Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of patch size and nest density on artificial nest survival in grasslands

ABSTRACT Habitat patch size is sometimes negatively associated with nest predation rates, but there remains considerable variation in its effect size. The area, shape, amount of edge, and quality of vegetation associated with a patch all correlate with patch size, and each can alter nest predation rates independently; however, the picture is further complicated because the patch itself is part of a larger landscape that may influence processes within the patch. To separate the effect of patch size from other ecological correlates of nest predation, we used artificial nests within a full factorial design where treatments included small and large habitat patch sizes and low and high artificial nest densities. We incorporated treatments, measurements of vegetation structure surrounding nests, and habitat availability within the landscape into a survival analysis of 617 artificial nests across 12 study sites and 2 replicate trials. We modeled daily survival of nests and found that it was similar across patch size treatment groups. Our results suggest that the availability of additional nesting habitat in the landscape beyond fields directly bordering the nesting habitat may have diminished the effect of our patch size and nest density treatments, creating a system where large and small patches have similar predicted nest survival. RESUMEN (Spanish) El tamaño del parche de hábitat se encuentra a veces negativamente asociado con las tasas de depredación, aunque queda una variación considerable en el tamaño de dicho efecto. Todas, el área, forma, cantidad de borde y calidad de la vegetación asociada con un parche se encuentran correlacionadas con tamaño del parche, y cada una de ellas puede alterar la tasa de depredación independientemente. Sin embargo, el escenario se complica porque el parche propiamente dicho es parte de un paisaje mayor que puede influenciar procesos al interior del mismo. Para separar el efecto del tamaño de parche de otras variables ecológicas correlacionadas, usamos nidos artificiales en un diseño factorial completo donde los tratamientos incluyeron parches de hábitat de tamaños grandes y pequeños así como densidades de nidos altas y bajas. Incorporamos tratamientos, medidas de estructura de la vegetación alrededor de los nidos y disponibilidad de hábitat al interior del paisaje en un análisis de sobrevivencia de 617 nidos artificiales en 12 sitios de estudio y 2 tratamientos réplica. Modelamos la sobrevivencia diaria de los nidos y encontramos que ésta fue similar entre grupos con tratamientos de tamaño de parche. Nuestros resultados sugieren que la disponibilidad de hábitat de anidación adicional en el paisaje más allá de los campos que circundan directamente el hábitat de a nidación podrían disminuir el efecto de nuestros tratamientos de tamaño de parche y densidad de nidos, creando un sistema donde parches grandes y pequeños tienen una predicción similar de sobrevivencia de nidos. Palabras clave: depredación de nidos, multi-escala, nidos de aves, paisaje.
Marginality: A Critical Review of the Concept

Marginality is a widely used concept in the literature on inequality and development. It has gained momentum, especially in the context of rising inequality in the post-liberalised and globalised world. This article looks into the evolution of marginality, analyses its role in the developmental discourse and argues that the interrelation between objective and subjective aspects of marginality constitutes the idea of it. This discussion identifies marginality as a measure of inequality in a given social context and time and indicates the degree of inequality experienced by individuals or groups. The article is divided into four parts. The first part looks into the change in meaning and components of marginality. The second part discusses marginality with other closely related concepts such as exclusion, poverty, the culture of poverty and precarity. The third part engages with the constituents of the marginal situation, and the fourth part discusses different typologies available to understand the dimensions of marginality.
Enhanced multipolar transitions near 2D material nanoislands

Generally, the spontaneous emission rate of an emitter is computed via classical electromagnetic simulations, modelling the dipolar source as a point. The quantum and classical simulations usually give identical results, since the size of the quantum emitter (QE) is orders of magnitude smaller than the wavelength. However, close to nanophotonic structures sustaining strongly confined fields, such as graphene and other 2D materials, the approximation is not valid anymore: the dipolar transition is no longer dominant and can be surpassed by the rates of higher-order transitions i.e. transitions beyond the dipolar one, such as two-photon processes, electric and magnetic multipolar transitions. The determination of these rates requires the spatial variation of the Green’s function along the spatial extent of the QE, which is not easily accessed by conventional numerical solvers. Here, we compute the high-order transition rates of a hydrogen-like atom close to graphene nanoislands (triangle and crescent geometries), by developing a method valid for any material of any shape, and show, as an example, quadrupolar transition rates becoming 100 times larger than dipolar ones. Engineering the high-order transitions through the complex nanophotonic environment promises new infrared sources, entangled two-photon sources and other novel physical effects.
An adaptive Monte Carlo test for uncertain discrete-event dynamic systems

