Reviewerly/exHarmony huggingface dataset:
Co-Designing for Equity in Informal Science Learning: A Proof-of-Concept Study of Design Principles

Informal science learning has great potential to engage diverse learners, but faces issues of persistent inequities. While systemic change is needed to address these issues at a structural level, there is also a need for practical tools to support the organisations and the educators who are working to engage audiences in informal science that is authentic, culturally responsive, interest driven and learner centered. This article presents a collection of design principles, generated through a design approach which actively involved informal science learners, practitioners and researchers from nineteen countries as contributors. We present the design approach adopted, and suggest that participatory design methods could play a role in supporting equity efforts in informal science learning since several of the educators involved in the process decided to adopt participatory methods in their own practice. We also present an overview of the design principles generated through this process, and discuss the application of an early draft of these in an authentic informal science education programme. By adopting and adapting these principles and approaches in their practices, educators can work towards creating equitable and transformative informal science learning environments and experiences.
The History and Future of XR

This chapter will look at the big picture of the XR industry and what factors in its development are, foremost, relevant to UX design. This overview entails snapshots of the history of VR and AR, the elements that drive an XR experience, and the ecology of devices, technologies, use cases, as well as usability issues that frequently come up. The chapter will look at macro trends and future outlook behind spatial computing and the backstory of gamification and how it relates to the success story of UX design and XR in the digital economy.
Hierarchical Graph Convolutional Networks for Jointly Resolving Cross-document Coreference of Entity and Event Mentions

This paper studies the problem of crossdocument event coreference resolution (CDECR) that seeks to determine if event mentions across multiple documents refer to the same real-world events.Prior work has demonstrated the benefits of the predicateargument information and document context for resolving the coreference of event mentions.However, such information has not been captured effectively in prior work for CDECR.To address these limitations, we propose a novel deep learning model for CDECR that introduces hierarchical graph convolutional neural networks (GCN) to jointly resolve entity and event mentions.As such, sentencelevel GCNs enable the encoding of important context words for event mentions and their arguments while the document-level GCN leverages the interaction structures of event mentions and arguments to compute document representations to perform CDECR.Extensive experiments are conducted to demonstrate the effectiveness of the proposed model.
Driving Video Fixation Prediction Model Via Spatio-Temporal Networks and Attention Gates

Driving fixation prediction is becoming an essential research problem in human-like driving systems or advanced driver assistance systems (ADAS) in a dynamic driving environment. However, it is still a lack of driving video fixation prediction models that can dynamically predict drivers’ fixational locations. In this work, we propose a driving video fixation prediction model via spatio-temporal networks and attention gates method, named as DSTANet, to predict drivers’ attention in the dynamic driving videos. The spatial and temporal driving information are both considered in DSTANet by convolutional long short-term memory (ConvLSTM). In addition, the human attention mechanism is designed to filter some driving-irrelevant information via the attention gates (AGs). The experimental results indicate that the proposed DSTANet outperforms the state-of-the-art saliency models and predicts drivers’ attentional spatial locations more accurately. Furthermore, the time-series prediction results show that DSTANet is more robust and coherent than others and includes more temporal information.
Application on Online Process Learning Evaluation Based on Optimal Discrete Hopfield Neural Network and Entropy Weight TOPSIS Method

Sustainable development education respects differences and encourages different assessment methods to evaluate students. During the epidemic, many colleges’ examinations changed from offline to online. How to fully consider students’ process learning status and make a reasonable evaluation of students is worthy of research. Based on the process learning data of a course in a university in China, this study establishes a discrete Hopfield neural network model to classify the test samples. In the process of modelling, the grey correlation analysis method is used to optimize the elements affecting students’ comprehensive evaluation index, and it solves the problem of failure of the model due to the large gap between the factors in the traditional discrete Hopfield neural network model. Then, the entropy right TOPSIS method is used to rank samples with the same evaluation grade. Teachers can objectively evaluate each student’s process learning performance according to the ranking results. Finally, the article compares and analyzes the evaluation results of various different methods. The analysis results believe that the optimized discrete Hopfield neural network is feasible in the process learning evaluation, and the model evaluation results are more objective and comprehensive.
Variational Autoencoders: A Hands-Off Approach to Volatility

