Reviewerly/exHarmony huggingface dataset:
Impact of gameplay vs. reading on mental models of social-ecological systems: a fuzzy cognitive mapping approach

Climate change is a highly complex social-ecological problem characterized by system-type dynamics that are important to communicate in a variety of settings, ranging from formal education to decision makers to informal education of the general public.Educational games are one approach that may enhance systems thinking skills.This study used a randomized controlled experiment to compare the impact on the mental models of participants of an educational card game vs. an illustrated article about the Arctic social-ecological system.A total of 41 participants (game: n = 20; reading: n = 21) created pre-and post-intervention mental models of the system, based on a "fuzzy cognitive mapping" approach.Maps were analyzed using network statistics.Both reading the article and playing the game resulted in measurable increases in systems understanding.The group reading the article perceived a more complex system after the intervention, with overall learning gains approximately twice those of the game players.However, game players demonstrated similar learning gains as article readers regarding the climate system, actions both causing environmental problems and protecting the Arctic, as well as the importance of the base-and mid-levels of the food chain.These findings contribute to the growing evidence showing that games are important resources to include as strategies for building capacity to understand and steward sustainable social-ecological systems, in both formal and informal education.
Experimental Analysis in Hadoop MapReduce: A Closer Look at Fault Detection and Recovery Techniques

Hadoop MapReduce reactively detects and recovers faults after they occur based on the static heartbeat detection and the re-execution from scratch techniques. However, these techniques lead to excessive response time penalties and inefficient resource consumption during detection and recovery. Existing fault-tolerance solutions intend to mitigate the limitations without considering critical conditions such as fail-slow faults, the impact of faults at various infrastructure levels and the relationship between the detection and recovery stages. This paper analyses the response time under two main conditions: fail-stop and fail-slow, when they manifest with node, service, and the task at runtime. In addition, we focus on the relationship between the time for detecting and recovering faults. The experimental analysis is conducted on a real Hadoop cluster comprising MapReduce, YARN and HDFS frameworks. Our analysis shows that the recovery of a single fault leads to an average of 67.6% response time penalty. Even though the detection and recovery times are well-turned, data locality and resource availability must also be considered to obtain the optimum tolerance time and the lowest penalties.
Creating and destroying coherence with quantum channels

The emerging quantum technologies rely on our ability to establish and control quantum systems in nonclassical states, exhibiting entanglement and quantum coherence. It is thus crucial to understand how entanglement and coherence can be created in the most efficient way. In this work we study optimal ways to create a large amount of quantum coherence via quantum channels. For this, we compare different scenarios, where the channel is acting on an incoherent state, on states which have coherence, and also on subsystems of multipartite quantum states. We show that correlations in multipartite systems do not enhance the ability of a quantum channel to create coherence. We also study the ability of quantum channels to destroy coherence, proving that a channel can destroy more coherence when acting on a subsystem of a bipartite state. Crucially, we also show that the destroyed coherence on multipartite system can exceed the upper bound of those on the single system when the total state is entangled. Our results significantly simplify the evaluation of coherence generating capacity of quantum channels, which we also discuss.
A Lightweight Dense Connected Approach with Attention on Single Image Super-Resolution

In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training. To deal with deep model training problems, researchers utilize dense skip connections to promote the model’s feature representation ability by reusing deep features of different receptive fields. Benefiting from the dense connection block, SRDensenet has achieved excellent performance in SISR. Despite the fact that the dense connected structure can provide rich information, it will also introduce redundant and useless information. To tackle this problem, in this paper, we propose a Lightweight Dense Connected Approach with Attention for Single Image Super-Resolution (LDCASR), which employs the attention mechanism to extract useful information in channel dimension. Particularly, we propose the recursive dense group (RDG), consisting of Dense Attention Blocks (DABs), which can obtain more significant representations by extracting deep features with the aid of both dense connections and the attention module, making our whole network attach importance to learning more advanced feature information. Additionally, we introduce the group convolution in DABs, which can reduce the number of parameters to 0.6 M. Extensive experiments on benchmark datasets demonstrate the superiority of our proposed method over five chosen SISR methods.
FE-RetinaNet: Small Target Detection with Parallel Multi-Scale Feature Enhancement