The analysis of uncertain dynamic discrete-event systems is generally intractable by deterministic numeric methods. In this paper, we propose an adaptive Monte Carlo test method to analyze systems. In contrast to the conventional methods of estimating the probability that a system fails to satisfy prespecified requirements, our goal is to determine whether the probability that the system violates the requirements. To accomplish this goal, we exploit a testing method based on the sequential probability ratio (SPRT) method invented by Wald. We demonstrate that such method can result in a substantial reduction of computational complexity as compared to conventional methods. To make the test method rigorous, we develop exact methods for computing the probability of making wrong decisions and the average number of simulations runs. The proposed method can be applied to investigate the stability of a control system with parametric uncertainty.
The Role of Learning Theory in Child-Computer Interaction - A Semi-Systematic Literature Review

In this paper, we explore the role of learning theory in the Child-computer interaction (CCI) community’s leading venues: the Interaction Design and Children (IDC) conference and the International Journal of Child-Computer Interaction (IJCCI). Searching all publications in the IDC conference proceedings and IJCCI, 63 papers that use the word stem ‘learn*’ in title, abstract and keywords were included in the corpus. Based on an analysis of these papers, our semi-systematic literature review demonstrates that assessment of learning regarding transfer of learning and controlled groups is rare, that the main role for learning theory is application, and that four main theoretical positions on learning can be recognized: constructivism, constructionism, cognitive theories, and socio-cognitive theory. The paper further presents an overview of how and which learning theories are used, and outlines paths for future CCI research based on the results.
Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches

Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. To determine the most appropriate node to host a given workload, Kubernetes makes use of a scheduler that takes into account a set of hard and soft constraints defined by workload owners and cluster administrators. Despite being highly configurable, the default Kubernetes scheduler cannot fully meet the requirements of emerging applications, such as machine/deep learning workloads and edge computing applications. This has led to different proposals of custom Kubernetes schedulers that focus on addressing the requirements of the aforementioned applications. Since the related literature is growing in this area, we aimed, in this survey, to provide a classification of the related literature based on multiple criteria, including scheduling objectives as well as the types of considered workloads and environments. Additionally, we provide an overview of the main approaches that have been adopted to achieve each objective. Finally, we highlight a set of gaps that could be leveraged by academia or the industry to drive further research and development activities in the area of custom scheduling in Kubernetes.
MEKIDS Media Education with Kids through Interactive Digital Storytelling

New digital technologies give rise to many concerns and debates in primary education, as educational institutions and practitioners are challenged by multiple developments, ranging from the transformation of media environments to calls for transversal media education from early on. Educational research can expand the available repertoire of teachers to design learning arrangements, contribute in the user-centred design and development of tools in authentic educational settings and explore new applications of established educational theories. To assess potentials and challenges for learning based on narrative thinking and interactive storytelling, “MEKIDS” (Media Education with Kids through Interactive Digital Storytelling) explores the use of interactive narratives as a way for teachers to introduce thematically complex subjects in learning settings with young children (8-11 years). In specific, a hypertextual interactive tool (’Fantastinomio’) is developed to facilitate creative storytelling with young children about subjects that can be curated by educators. The study follows a design-based research approach, also known as educational design research [12] and is carried out with various research methods in field studies across the stages of exploration, design and evaluation. Preliminary findings show that the ability to customize educational technology to didactic needs has multiple benefits, for example through the establishment of connections between intended learning outcomes and the life worlds of children through the use of children-made story elements.
Note: Urbanization and Literacy as factors in Politicians’ Social Media Use in a largely Rural State: Evidence from Uttar Pradesh, India

With Twitter growing as a preferred channel for outreach among major politicians, there have been focused efforts on online communication, even in election campaigns in primarily rural regions. In this paper, we examine the relationship between politicians’ use of social media and the level of urbanization and literacy by compiling a comprehensive list of Twitter handles of political party functionaries and election candidates in the run-up to the 2022 State Assembly elections in Uttar Pradesh, India. We find statistically significant relationships between political Twitter presence and levels of urbanization and with levels of literacy. We also find a strong correlation between vote share and Twitter presence in the winning party, a relationship that is even stronger in urban districts. This provides empirical evidence that social media is already a central part of electoral outreach processes in the Global South, but that this is still selectively more relevant to voters in, and politicians standing for elections from urban and higher-educated regions.
Fake News Detection Based on Multi-Modal Classifier Ensemble