A volatility surface is an important tool for pricing and hedging derivatives. The surface shows the volatility that is implied by the market price of an option on an asset as a function of the option's strike price and maturity. Often, market data is incomplete and it is necessary to estimate missing points on partially observed surfaces. In this paper, we show how variational autoencoders can be used for this task. The first step is to derive latent variables that can be used to construct synthetic volatility surfaces that are indistinguishable from those observed historically. The second step is to determine the synthetic surface generated by our latent variables that fits available data as closely as possible. As a dividend of our first step, the synthetic surfaces produced can also be used in stress testing, in market simulators for developing quantitative investment strategies, and for the valuation of exotic options. We illustrate our procedure and demonstrate its power using foreign exchange market data.
PRE-SERVICE TEACHERS’ CONCEPTIONS AND COMPETENCES ON DIGITAL LITERACY IN AN EFL ACADEMIC WRITING SETTING

This case study seeks to examine pre-service teachers’ digital literacy conceptions in an EFL academic writing context. Despite the recent growth of research regarding general conceptions of digital literacy, little attention has been given to its conception focusing on EFL digital writing environment. To bridge the gap, this research offers a new perspective of the conception of digital literacy from the perspective of pre-service teachers in their academic writing setting. Also, the study aims to identify pre-service teachers’ competences concerning the predominant dimensions of digital literacy encompassing critical thinking, online safety skills, digital culture, collaboration and creativity, finding information, communication, and functional skills. This case study involved both quantitative and qualitative data taken from 107 pre-service teachers’ online questionnaires and one 5-member focus group discussion delivered to pre-service teachers taking Academic Writing subjects in English Language Education Department in an urban university in Indonesia. While thematic analysis was involved for qualitative data, the quantitative ones were analyzed using descriptive analyses. Emergent themes related to the conception of digital literacy in academic writing context included basic conception of digital literacy, competences related to digital literacy, awarness of the importance of digital literacy, and challenges of digital literacy. In general, the result of the study revealed that the pre-service teachers’ conceptions of digital literacy were principally associated with the narrow proficiency of utilizing online tools and technological devices and set aside a critical mindset. Further, in spite of the fact that most participating students were found to have lack of understanding of critical thinking and digital culture towards digital literacy, they appeared to possess the competencies of finding information, communication, and functional skills. Additionally, quantitative result of the pre-service teachers’ competences demonstrated that communication dimension was the highest of all with the mean value of 3.95, followed by online safety skills (3.87), finding information (3.79), critical thinking (3.77), functional skills (3.75), as well as collaboration and creativity (3.43). The lowest mean (3.40) belonged to digital culture dimension. The findings have important implications for developing digital literacy framework in an EFL academic writing.
ChicHealth @ MEDIQA 2021: Exploring the limits of pre-trained seq2seq models for medical summarization

In this article, we will describe our system for MEDIQA2021 shared tasks. First, we will describe the method of the second task, multiple answer summary (MAS). For extracting abstracts, we follow the rules of (CITATION). First, the candidate sentences are roughly estimated by using the Roberta model. Then the Markov chain model is used to evaluate the sentences in a fine-grained manner. Our team won the first place in overall performance, with the fourth place in MAS task, the seventh place in RRS task and the eleventh place in QS task. For the QS and RRS tasks, we investigate the performanceS of the end-to-end pre-trained seq2seq model. Experiments show that the methods of adversarial training and reverse translation are beneficial to improve the fine tuning performance.
Systematic Review on Realism Research Methodologies on Immersive Virtual, Augmented and Mixed Realities