Because small targets have fewer pixels and carry fewer features, most target detection algorithms cannot effectively use the edge information and semantic information of small targets in the feature map, resulting in low detection accuracy, missed detections, and false detections from time to time. To solve the shortcoming of insufficient information features of small targets in the RetinaNet, this work introduces a parallel-assisted multi-scale feature enhancement module MFEM (Multi-scale Feature Enhancement Model), which uses dilated convolution with different expansion rates to avoid multiple down sampling. MFEM avoids information loss caused by multiple down sampling, and at the same time helps to assist shallow extraction of multi-scale context information. Additionally, this work adopts a backbone network improvement plan specifically designed for target detection tasks, which can effectively save small target information in high-level feature maps. The traditional top-down pyramid structure focuses on transferring high-level semantics from the top to the bottom, and the one-way information flow is not conducive to the detection of small targets. In this work, the auxiliary MFEM branch is combined with RetinaNet to construct a model with a bidirectional feature pyramid network, which can effectively integrate the strong semantic information of the high-level network and high-resolution information regarding the low level. The bidirectional feature pyramid network designed in this work is a symmetrical structure, including a top-down branch and a bottom-up branch, performs the transfer and fusion of strong semantic information and strong resolution information. To prove the effectiveness of the algorithm FE-RetinaNet (Feature Enhancement RetinaNet), this work conducts experiments on the MS COCO. Compared with the original RetinaNet, the improved RetinaNet has achieved a 1.8% improvement in the detection accuracy (mAP) on the MS COCO, and the COCO AP is 36.2%; FE-RetinaNet has a good detection effect on small targets, with APs increased by 3.2%.
NIME and the Environment: Toward a More Sustainable NIME Practice

This paper addresses environmental issues around NIME research and practice.We discuss the formulation of an environmental statement for the conference as well as the initiation of a NIME Eco Wiki containing information on environmental concerns related to the creation of new musical instruments.We outline a number of these concerns and, by systematically reviewing the proceedings of all previous NIME conferences, identify a general lack of reflection on the environmental impact of the research undertaken.Finally, we propose a framework for addressing the making, testing, using, and disposal of NIMEs in the hope that sustainability may become a central concern to researchers.
A Literary Review on Curriculum Implementation Problems

The aim of this study is to examine studies on curriculum implementation problems in literature and to compile some measures that can be taken to reduce these problems. Considering the criteria of this study as a result of the preliminary examination, 39 articles were excluded and 72 articles were included in this review study. Descriptive and content analysis methods were used to analyze included studies. With content analysis, studies were examined and codes were determined about curriculum implementation problems. The researcher and a co-observer studied together to confirm codes. As a result, curriculum implementation problems were presented in four headings, curriculum implementation problems related to teacher, related to students, related to curriculum and related to schools. With this study, curriculum implementation problems can be determined and necessary measures can be taken by teachers, educators and administrators.
A Bird’s Eye View of Natural Language Processing and Requirements Engineering

Natural Language Processing (NLP) has demonstrated effectiveness in many application domains.NLP can assist software engineering by automating various activities.This paper examines the interaction between software requirements engineering (RE) and NLP.We reviewed the current literature to evaluate how NLP supports RE and to examine research developments.This literature review indicates that NLP is being employed in all the phases of the RE domain.This paper focuses on the phases of elicitation and the analysis of requirements.RE communication issues are primarily associated with the elicitation and analysis phases of the requirements.These issues include ambiguity, inconsistency, and incompleteness.Many of these problems stem from a lack of participation by the stakeholders in both phases.Thus, we address the application of NLP during the process of requirements elicitation and analysis.We discuss the limitations of NLP in these two phases.Potential future directions for the domain are examined.This paper asserts that human involvement with knowledge about the domain and the specific project is still needed in the RE process despite progress in the development of NLP systems.
Deep Learning-based Natural Language Processing Methods Comparison for Presumptive Detection of Cyberbullying in Social Networks

