos,así como el aprendizaje de destrezas, actitudes y competencias profesionales que se exigen para ejercer la docencia. En el caso de la metodología del Trabajo por Proyectos, a pesar de tener su origen hace varios siglos, es actualmente cuando está en auge en España, implementándose sobre todo en las aulas de Educación Infantil, por lo que se ha decidido ampliar la información sobre esta metodología. Por ello, ha sido necesario recurrir a la revisión de diversos estudios y ejemplos de aplicaciones prácticas, ejecutadas en algunos centros educativos. El criterio de selección del colegio para este estudio, estuvo enmarcado dentro del contexto de la elección de centros para el desarrollo del Practicum III en la Universidad de Valladolid, tras la investigación previa de aquellos centros donde trabajaban y llevaban a cabo esta metodología.Es así cómo la escogencia del colegio, permitió tanto la observación directa sobre la práctica docente en un aula de Educación Infantil con 22 estudiantes de 4 años, así como realizar una evaluación de la aplicación práctica de la Metodología del Trabajo por Proyectos, bajo el foco del siguiente planteamiento: ¿se están cumpliendo los criterios que sigue esta metodología? Durante la práctica observada, se fueron obteniendo datos y se construyeron 8 criterios que permiten valorar la práctica observada. Asimismo, se ha realizado una propuesta de mejora sustentada en lo que la autora de este trabajo ha contrastado con los principios metodológicos para la aplicación del Trabajo por Proyectos, siguiendo un orden lógico de acuerdo a los criterios de evaluación propuestos y respondiendo a: ¿Qué dice la teoría?, ¿Qué se observó en la práctica real del aula? y ‘la propuesta de mejora: ¿Qué haría yo?
Aps: un itinerario para las competencias en la asignatura de deporte adaptado
Authors: Puchalt Hernández José Manuel
Published: June 2020

En el tercer curso del Grado de Ciencias de la Actividad Física y Deporte de la Universidad Católica de Valencia se imparte la asignatura “Deporte Adaptado y Actividad Física Inclusiva”, cuyo objeto de estudio son las personas con discapacidad y su relación con los diferentes ámbitos del deporte, terapéutico, educativo, competitivo y de ocio y recreación. Para conseguir las competencias específicas de la asignatura los alumnos pueden elegir tres itinerarios distintos. Por un lado, se ofrece trabajo en grupo con exposición teórica y aplicación práctica; por otro, portafolioscomo trabajo individual y en tercer lugar itinerario de Aprendizaje y Servicio, en el que los alumnos por grupos buscan una institución y desarrollan en ella un programa de Aprendizaje-Servicio mediante el cual adquieren las competencias específicas de la asignatura. Este último itinerario es el que mejor cumple los objetivos de la asignatura, puesto que los alumnos se familiarizan con las personas usuarias de las instituciones donde lo aplican y se sensibilizan hacia la discapacidad, obteniendo un aprendizaje auténtico y situado, puesto que vivencian la discapacidad en primera persona. También es una metodología que denominamos “trascendente” puesto que trasciende la Universidad y repercute directamente en el entorno social más cercano, reduciendo esa brecha que a veces existe entre la Universidad y la sociedad real.Una vez elegida la institución y solucionados los trámites administrativos, el itinerario APS consta de cuatro fases querealizan losalumnosen grupo: preparación, intervención,evaluación y difusión. Del desarrollo detodas ellas da cuenta la presente comunicación.
Creatividad como herramienta socioeducativa. Actividad piloto "medioambiente, sociedad y barro"
Authors: Del Palacio Pérez María , Isidro de Pedro Ana Isabel
Published: June 2020

El presente trabajo versa sobre la ejecución de la Actividad piloto “Medioambiente, sociedad y barro”, una propuesta de intervención socioeducativa a través del arte. La actividad piloto, de temática medioambiental y transformadora,serealizaatravés del barro,con un grupo de menores pertenecientesa un centro desocialización y adultos aficionados al barro, y en relación a los datos de la fundamentación teórica, que abarcan diversos conceptos como autoestima, tolerancia, transformación individual y social y actitud crítica como aspectos posibles a desarrollar a través de la creatividad. En su desarrollo, se presentan dos tipos de baldosas de arcilla: unas intactas, en las que se representa un tema a partir de láminas ilustrativas sobre la problemática socioambiental; y otras dañadas previamente, que habrán de reconstruir. De este modo, se permite la reflexión sobre los problemas ambientales y la relación entre los problemas naturales y sociales, así como entre el conflicto de inserción y el conflicto medioambiental, pudiendo abarcar temas como la sociedad de consumo o el sistema educativo tradicional y los “deshechos” que generan. El resultado es un mural que muestra la naturaleza dañada en la mitad de sus piezas y la transformación de lo dañado en la otra mitad.
Relación entre metodologías docentes y enfoques de aprendizaje en la universidad
Authors: García Jiménez Marta , Fernández Cabezas María
Published: June 2020

El Espacio Europeo de Educación Superior (EEES) requiere un giro en la percepción del proceso de enseñanza-aprendizaje. El objetivo principal de este estudio es conocer las correlaciones existentes entre las metodologías docentes llevadas a cabo y su relación con los enfoques y estrategias de aprendizaje utilizadas por el alumnado de primer y de tercer curso del grado en Pedagogía de la Facultad de Ciencias de la Educación de la Universidad de Granada. Los instrumentos de recogida de información han sido el Revised two- Factor StudyProcess Questionnaire (R-SPQ- 2F), el Inventario de hábitos de estudio (I.H.E) y el Cuestionario de Evaluación de las Variables “Moduladoras” del Estilo de Enseñanza en Educación Superior (CEMEDEPU). Para el análisis de datos se han realizado estadísticos descriptivos, una comparación de medias mediante el análisis de T de Student y se han analizado las correlaciones entre variables de los cuestionarios I.H.E. y R-SPQ-2F. Los resultados señalan que la metodología docente centrada en el aprendizaje influye directamente en el enfoque profundo, la motivación profunda y la estrategia profunda. Se obtiene una relación directa y significativa del enfoque profundo con las escalas de condiciones ambientales del estudio, planificación del estudio y asimilación de contenidos.
¿Puede el ser humano alcanzar la felicidad?
Authors: Rodríguez Huerta Sonia María
Published: June 2020

El ser humano mueve las piezas de su propia vida para llegar a conseguir su felicidad. En el trabajo se cuestiona radicalmente la propia posibilidad de consecución de la felicidad, partiendo de una especie de agnosia sobre su concepción como estadio final. Las ideas homologadas de la felicidad siguen vivas en nuestros días, cadavez másyen más disciplinas,sobretodo en lassociedades occidentales(Bueno, 2011). Sellamalaatención sobre el aspecto bifronte de la felicidad, físico, por un lado, y espiritual por otro. Se concluye que es muy difícil que podamos alcanzar el grado máximo y permanente de felicidad o la felicidad completa. Puede que asumamos el peso ylaresponsabilidad deconseguir la máximafelicidad pero,como Sísifo,cargando unay otravez la gigantesca piedra hacia la cumbre de la montaña, nunca podamos mantenernos allí, en ese territorio ideal o emocional del que poco conocemos y que se configura como una asíntota. Con respecto a la posibilidad de ser feliz en mayor o menor medida, es sugerente la propuesta de la pirámide gradual de Abraham Maslow. La conclusión es optimista, al entender que la felicidad relativa engloba un rango muy variable y no es un punto final. Se propone la modificación del significado del término “felicidad” en el Diccionario de la Real Academia Española de la Lengua, para identificarlo con “una variedad de estados de grata satisfacción espiritual y física”