Proper evaluation of realism in immersive virtual experiences is crucial to ensure optimisation of resources. This way, we can take better decisions while designing realistic immersive experiences, prioritising factors that have a higher impact on the perceived realism of the virtual experience. This systematic review aims to provide readers with an overview of methodologies used throughout the literature to evaluate realism in immersive virtual, augmented and mixed reality. A total of 79 from 1300 gathered articles met the eligibility criteria and were analysed. Results have shown that virtual reality is by far the platform where realism studies were performed. Head-mounted displays are by far the preferred equipment for such studies. Visual realism is the most researched, followed by audiovisual. The majority of methodologies consisted of subjective, as well as a combination of objective and subjective measures. The most used evaluation instrument is questionnaires where many of which are custom and non-validated. Presence questionnaires are the most used ones and are often used to evaluate the presence, perceived realism and involvement. Cybersickness evaluation is consistently assessed by one self-report questionnaire.
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization

In this paper, we propose a novel stochastic gradient estimator -- ProbAbilistic Gradient Estimator (PAGE) -- for nonconvex optimization. PAGE is easy to implement as it is designed via a small adjustment to vanilla SGD: in each iteration, PAGE uses the vanilla minibatch SGD update with probability $p_t$ or reuses the previous gradient with a small adjustment, at a much lower computational cost, with probability $1-p_t$. We give a simple formula for the optimal choice of $p_t$. Moreover, we prove the first tight lower bound $\Omega(n+\frac{\sqrt{n}}{\epsilon^2})$ for nonconvex finite-sum problems, which also leads to a tight lower bound $\Omega(b+\frac{\sqrt{b}}{\epsilon^2})$ for nonconvex online problems, where $b:= \min\{\frac{\sigma^2}{\epsilon^2}, n\}$. Then, we show that PAGE obtains the optimal convergence results $O(n+\frac{\sqrt{n}}{\epsilon^2})$ (finite-sum) and $O(b+\frac{\sqrt{b}}{\epsilon^2})$ (online) matching our lower bounds for both nonconvex finite-sum and online problems. Besides, we also show that for nonconvex functions satisfying the Polyak-Łojasiewicz (PL) condition, PAGE can automatically switch to a faster linear convergence rate $O(\cdot\log \frac{1}{\epsilon})$. Finally, we conduct several deep learning experiments (e.g., LeNet, VGG, ResNet) on real datasets in PyTorch showing that PAGE not only converges much faster than SGD in training but also achieves the higher test accuracy, validating the optimal theoretical results and confirming the practical superiority of PAGE.
AI assisted PHY in future wireless systems: Recent developments and challenges

Nowadays, the rapid development of artificial intelligence (AI) provides a fresh perspective in designing future wireless communication systems. Innumerable attempts exploiting AI methods have been carried out, which results in the state-of-the-art performance in many different areas of wireless communications. In this article, we present the most recent and insightful developments that demonstrate the potentials of AI techniques in different physical layer (PHY) components and applications including channel characterization, channel coding, intelligent signal identification, channel estimation, new PHY for random access in massive machine-type communication (mMTC), massive multiple-input multiple-output (MIMO) power control and PHY resource management. Open challenges and potential future directions are identified and discussed along this research line.
Image Dehazing Using Near-Infrared Information Based on Dark Channel Prior

Images collected in haze or other weather have problems of quality degradation, blurred details, and low recognition, which seriously affect the application and development of related image application fields. The dark channel prior method is the typical method of image dehazing, but it is not suitable for sky region. The wavelength of near-infrared is longer than that of visible light, which makes it more penetrating, less affected by the scattering of suspended particles in the air, and carries more detailed information. This paper uses near-infrared information to distinguish between sky region and non-sky region, improve the dark channel prior method, enhance the visibility of the image, and restore the contrast of the image. A large number of experiments have proved that our method has achieved satisfactory results in solving problems such as sky region and darker scene. This method has strong pertinence and stability for image dehazing, and the effect is relatively natural and efficient. Therefore, our method is better than the existing method of image dehazing.
Reconstructing Implicit Knowledge with Language Models

In this work we propose an approach for generating statements that explicate implicit knowledge connecting sentences in text.We make use of pre-trained language models which we refine by fine-tuning them on specifically prepared corpora that we enriched with implicit information, and by constraining them with relevant concepts and connecting commonsense knowledge paths.Manual and automatic evaluation of the generations shows that by refining language models as proposed, we can generate coherent and grammatically sound sentences that explicate implicit knowledge which connects sentence pairs in texts -on both in-domain and out-of-domain test data.
Appraising Mahallat Geothermal Region using thermal surveying data accompanied by the geological, geochemical and gravity analyses