Due to TIC development in the last years, users have managed to satisfy many social experiences through several digital media like blogs, web and especially social networks.However, not all social media users have had good experiences with these media.Since there are malicious users that are able to cause negative psychological effects over people, this is called cyberbullying.For this reason, social networks such as Twitter are looking to implement models based on deep learning or machine learning capable of recognizing harassing comments on their platforms.However, most of these models are focused on the use of English language and there are very few models adapted for Spanish language.This is why, in this paper we propose the evaluation of an RNN+LSTM neural network, as well as a BERT model through sentiment analysis, to perform the detection of cyberbullying based on Spanish language for Ecuadorian accounts of the social network Twitter.The results obtained show a balance between execution time and accuracy obtained for the RNN + LSTM model.In addition, evaluations of comments that are not explicitly offensive show a better performance for the BERT model, which outperforms its counterpart by 20%.
Wikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting Wikipedia Hyperlinks

Masked language models have quickly become the de facto standard when processing text.Recently, several approaches have been proposed to further enrich word representations with external knowledge sources such as knowledge graphs.However, these models are devised and evaluated in a monolingual setting only.In this work, we propose a languageindependent entity prediction task as an intermediate training procedure to ground word representations on entity semantics and bridge the gap across different languages by means of a shared vocabulary of entities.We show that our approach effectively injects new lexicalsemantic knowledge into neural models, improving their performance on different semantic tasks in the zero-shot crosslingual setting.As an additional advantage, our intermediate training does not require any supplementary input, allowing our models to be applied to new datasets right away.In our experiments, we use Wikipedia articles in up to 100 languages and already observe consistent gains compared to strong baselines when predicting entities using only the English Wikipedia.Further adding extra languages lead to improvements in most tasks up to a certain point, but overall we found it non-trivial to scale improvements in model transferability by training on ever increasing amounts of Wikipedia languages.
Topology of Parametrized Motion Planning Algorithms

We introduce and study a new concept of parametrized topological complexity: a topological invariant motivated by the motion planning problem of robotics. In the parametrized setting, a motion planning algorithm has a high degree of universality and flexibility and can function under a variety of external conditions (such as positions of obstacles). We explicitly compute the parametrized topological complexity of obstacle-avoiding collision-free motion of many particles (robots) in 3-dimensional space. Our results show that the parametrized topological complexity can be significantly higher than the standard (nonparametrized) invariant.
Modular Networks for Compositional Instruction Following

Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g.navigating to landmarks or picking up objects) observed during training.We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals.In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type.A sequence of modules to execute is chosen by learning to segment the instructions and predicting a subgoal type for each segment.When compared to standard, nonmodular sequence-to-sequence approaches on ALFRED (Shridhar et al., 2020), a challenging instruction following benchmark, we find that modularization improves generalization to novel subgoal compositions, as well as to environments unseen in training.
Generative Adversarial Transformers

We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linear efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model's strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-the-art results in terms of image quality and diversity, while enjoying fast learning and better data-efficiency. Further qualitative and quantitative experiments offer us an insight into the model's inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. An implementation of the model is available at this https URL.
Unifying Vision-and-Language Tasks via Text Generation

Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring expression comprehension, and a language decoder for image captioning, etc. To alleviate these hassles, in this work, we propose a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation, where our models learn to generate labels in text based on the visual and textual inputs. On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models. Moreover, our generative approach shows better generalization ability on questions that have rare answers. Also, we show that our framework allows multi-task learning in a single architecture with a single set of parameters, achieving similar performance to separately optimized single-task models. Our code is publicly available at: this https URL
EventPlus: A Temporal Event Understanding Pipeline

