bluuebunny/crossref_metadata_2025 huggingface dataset:
Information Theoretic Generative Modeling
Authors: Lastras Luis
Published: July 2022

In this article we use rate-distortion theory, a branch of information theory devoted to the problem of lossy compression introduced by Claude Shannon in 1959 [1], to shed light on an important problem in latent variable modeling of data: is there room to improve the model? One way to address this question is to find an upper bound on the probability (equivalently a lower bound on the negative log likelihood) that the model can assign to some data as one varies the prior and/or the likelihood function in a latent variable model. The core of our contribution is to formally show that the problem of optimizing priors in latent variable models is exactly an instance of the variational optimization problem that information theorists solve when computing rate-distortion functions, and then to use this to derive a lower bound on negative log likelihood. Moreover, we will show that if changing the prior can improve the log likelihood, then there is a way to change the likelihood function instead and attain the same log likelihood, and thus rate-distortion theory is of relevance to both optimizing priors as well as optimizing likelihood functions. The result we present here runs much deeper than the particular modeling problem being solved - in formally connecting the latent variable modeling problem to rate-distortion theory, we have established a bridge where decades of work on either field can now be considered for possible cross-pollination; notably in a subsequent article we intend to ask the question next of whether practical algorithms in data compression can be used to design latent variables. We will experimentally argue for the usefulness of quantities derived from rate-distortion theory in latent variable modeling by applying them to a problem in image modeling.
Predicting Future User Activities with Constrained Non-Linear Models
Authors: Phielipp Mariano , Kveton Branislav
Published: July 2022

Prediction of future user activities from their history, all past activities, is a challenging problem. One reason is that the number of all potential histories grows exponentially with the length of the 4 history. Recently, deep-learning models have been proposed for solving this problem. It is easy to learn a simple predictor of future user activities, by averaging all past activities of the user and then learning an activity classifier from this average representation, for instance by logistic regression. This approach tends to have a high bias, due to using a simple feature representation and model. It is also easy to apply sequence models in deep learning to learn a predictor of future user activities. This approach tends to have a high variance, because of the large number of parameters in the neural network.
Exploring Transfer Learning for Self-driving Car Dataset
Authors: Santana Paula
Published: July 2022

A core subject in the self-driving cars domain is how to use images taken in real time to best steer a vehicle in a road. Mapping visual inputs to steering commands can be seen from a machine learning perspective as a regression or classification problem, depending mainly if the control outputs will be discrete e.g. ’turn left’, ’go forward’ or continuous e.g. steering angle: +0.014◦. In this experiment, the road following task will be treated as a classification problem, so given an input image the learning model will predict a control command (‘up’, ‘left’, ‘right’).
Hover: A Wearable Object Identification System for Audio Augmented Reality Interactions
Authors: Colon Hernandez Pedro , Bove Michael
Published: July 2022

We design, implement, and test a wearable object identification system which allows users to "hover" their hands over objects of interest and get access to contextual information that may be tied to them, through an intelligent personal assistant. The system uses a fusion of sensors to be able to perform the identification of an object under a variety of conditions. Among these sensors there is a camera (operating in the visible and infrared spectrum), a small solid-state radar, and multi-spectral light spectroscopy sensors. Users can interact with contextual information tied to an object through conversations with an intelligent assistant to permit a hands-free, non-obtrusive, and personalized experience. The system explores audio interfacing with augmented reality content without the hassle of phones or head mounted devices.
Monte Carlo Methods on High Dimensional Data
Authors: Guzman Rel
Published: July 2022

Markov Chain Monte Carlo (MCMC) simulation is a family of stochastic algorithms that are commonly used to approximate probability distributions by generating samples. The aim of this proposal is to deal with the problem of doing that job on a large scale because due to the increasing power computational demands of data being tall or wide, a study that combines statistical and engineering expertise can be made in order to achieve hardware-accelerated MCMC inference. In this work, I attempt to advance the theory and practice of approximate MCMC methods by developing a toolbox of distributed MCMC algorithms, and then a new method for dealing with large scale problems will be proposed, or else a framework for choosing the most appropriate method will be established. Papers like [1] provide a comprehensive review of the existing literature regarding methods to tackle big data problems. My idea is to tackle divide and conquer approaches since they can work distributed in several machines or else Graphics Processing Unit (GPUs), so I cover the theory behind these methods; then, exhaustive experimental tests will help me compare and categorize them according to their limitations in wide and tall data by considering the dataset size n, sample dimension d, and number of samples T to produce.
ChaLearn AutoML Challenges
Authors: Escalante Hugo , Madrid Jorge , Morales Eduardo
Published: July 2022