With the advent of the era of big data, the ubiquity of multi-modal fake news has increasingly affected information dissemination and consumption. Measurements should be taken to identify multimodal fake news for improving the credibility of news. However, existing single-modal fake news detection models fail to detect fake news based on complete multi-modal information, while multimodal models are often difficult to fully utilize the original information of each single modality to obtain the ultimate accuracy. To tackle above problems, we propose a novel multi-modal fake news detection method, called fake news detection based on multi-modal classifier ensemble, which takes into account the advantages of both single-modal and multi-modal models. Specifically, we design two single-modal classifiers for text and image inputs respectively. We then establish a similarity classifier to calculate the feature similarity over the modalities. We also build an integrity classifier that utilizes integral multi-modal information. Finally, all classifier outputs are integrated with an ensemble learning to increase the classification accuracy. Furthermore, we introduce the center loss, to reduce intra-class variance, which is helpful to achieve higher detection accuracy. The cross-entropy loss is used to maximize the inter-class variations while the center loss is used to minimize the intra-class variations so that the discriminate ability of the learned news features can be enhanced. Experimental results on both Chinese and English datasets demonstrate that the proposed method outperforms the baseline fake news detection approaches.
Predicting the Severity of the Road Accidents in Senegal: An Empirical Study

Road accidents are a major problem, the consequences of which have negative impacts on the economy, health and development of a country in general. The recrudescence of road accidents in Senegal during these last years has aroused a great debate in which certain responsibilities have been pointed out. A serious road traffic accident constitutes in most cases an emotional and economic disaster, hence the need to find sustainable solutions for the improvement of road safety. The objective of this work is to identify the major risk factors of road accidents in Senegal in order to set up a training corpus for the predictive study of the gravity of these accidents. According to our research on the bibliography, there is no study on the implementation of a corpus adapted to the data of accidents in Senegal. In this research, we used an exploratory study on the collected data to determine the factors most likely to cause accidents and supervised learning for predictive modeling of accident severity. The results showed that modelling with supervised learning can be used as an accident severity prediction system with promising accuracy.
Grasping Derivatives: Teaching Mathematics through Embodied Interactions using Tablets and Virtual Reality

Grasping mathematics can be difficult. Often, students struggle to connect mathematical concepts with their own experiences and even believe that math has nothing to do with the real world. To create more concreteness in mathematics education, we focus on the role of the body in learning, and more specifically, embodied interactions for learning derivatives. In this project, we designed an embodied game to teach derivatives, and validated our design with a panel of experts. We then used this prototype to explore different embodied interactions in terms of usability, sense of embodiment, and learning outcomes. In particular, we evaluated different degrees of embodied interactions, and different types of embodied interactions in Virtual Reality. We conclude with insights and recommendations for mathematics education with embodied interactions.
An Initial Study of Depression Detection on Mandarin Textual through BERT Model

Depression is one of the most common mental disorders nowadays. Cases of depression is increasing significantly in Malaysia during Covid-19 pandemic. In the era of advancement of Internet technology, number of social media user is growing exponentially and became part of human lifestyle. Social media has provided a platform to their user to share their thought and feelings effortlessly. Previous studies demonstrated that the possibility and capability of artificial intelligence technology on analyzing texts on social media for detecting depression tendency. However, most of the study are conducted on English textual content. Mandarin is ranked second popular spoken language in the world, thus it is worth to explore depression detection technique on Mandarin textual content. In this study, BERT model is proposed for conducting depression detection on social media. Mandarin text data is targeted as there are less studies exploring to depression detection on Mandarin text. In addition, it is also worth to find out the capability and performance of pre-trained BERT model on this particular tasks especially on Mandarin language. This is an initial study and there are more works yet to be done. This paper will focus on the dataset acquisition and analysis.
Knowledge management in an ICT classroom: an agile group dynamics for innovative teaching and learning

This paper was an attempt to investigate agile group dynamics and knowledge sharing in an ICT project classroom. The paper furthermore explains how agile group dynamics influence logical reasoning and information processing capabilities of project team members. This research was based on a qualitative, action-research approach. Twenty students at an institution of higher education expressed their experiences of agile project dynamics. The sample was selected using a purposeful convenient sampling method. The data in the ICT classroom were collected by means of semistructured interviews, the hybrids of formal interviews and observations. Findings highlighted some agile methods and techniques (adaptive innovation agile strategies, conceptual mapping, telling and predicting, communication skills, entrepreneurial initiatives, show time, agile planning, discipline of dialogues,) that were successfully applied in the ICT classroom. Findings indicated the positive influence of agile group dynamics on learners’ information processing, reasoning skills and knowledge sharing. In the promotion of students’ concurrent thinking and knowledge sharing appropriate attention should be given to a multidisciplinary agile group dynamics within the ICT classroom.
ADPOLY Student Information Chatbot