We present EventPlus, a temporal event understanding pipeline that integrates various stateof-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction.Event information, especially event temporal knowledge, is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events.Event-Plus as the first comprehensive temporal event understanding pipeline provides a convenient tool for users to quickly obtain annotations about events and their temporal information for any user-provided document.Furthermore, we show EventPlus can be easily adapted to other domains (e.g., biomedical domain).We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.
Sparse reward exploration via novelty search and emitters

Reward-based optimization algorithms require both exploration, to find rewards, and exploitation, to maximize performance. The need for efficient exploration is even more significant in sparse reward settings, in which performance feedback is given sparingly, thus rendering it unsuitable for guiding the search process. In this work, we introduce the SparsE Reward Exploration via Novelty and Emitters (SERENE) algorithm, capable of efficiently exploring a search space, as well as optimizing rewards found in potentially disparate areas. Contrary to existing emitters-based approaches, SERENE separates the search space exploration and reward exploitation into two alternating processes. The first process performs exploration through Novelty Search, a divergent search algorithm. The second one exploits discovered reward areas through emitters, i.e. local instances of population-based optimization algorithms. A meta-scheduler allocates a global computational budget by alternating between the two processes, ensuring the discovery and efficient exploitation of disjoint reward areas. SERENE returns both a collection of diverse solutions covering the search space and a collection of high-performing solutions for each distinct reward area. We evaluate SERENE on various sparse reward environments and show it compares favorably to existing baselines.
Learning and Evaluating a Differentially Private Pre-trained Language Model

Contextual language models have led to significantly better results on a plethora of language understanding tasks, especially when pre-trained on the same data as the downstream task.While this additional pre-training usually improves performance, it can lead to information leakage and therefore risks the privacy of individuals mentioned in the training data.One method to guarantee the privacy of such individuals is to train a differentiallyprivate model, but this usually comes at the expense of model performance.Moreover, it is hard to tell given a privacy parameter what was the effect on the trained representation.In this work we aim to guide future practitioners and researchers on how to improve privacy while maintaining good model performance.We demonstrate how to train a differentially-private pre-trained language model (i.e., BERT) with a privacy guarantee of = 1 and with only a small degradation in performance.We experiment on a dataset of clinical notes with a model trained on a target entity extraction task, and compare it to a similar model trained without differential privacy.Finally, we present experiments showing how to interpret the differentially-private representation and understand the information lost and maintained in this process.
Distance learning in the time of Covid-19: Exploring students’ anxiety

Performing sudden and uncertain distance learning can be difficult and complicated experiences for students as they may suffer from anxiety. The study of anxiety in Indonesia typically concentrated on the relation of anxiety with speech production (Anandari, 2015), gender differences (Nurhaeni, 2019), four basic skills (reading, writing, speaking, and listening) (Male, 2018) and its impact on language learning (Budiman, Ngadiso, &amp; Suparno, 2017). The study of anxiety in distance learning especially in the time of crisis, like the outbreak of Covid-19 is rare. Thus, the study to examine the distance learning anxiety faced by higher education students in the time of crisis (Covid-19) and explore the cause of those anxieties is important. The data of this study was collected using two instruments, a questionnaire and an interview. To get the data, the researcher modified the Foreign Language Classroom Anxiety Scale questionnaire (FLCAS) by Howitz and Young (1991) which is commonly applied to determine the level of anxiety. Since the study concentrated in distance learning, the researchers also decided to adopt an “On-line Language Learning Anxiety (OLLA) Inventory” (Majid, Othman, &amp; Rahmat, 2007). Findings revealed that students experienced higher anxiety levels in the beginning stage of distance learning. A year after the implementation of distance learning, students had adapted well and the level of anxiety they encountered was reduced. Yet, technical problems like unstable internet connection and devices’ malfunction that occurred during distance learning still triggered much anxiety among students since that condition made them lose the opportunity to receive some important information and chances to actively participate in online activities as well as improve their skills.