Academic challenges comprise effective means to rapidly advance the state of the art and solve problems of practical relevance. We elaborate on the benefits of challenges for the LatinX community and summarize ChaLearn challenges on Autonomous Machine Learning.
Using Deep Convolutional Networks for the Automatic Recognition of MacroInvertebrate in Rivers and Affluents in Panama
Authors: Sanchez Galan Javier , Quintero Carlos , Merchan Fernando , Cornejo Aydee
Published: July 2022

The determination of water quality in rivers is a very important task for ensuring the well-being of any population. Moreover, It is very laborious task, involving a combination of chemical and microbiological analysis techniques. A low cost alternative to achieve this same goal is the use of biomonitoring or recognition of macroinvertebrate organisms which presence or absence can be used to determine the water quality. Biomonitoring is often used in rural communities that use of natural streams for consumption and daily life. In those cases community members are often the ones obtaining the quality reports. Although it is an efficient method it has the disadvantage that it requires biological training both for the capture, and identification of macroinvertebrate present in water and watersheds. This work focuses on providing a tool to automate the recognition of freshwater macroinvertebrate images based on deep convolutional networks using the Python programming language, and the Tensorflow and Keras libraries.
Overview of UP-Fall Detection Project
Authors: Martinez Villasenor Lourdes
Published: July 2022

In this paper, we present an overview of our project of UP-Fall Detection. The aim of this project is to configure a simple, comfortable and fast fall detection and human activity recognition system. This system must be accurate in fall detection and easy to implement and adopt. We briefly describe the achievements and advances obtained in two years.
Deep Predictive Coding for Multimodal Spatiotemporal Representation Learning
Authors: Fonseca Marcio
Published: July 2022

Common sense reasoning relates to the capacity of learning representations that disentangle hidden factors behind spatiotemporal sensory data. In this work, we hypothesize that the predictive coding theory of perception and learning from neuroscience literature may be a promising candidate for implementing such common-sense inductive biases. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) and extend its application to the challenging task of inferring abstract, everyday human actions such as cooking and diving. Our transfer learning experiments demonstrate good generalization of learned representations on the UCF-101 action classification dataset for both visual and auditory modalities.
Signed Causal Bayesian Networks for Microbiomes
Authors: Sazal Musfiqur
Published: July 2022

Inferring causality is the process of connecting causes with effects. Identifying even a single causal relationship from data is more valuable than observing dozens of correlations in a data set. Microbe-microbe and host-microbe interactions play a vital role in both health and disease. In this study, we investigate how to learn a causal structure from data from microbiome studies and its potential interpretation about events and processes in the microbial community under study. We report evidence that causal structure can extract colonization patterns even though the analysis only uses data with no temporal information
Backpropagating the Unsupervised Error of Self-Organizing Maps to Deep Neural Networks
Authors: Braga Pedro
Published: July 2022

Previous research has shown the potential that Deep Neural Networks have in building representations that are useful not only for the task that the network was trained for but also for correlated tasks that take data from similar input distributions. For instance, recent works showed that representations built by a Convolutional Neural Network (CNN) are better than the state-of-art handcrafted features used for object classification and demonstrated that the representations learned by Google LeNet could be used for the task of Unsupervised Visual Object Recognition (UVOC), achieving about 75-90% of agreement with labels assigned by humans in an unseen dataset, when fed as input to a SOM-based clustering method. In this work, we propose an approach that combines SOM with Deep Learning in a synergic way to allow dealing with complex data structures, such as images and sound, by backpropagating the unsupervised error through layers of neurons.
Speeding up Reinforcement Learning for Inference and Control of Gene Regulatory Networks
Authors: Bonini Rodrigo
Published: July 2022

Motivated by the desire to understand genomic functions through interactions between genes and gene products, the research in the area of gene regulatory networks has become a very important object of study in recent years. Probabilistic Boolean Networks (PBN), which are rules-based dynamic systems, are some of the most studied mathematical models to represent networks and their regulations. However, frequently their representation, regulation, and interactions between genes are overly complex to learn and control, requiring a complex model. Reinforcement Learning (RL) is an interesting technique to deal with this problem because it can learn solutions without the need of a model. This approach is used to train autonomous agents who can find solutions to complex problems, including those of regulation and relationships between genes. But its classical approaches are slow when having to learn tasks with many states especially when these tasks have multiple goals and agents. Besides that, learning bad solutions can make the learning process even more difficult and slow. Therefore, some RL approaches and techniques need to be tested in order to verify the best way to flexibilize, adapt and improve them to intervene and control the gene networks.
Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders
Authors: Larrazabal Agostina , Martinez Cesar , Ferrante Enzo
Published: July 2022

Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional Random Fields) to incorporate connectivity constraints into the resulting masks. These post-processing steps are based on the assumption that objects are usually continuous and therefore nearby pixels should be assigned the same object label. Even if it is a valid assumption in general, these methods do not offer a straightforward way to incorporate more complex priors like convexity or arbitrary shape restrictions. In this work we propose Post-DAE, a post-processing method based on denoising autoencoders (DAE) trained using only segmentation masks. We learn a low-dimensional space of anatomically plausible segmentations, and use it as a post-processing step to impose shape constraints on the resulting masks obtained with arbitrary segmentation methods. Our approach is independent of image modality and intensity information since it employs only segmentation masks for training. This enables the use of anatomical segmentations that do not need to be paired with intensity images, making the approach very flexible. Our experimental results on anatomical segmentation of X-ray images show that Post-DAE can improve the quality of noisy and incorrect segmentation masks obtained with a variety of standard methods, by bringing them back to a feasible space, with almost no extra computational time.
Advanced Transfer Learning Approach for Improving Sentiment Analysis on Different Dialects of Spanish
Authors: Palomino Daniel
Published: July 2022

In the last years, innovative techniques like Transfer Learning have impacted strongly in Natural Language Processing, increasing massively the state-of-the art in several challenging tasks. In particular, the Universal Language Model Fine-Tuning (ULMFiT) and the Bidirectional Encoder Representations from Transformers (BERT) algorithms have proven to have an impressive performance on several English text classification tasks. In this paper, we aim at developing an algorithm for Spanish Sentiment Analysis of short texts that is comparable to the state-of-the-art. In order to do so, we have adapted the ULMFiT and BERT algorithms to this setting. Experimental results on benchmark datasets (InterTASS 2017 and InterTASS 2018) show how this simple transfer learning approach performs well when compared to fancy deep learning techniques.
Semantic Segmentation on Image Using Multi-task Hourglass Networks
Authors: Saire Darwin
Published: July 2022

Semantic segmentation task aims to create a dense classification by labeling pixelwise each object present in images. Convolutional neural network (CNN) approaches have been proved useful by exhibiting the best results in this task. However, some challenges remain, such as the low-resolution of feature maps and the loss of spatial precision, both produced in the last convolution layer of the CNNs. In this work, we propose an hourglass model based on the multi-task approach. Consequently, we combine the tasks of edge detection, semantic segmentation, and distance transform. The refinement of the tasks (getting specific information of each task) is obtained in the last layers of the decodification stage. All the tasks share the rest of the information, that is, shared weights. Thus our model is efficient with respect to the number of tasks and memory used. We obtained encouraging preliminary results still in images using Cityspace and Kitti datasets.
A study of observation scales based on the FH dissimilarity measure
Authors: Yuri Edward
Published: July 2022

Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method.
Which Kernels to Transfer in Deep Q-Networks?
Authors: Garcia Ramirez Jesus
Published: July 2022

Deep Reinforcement Learning (DRL) combines the benefits of Deep Learning and Reinforcement Learning. However, it still requires long training times and a large number of instances to reach an acceptable performances. Transfer Learning (TL) offers an alternative to reduce the training time of DRL agents, using less instances and possibly improving performance. In this work, we propose a transfer learning formulation for DRL across tasks. Relevant source tasks are selected considering the action spaces and the Wasserstein distances of an output in a hidden layer of a convolutional neural network. Rather than transferring the whole source model, we propose a method for selecting only relevant kernels based on their entropy values, which results in smaller models that can produce better performances. In our experiments we use Deep QNetworks (DQN) with Atari games We evaluated the proposed method with dierent percentages of selected kernels and show that we can obtain similar performances than DQN in less nteractions and with smaller models.
Automatically Personalized Pain Intensity Estimation from Facial Expressions using CNN-RNN and HCRF in videos
Authors: Quispe Jefferson
Published: July 2022

Deep Learning methods have achieved impressive results in several complex tasks such as pain estimation from facial expressions in video sequences. Estimation pain has a difficult way to measure, it due to subjective and specifics features by each person. However, its estimation is important for clinical evaluation processes. This research paper proposes the use of Convolutional Neural Networks (CNN) with Transfer Learning and Sequence Model using GRU in order to get an accurate pain estimate. Prior to this, a preprocessing is performed using the landmarks. For a correct estimation of the automatic intensity of pain, Prkachin and Salomon Pain Intensity (PSPI) is used. However, this metric is not a personalized representation of the patient; therefore, the key contribution is Hidden Conditional Random Field (HCRFs), using PSPI, Visual Analog Score (VAS), and other scales estimate; which allows us to achieve results taking into account the evaluation metric used by specialists.
Liderlik, Liderlik Tarzları ve Türk Gümrük İdareleri Özelinde Kamu Yönetiminde Liderlik
Authors: KELEŞ Nuh
Published: July 2022