∗ This paper presents a chatbot for educational purposes, in which students or outsiders can consult regarding university admissions or other related topics. We discussed how utilizing a chatbot can be more beneficial and what impact it will have on learners in the first part. In the second section, we compared and contrasted different types of chatbots utilizing different approaches and programming languages. This part aided us to determine which programming style would best meet our chatbot’s requirements. Finally, a brief overview of chatbot architecture was provided, along with an explanation of the process of creating a chatbot.
Documenting Web Data for Social Research (#DocuWeb22): A participatory workshop for developing structured and reusable practices

With this half-day, in-person workshop, we attempt to collaboratively discover best practices as well as frequent pitfalls encountered when working with Web data. Participants will first be presented with different perspectives on the significance of data quality in this specific context and familiarized with existing, structured approaches for the critical reflection on and documentation of data collection processes, before being invited to share their own experiences with the collection, use and documentation of Web data. We hope to thereby inspire participants to further integrate data documentation practices into their research processes, and for us to learn from the participants’ experiences in order to improve upon existing documentation frameworks for Web data. More details of the workshop, including the planned activities can be found at https://frohleon.github. io/DocuWeb22/.
Exploring Caregiver Support for and Conceptualizations of Their Children’s Entrepreneurship in Interactive Online Spaces

As the role of interactive digital media continues to grow in our ever-connected lives, so too do the expectations of the caregivers and parents of today’s young people, particularly in scaffolding and brokering their experiences with online interactive media. Consequently, there are more and more ways for young people to socialize, learn, and create online. While these new forms of connection and access to information lead to many positive outcomes, some bring more challenging and complicated implications. In particular, young people are increasingly able to earn money and make a profit online from the fruits of their computational learning. This study seeks to better understand the complex landscape of young people’s experiences engaging with paid online work through detailed qualitative studies. I seek to explore the tensions around youth creation online and caregiver’s abilities to scaffold these experiences, with a particular emphasis on how caregivers conceptualize their children creating and become developers in online spaces.
CodeStruct: Design and Evaluation of an Intermediary Programming Environment for Novices to Transition from Scratch to Python

Transitioning from block-based programming environments to conventional text-based programming languages is a challenge faced by many learners as they progress in their computer science education. In this paper, we introduce CodeStruct, a new intermediary programming environment for novices designed to support children who have prior experience with block-based programming to ease the eventual transition to text-based programming. We describe the development of CodeStruct and its key design features. We then present the results from a two-week long programming class with 26 high school students (ages 12-16; M=14 years) investigating how CodeStruct supported learners in transitioning from Scratch to Python. Our findings reveal how learners used the scaffolds designed into CodeStruct to support their transition from blocks to text, and that transitioning to CodeStruct reduced completion time (1.98x) and help requests (4.63x) when compared to transitioning directly to Python. Finally, learners that used CodeStruct, performed equally well (and slightly better in 10/16 programming activities) in their final transition to fully text-based Python programming.
KidConnect VR: Technology to Stay Connected

Having a healthy social life is an important part of our mental health, especially when it comes to children. Kids need to feel a connection between the environments in which they both live and play. In the last two years, COVID-19 pandemic has impacted our lives both personally and professionally. Because of the pandemic, meeting people in physical locations has been problematic. This situation has only amplified the need to foster connections. Technology plays a crucial role in helping children stay connected, however, we have found that children are more accepting of this when they are allowed to be able to take more active role in developing these platforms. Creating virtual environments, for example, has been shown to increase the interest of children in reaching out to their friends and family, and connecting with others across time zones, languages, and cultures. The children’s Idea Booklet, from the 2022 ACM Interaction Design and Children (IDC) Research & Design Challenge identified significant interest in children using VR headset technology to connect with friends and family. Our research team, KidsTeam, built on these ideas to design KidConnect VR. KidConnect VR allows kids to connect with others via a virtual environment. This environment can be built to look like their surrounding area. In the virtual world, children can play, chat, and even study with friends.