Liderlik geçmişten bu yana varlığını sürdüren bir kavram olarak birçok araştırmaya konu olmuştur. Tarih büyük liderlerin izleri/yaptıklarıyla kaydedilmiş ve önemli liderlerin bilgileri günümüze gelmiştir. Liderlikle ilgili bilimsel araştırmaların yapılması yönetim bilimi alanında yapılan araştırmalara paralel olarak 20. yüzyılın başlarına dayanmaktadır. Tarihsel süreçte liderlik teorileri “Büyük Adam Teorisi, Özellikler Teorisi, Davranışsal Liderlik Teorileri, Durumsal Liderlik Teorileri ve Modern Liderlik Teorileri” başlıkları altında incelenmiştir. Yakın geçmişte ve günümüzde liderlikle ilgili birçok yeni liderlik tarzı ortaya çıkmıştır. Bu çalışmanın amacı geçtiğimiz yüzyılı kapsayan tarihsel süreçte ortaya çıkan ve günümüze kadar gelen liderlik tarzlarını genel hatlarıyla açıklayarak bu liderlik tarzlarını gümrük idareleri özelinde kamu yönetimiyle ilişkilendirmektir. Nitel araştırma yöntemlerine uygun bir şekilde bu çalışma alanyazın taramasına göre yürütülmüş ve liderlikle ilgili teorik bilgiler sunulmuştur. Sonuç olarak kamu yönetiminde bulunan liderler/yöneticiler tarafından 32 farklı liderlik tarzı içerisinden demokratik, destekleyici, dönüşümcü, etkileşimci, görev odaklı, karizmatik, otokratik, takım/ekip yönetimi ve yönetsel liderlik tarzlarının daha uygun/etkili biçimde uygulanabileceği ifade edilmiştir.
Investigating Pre-School Teachers' Perceptions About the Leadership Styles of School Principals
Authors: ZAMBAK Ayse , ÖZDEMİR Emine
Published: July 2022

This study aims to investigate how preschool teachers perceive the leadership styles of the school principals they work with, as they have a significant impact on student performance and school atmosphere. Therefore, a mixed-method research paradigm was utilized with a purposive random sampling method of 114 pre-school teachers at public schools. School Principals' Leadership Style Scale together with the demographic information part and a metaphor elicitation form, were applied to collect the necessary data. Because a convergent parallel design was utilized to weigh the processes equally, quantitative and qualitative data were evaluated independently, and the results were interpreted separately. The results revealed that, according to pre-school teachers' perceptions, the principals they work with show their laissez-faire leadership style more. Unlike quantitative findings, data from the metaphor elicitation form indicate that the category with the most metaphors for a school principal is a figure of authority. Although the descriptive findings of teachers' impressions show that most school principals have a laissez-faire leadership style, metaphors portray school principals as persons who have power at school, supervise their team, have varied tasks, and are motivators and innovators. To sum up, it can be put forward that effective leadership is a crucial necessity of successful organizations, and principals need to take an active part in the preparation process to go a step further in their careers and lead their organizations more effectively. The results revealed that, according to pre-school teachers' perceptions, the principals they work with show their laissez-faire leadership style more. Unlike quantitative findings, data from the metaphor elicitation form indicate that the category with the most metaphors for a school principal is a figure of authority. Although the descriptive findings of teachers' impressions show that most school principals have a laissez-faire leadership style, metaphors portray school principals as persons who have power at school, supervise their team, have varied tasks, and are motivators and innovators. To sum up, it can be put forward that effective leadership is a crucial necessity of successful organizations, and principals need to take an active part in the preparation process to go a step further in their careers and lead their organizations more effectively.
Türkiye’s Foreign Aids Leadership in Africa
Authors: AKILLI Erman
Published: July 2022

As humanitarian and foreign aid has become a norm in international relations, those foreign policy tools have been widely used in world politics to enhance states’ foreign policy goals and help countries in need. Nonetheless, difficulties in delivering humanitarian and foreign aid to Africa have been the subject of much discussion and criticism. Many of these reasons missed the opportunity to consider how diplomacy may help facilitate the process by which aid and humanitarian assistance are given in Africa, even though many of these arguments are legitimate in their objective critique of the efficacy of foreign aid in Africa. On the other hand, Turkiye’s approach toward Africa has never been through the same lens as the former imperial states assisting in the region. Turkiye’s approach to the region is embraced by the “win-win” motto, which maintains its relationships with African nations based on “friendship” and “fraternity.” Thus, Turkiye’s regional leadership based on humanitarian and foreign aids basis is remarkable. This study aimed to examine Turkiye’s dost eli approach in Africa with leadership, especially in the days of Covid-19.
Clinical Severeity of Covid-19 Affected by Thyroid Disease
Authors: Bukhari Syed Ali Haziq , Nabi Awais Ghulam , Farooqi Muhammad Irfan
Published: July 2022

Objective: The current study's goal is to determine if thyroid disease can predict the severity of COVID-19. Study Design: Prospective Study Place and Duration: THQ kotmomin district Sargodha, Punjab Medical College. March 2021-SEP 2021 Methods: This research included 122 patients of both sexes. Patients ranged in age from 20 to 75. In order to obtain demographic data, all patients completed a permission form in which they said that they were willing to have this information used for research purposes. A PCR test was performed on each patient to establish the presence of a pandemic illness. Two equal groups, I and II, were created for the patients. Patients with symptoms in group I comprised of 61 patients, while group II had 61 patients without symptoms. Thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPA), thyroid free triiodothyronine (FT3), and thyroglobulin antibody were all tested for in the participants' blood (TGA). SPSS 24.0 edition was used for the whole data set to be analysed. Results: There was no any significant difference of age and BMI among both groups. Majority of the patients among both groups 40 (65.6%) and 39 (63.9%) were males. 35 (55.7%) of the patients in group I had severe disease, whereas 20 (32.8%) of those in group II had severe infection. Group I patients exhibited significantly lower levels of free thyroxine (FT4) and free triiodothyronine in their blood than the rest of the patients (FT3). The levels of IL-6 in group I were significantly greater than in group II. Among the 10 (16.4%) people in group I, had an overt thyroid problem, compared to 5 (8.2 % in group II . 17 (27.9%) instances of thyroid nodules larger than 1cm were detected in group I, whereas 6 (9.8%) cases of nodules larger than 1cm were found in group II. Group I had a considerably higher mortality rate, as shown by a p value of 0.05. Conclusion: Thyroid hormone abnormalities were more common in very sick COVID19 patients studied in this investigation. COVID19 patients' FT3 levels at the time of hospital admission may serve as a prognostic indication. Nodules on the thyroid may be a symptom of a severe form of COVID-19.
Vertical Housing Price Gradient and Ground Floor Premium in Taipei City
Authors:   National Taipei University, Chen Yu-Hui , Peng Chien-Wen ,   National Taipei University, Yang Chung-Hsien ,   National Pingtung University
Published: July 2022

This study aims to examine the influence of the floor level on housing prices based on the vertical dimensions of the building. A two stage least squares regression (2SLS) and two stage quantile regression (2SQR) are utilized to analyze the urban regions in Taipei City by using data obtained from the online actual price registry of real estate transactions from August 2012 to April 2020. After considering the spatial autocorrelation, the following conclusions are reached. First, in addition to the ground floor, the floor level premium is 0.55% for residential buildings with an elevator, and the premium increases when the housing price quantile increases. However, for walk-up apartments, there is a floor level discount of -4.21%, and the price discount increases when the quantile for the housing price increases. Secondly, when compared to the other floors, the ground floor premium is 9.61% for residential buildings with an elevator, which is a little higher than walk-up apartments for which the ground floor premium is 8.54%. The results reveal that the influence of the type of residential building and the total number of floors on the ground floor premium is not as high as expected. Finally, regardless of whether the residential buildings have an elevator or are walk-up apartments, the ground floor premium increases when the housing price quantile increases. The ground floor premiums for residential buildings with an elevator range from 7.30% to 11.85%. They are a little higher than those for walk-up apartments which range from 6.25% to 9.69%.
Office Property Pricing and Macroeconomic Shocks: European Regions through the Real Estate Cycle
Authors:   EBS Universität für Wirtschaft und Recht, Lang Elmar , Mager Ferdinand ,   EBS Universität für Wirtschaft und Recht, Hennig Kerstin ,   EBS Universität für Wirtschaft und Recht
Published: July 2022

We study the relation between European office prices and their fundamentals by using a panel dataset of 29 major European cities over 25 years. We control for the dotcom crisis, global financial crisis (GFC), and sovereign debt crisis to analyze the varying impact of our variables. We find real estate variables such as rent and vacancies to be the most important factors in explaining for office values. For the European Monetary Union, we find monetary policy measures played a considerable role only during the GFC. For Europe as a whole, monetary policy variables are less important. We document a strong negative linkage of the risk premium of investors to office prices during crisis times, but not in normal times. The dotcom crisis left European office prices almost unaffected. The German and Swiss office markets exhibit safe haven characteristics.
Analysing the Impact of Participation in Community Activities (Festivals) on Mental Health
Authors:   Wakayama University, Ueno Misaki
Published: July 2022

Social capital is vital for community activities, such as those pertaining to area management activities. Furthermore, festivals and the existence of temples and shrines are crucial for enhancing social capital in Japan. Among all of the area management activities in Japan, festivals are typically held at shrines, which allow attendees to pray for their health and well-being. In this paper, we investigate the impact of participating in festivals and traditional events on mental health. We conduct a nationwide questionnaire survey in Japan and use ordered logit techniques to analyse the impact of participation in festivals and traditional events on mental health. It is found that community activities related to festivals and traditional events normalise mental health. The dependent variable (i.e., the Kessler Psychological Distress Scale (K6)), is negative and statistically significant, which denote that participation in community activities can stabilise mental health. Since landscape maintenance activities, such as cleaning, are not statistically significant, it would be effective to merge such activities with festivals and other community-based efforts. It is also important to consider how residents could be motivated to participate in community activities when ties with neighbours become weaker.
Memory limitations are hidden in grammar
Authors: Gómez-Rodríguez Carlos , Christiansen Morten H. , Ferrer-i-Cancho Ramon
Published: July 2022

The ability to produce and understand an unlimited number of different sentences is a hallmark of human language. Linguists have sought to define the essence of this generative capacity using formal grammars that describe the syntactic dependencies between constituents, independent of the computational limitations of the human brain. Here, we evaluate this independence assumption by sampling sentences uniformly from the space of possible syntactic structures. We find that the average dependency distance between syntactically related words, a proxy for memory limitations, is less than expected by chance in a collection of state-of-the-art classes of dependency grammars. Our findings indicate that memory limitations have permeated grammatical descriptions, suggesting that it may be impossible to build a parsimonious theory of human linguistic productivity independent of non-linguistic cognitive constraints.
Archiving the Anthropocene: New Taxonomies Between Art and Science
Authors: Baldacci Cristina , Guaraldo Emiliano
Published: July 2022

Call for papers. Since its initial formulation and popularization in the public sphere, the Anthropocene thesis has triggered several critical debates across scientific disciplines and artistic practices, which have included non-anthropocentric and decolonial perspectives, critiques of species thinking and extractive economies, and insights from political ecology and the natural sciences. While scientific institutions have increasingly initiated collaborations with artists and designers, visual artists from different geographies and traditions have creatively engaged with the geological and planetary imaginaries mobilized by the ‘epoch of man’. They have done so by maintaining a critical relationship with the forms of knowledge reproduced by geoscientific research and communication.
Pelonium aulaeum n. sp., a new checkered beetle from French Guiana (Coleoptera: Cleridae)
Authors: Zappi Iuri
Published: July 2022

Pelonium aulaeum n. sp., a new species of Pelonium Spinola, 1844, from French Guiana, is described and figured. The new species is distinguishable from its congeners on the basis of colour, setal pattern and elytral sculpturing.
The Slang Word Used by Students of English Language Education, Ganesha University Education on Instagram Story
Authors: Sari Ni Putu Eva Agustini
Published: July 2022

Previous research stated that many millennials are comfortable in applying the use of slang in their daily communication. However, there is difficulty among the elderly about the use of slang that makes them face obstacles in knowing the meaning behind the slang used. Therefore, this research has an objective to investigate what kind of slang words used by them on their Instagram account. This research is kind of descriptive qualitative research using observation method, that wants to examine the slang words used by 15 students of English Language Education, Ganesha University of Education on Instagram especially from Instagram story feature. The findings indicate that it exists four types of slang used by Students of English Language Education on Instagram story, namely fresh and creative with total number of slangs is 12; imitative is six; for acronym, the total number of slangs used is three; and clipping with total number of slangs is 10. It indicates that mostly the slang used is type of fresh and creative and the most rarely used in flippant.
Code-Switching by Teenagers from Jakarta
Authors: Guntala Roselin Putri
Published: July 2022

There are numerous languages available around the world. The Indonesian language and English are just two of the many languages spoken around the globe. Aside from that, there are numerous regional languages spoken on Indonesia's various islands. As a result, Indonesians tend to move from Indonesian to their regional language (mother tongue). Furthermore, Jakartan youths frequently transfer from Indonesian to English and vice versa. The goal of this paper is to look into the code-switching that occurs among Jakartan youths. Many people transition their language from their mother tongue to their second language unknowingly. Similarly, some Jakartan teens discovered that their first language (Indonesian) was being replaced by their second language (English). This study was part of a qualitative research project that used a case study approach. To collect the necessary data, this study used observations and interviews as tools. When having a conversation with someone, the participants engage code-switching, according to the findings of this study. This is because speaking makes them feel more at ease and confident. When a person is fluent in more than one language, this is referred to as bilingualism or multilingualism.
Correlation between Students’ Mastery of English Tenses and Students’ Achievement in Listening Autobiography Podcast
Authors: Mardiyah Ragdah Ainun , Alhumaira Zumirrah , Salsabila Aurelia Hanin
Published: July 2022

This study intends to find the correlation between the students’ mastery of English tenses and students’ achievement in listening podcasts and specifically on autobiography podcasts. The researchers use a quantitative method to analyze the correlation between students’ grammar mastery and listening podcast achievement. The population of this research is the students of the English Education Department Faculty of Teaching and Educational Science of UIN Syarif Hidayatullah that consisted of 17 students. The study conducts two tests; a grammar test and a listening podcast test. Each test is given a score of 100 if the students answer all the questions correctly. The grammar test uses a gap-filling test containing 15 questions. In the listening test, the study uses an autobiography audio of Benjamin Franklin and uses a multiple-choice test containing 10 questions. The result shows that more than half of the sample got low scores in grammar but got high scores in listening comprehension. It means by using Karl Pearson’s correlation coefficient theory there is a correlation between their grammar tenses knowledge and their listening comprehension was normal positive.
No Social Distancing: Challenges and Collaboration in the Use of Computer-Mediated Learning (CML) Experienced by Selected Students in the Master of Education in English as Second Language Course during the Time of the Coronavirus
Authors: Catalan Amy Luz U.
Published: July 2022

The COVID-19 pandemic forced higher learning institutions to shift to the remote mode paving the way for computer-mediated learning (CML), a new yet familiar method of engagement between the teacher and the students. The main purpose of this study is to discuss the challenges experienced and the manner of peer collaboration as students maximized the teaching and learning process in such a setup. The study is descriptive-qualitative by design. It involves a thematic analysis of the corpus collected from selected participants who shared their experiences on CML. The respondents of this study were enrolled in the Master of Education in English as a Second Language (M.Ed.-ESL) course. Before distributing the questions, the researcher explained the features of CML. She, then, proceeded to secure their consent. After which, she coded and extracted the sub-themes and themes. These were collated, categorized in a matrix, and validated. Verbatim statements to support the results were provided. Results showed the themes on challenges are internet connectivity, online tools and gadgets, motivation, emotional and mental health, limited interaction, environmental distractions, physical wellness, time management, and access to learning resources. Themes on collaboration showed that the participants work and interact through online applications and tools, adjust to peers’ schedules, and provide mutual peer support. The results also showed challenges even while they were collaborating such as internet connectivity, unfamiliar forms of class participation, untimely feedback, among others. This study furnished valuable insights in overcoming the difficulties that students encounter in CML especially on the affective aspect of the CML. This should improve collaboration between the mentors and the students, and among the students themselves, which could be beneficial in adapting to this new setup. An audit of CML tools describing how to navigate CML could assist the teachers and students to overcome the challenges, especially on collaboration, and banish a sense of isolation that is posed by this mode of learning.
The Flexible Environment and Professional Educators toward the Use of Video in Flipped Learning
Authors: Andika I Gusti Agung Yoga , Myartwan I Putu Ngurah Wage , Agustini Dewa Ayu Eka
Published: July 2022

Globalization and the growth of technology required the students and teachers to improve the learning process. The English instruction was also in-line with the implementation of the technology, such as conducting the flipped learning.  In flipped learning, the teacher enables the student to learn through the online video. Therefore, this research aims to investigate the role of video in flipped learning-based English instruction. The current study was done at SMA Negeri Bali Mandara that involved three participants. The data collection was done through observation, in-depth interviews by using an interview guide, voice record, and online video. The qualitative data was analyzed through the phase of data reduction, data display, and conclusion drawing. The results showed that the students had positive responses and perceptions due to video in the learning process, which enabled them to improve their English skills.
Osing Dialect in Term of Place, Situation, and Consumer in Banyuwangi, East Java
Authors: Purwantari Shindy Pramesti
Published: July 2022

Dialect is a part of language variation in pronunciation, grammar, and vocabulary to sound variation. Indonesia has many of variation and diversity of dialect and one of them is Osing dialect or Basa Osing in Banyuwangi, East Java. There are some of differences using Osing dialect in term of place, situation, and consumer. The form of Osing dialect based on place, situation and consumer will be displayed in this study is dialect in real environment of local people. The purpose of this study is to identify and analyze Basa Osing in Banyuwangi, East Java using descriptive qualitative research. The participant of this research is local people of Osing. Collecting the data in this study is conducted by observation and interview. The structured interview is used to obtain the information from the respondent that already set the needs by researcher. The researcher observes their conversation with the local people and listen how they was talking each other. After that the research tapped the pronunciation about their conversation that carried out by the local people. The result of these observation is recorded and noted by the researcher. The findings of this study are that the use of Osing dialect varies depending on each term of place, situation, and consumer.
Higher Order Thinking Skills in English Language Teaching: The Case of Colleges of Education in Ghana
Authors: Fenyi Daniel Arkoh , Jones-Mensah Ivy
Published: July 2022

The teaching and learning of language has, for many years, been centered on the four language competencies- listening, speaking, reading and writing. However, in modern times, the need to train students with critical, analytical, evaluative, creative, and problem-solving skills in order to meet and deal with the complex nature of modern-day challenges and real-life issues has made the teaching and learning of Higher Order Thinking Skills (HOTS) indispensable. This study, therefore, investigates the attitude of language tutors towards the teaching of HOTS. This study has a descriptive qualitative approach. 39 English language tutors from the Colleges of Education who voluntarily availed themselves as participants were recruited for the study. Using semi-structured interviews and classroom observation as data collection instruments, the data were subjected to a thematic analysis. It is revealed that teachers perceive HOTS to be a good concept and hence use strategies such as asking open-ended questions, engaging learners in group works, classroom discussions, lecturing method, asking learners to produce and/or create their own materials and the giving of constructive feedback to implement it. Regardless of all their efforts, teachers face challenges such as incompetency, learner’s low proficiency, learner’s inability to grasp concepts, insufficient and irrelevant teaching materials, time, and financial constraints. The study, therefore, recommends that HOTS be given some more attention in language teaching in Ghana.
An Analysis of Code-Switching in “Ali & Ratu Ratu Queens” Movie
Authors: Natalia Putu Rosa
Published: July 2022

Code-switching becomes one of the phenomena that happens in society, especially for bilingualism and multilingualism. The ability to master more than one language makes a person sometimes switch from one language to another when speaking or better known as code-switching. Code-switching not only happens in real-life contexts but also happens in novels, talk shows, songs, movies, and other works. This research aimed to find out and analyze code-switching that appeared in Ali & Ratu Ratu Queens movie. In gaining the data, this study will use the qualitative descriptive method. The findings show inter-sentential code-switching is mostly used in this movie. Further, background and setting also take an important role for each of the code-switching that happens in this movie.
Phenomenon of the Use of Code-Switching of Balinese People Daily Communication in Buleleng Regency
Authors: Windicha Putri Ni Putu Wirliana
Published: July 2022

This study aims to analyze the types and functions of code-switching used in the daily conversation of Balinese people. This research was conducted in Tinga-tinga village, located in the Buleleng Regency, Gerokgak District, Bali Province. The participants or subject of this research were the surrounding people in the Tinga-tinga village. The instruments used in this research were observation and interview. The data was analyzed using a descriptive qualitative design to describe the result of the study in the Buleleng Regency daily conversation. After collecting data by conducting observations and interviews, this research found that people in Buleleng Regency mostly do code-switching in their everyday conversations using the local language or Bahasa Bali and Indonesian language or Bahasa Indonesia. The type of code-switching often used among the community is intra-sentential switching. People often do code-switching from one language to another in one sentence when communicating and when they find it difficult to explain something using the local language. On the other hand, the function of code-switching that Buleleng people often use is the referential function. They use the function to explain or express something in simple language that listeners easily understand. In addition, the function often used is the expressive function of code-switching.
An Analysis of Lexical Cohesion Found in “Photograph” Song Lyrics
Authors: Putri Ni Kadek Ayu Anggreni
Published: July 2022

The aim of this study is to find out the type and function of lexical cohesion used in the lyrics of Ed Sheeran's song entitled "Photograph" based on the theory of lexical cohesion. This study uses a qualitative method that focuses on the type of lexical cohesion, especially repetition (anaphora and epistrophe repetition), the synonym, antonym, and collocation. The researcher used observation with note-taking techniques to collect data. The results of this study are found all the types of lexical cohesion in the song lyrics entitled "Photograph". In Reiteration, the writer found 16 repetitions (anaphora and epistrophe repetition), 2 synonyms, 5 antonyms, and 1 collocation. So, it can be deduced that the most lexical cohesion that appears in the song lyrics entitled "Photograph" is Repetition. The lexical cohesion that emerges in the lyrics of the song, will make it more harmonious and meaningful.