Abstract Mahallat Geothermal Region, located in the central part of Iran, is known as one of the largest low-temperature geothermal fields. In this study, Mahallat geothermal resource has been evaluated based on integrated geological, geochemical and geophysical analyses. Gravity data revealed three major negative anomaly zones. Based on the geochemical analyses, quartz geothermometers are more reliable than others and confirmed that the reservoir is about 90 °C. Lithological properties of Jurassic layers and high sulphate content observed in geochemical data showed traces of the coal-rich layers on the hot fluids. Measured temperatures in 7 boreholes with the depths ranging from 50 to 100 m, have proposed that expected geothermal gradient will be about 81.5 °C/km. Among all drilled boreholes, the data coming from only one resulted in this almost reliable gradient. Other boreholes are clearly too shallow or affected by upflow or downflow of water along existing faults. Geological, geochemical, gravity and measurements of drilled boreholes suggested the existence of a shallow reservoir with an approximate temperature of 90 °C. Regarding gravity and observed faults, geothermal reservoir is elongated parallel to one of the main faults of the region with NE-SW strike.
Knowledge-driven Natural Language Understanding of English Text and its Applications

Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach for English text. By leveraging the VerbNet lexicon, we are able to map syntax tree of the text to its commonsense meaning represented using basic knowledge primitives. The general purpose knowledge represented from our approach can be used to build any reasoning based NLU system that can also provide justification. We applied this approach to construct two NLU applications that we present here: SQuARE (Semantic-based Question Answering and Reasoning Engine) and StaCACK (Stateful Conversational Agent using Commonsense Knowledge). Both these systems work by ``truly understanding'' the natural language text they process and both provide natural language explanations for their responses while maintaining high accuracy.
Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition

Zero-resource named entity recognition (NER) severely suffers from data scarcity in a specific domain or language.Most studies on zero-resource NER transfer knowledge from various data by fine-tuning on different auxiliary tasks.However, how to properly select training data and fine-tuning tasks is still an open problem.In this paper, we tackle the problem by transferring knowledge from three aspects, i.e., domain, language and task, and strengthening connections among them.Specifically, we propose four practical guidelines to guide knowledge transfer and task finetuning.Based on these guidelines, we design a target-oriented fine-tuning (TOF) framework to exploit various data from three aspects in a unified training manner.Experimental results on six benchmarks show that our method yields consistent improvements over baselines in both cross-domain and cross-lingual scenarios.Particularly, we achieve new state-of-theart performance on five benchmarks.
Factuality Assessment as Modal Dependency Parsing

As the sources of information that we consume everyday rapidly diversify, it is becoming increasingly important to develop NLP tools that help to evaluate the credibility of the information we receive.A critical step towards this goal is to determine the factuality of events in text.In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events.We crowdsource the first large-scale data set annotated with modal dependency structures that consists of 353 Covid-19 related news articles, 24,016 events, and 2,938 conceivers. 1 We also develop the first modal dependency parser that jointly extracts events, conceivers and constructs the modal dependency structure of a text.We evaluate the joint model against a pipeline model and demonstrate the advantage of the joint model in conceiver extraction and modal dependency structure construction when events and conceivers are automatically extracted.We believe the dataset and the models will be a valuable resource for a whole host of NLP applications such as fact checking and rumor detection.
Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation

This paper studies the unsupervised cross-domain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a generative cooperative network that consists of an energy-based model and a latent variable model. The use of generative cooperative network enables maximum likelihood learning of the domain model by MCMC teaching, where the energy-based model seeks to fit the data distribution of domain and distills its knowledge to the latent variable model via MCMC. Specifically, in the MCMC teaching process, the latent variable model parameterized by an encoder-decoder maps examples from the source domain to the target domain, while the energy-based model further refines the mapped results by Langevin revision such that the revised results match to the examples in the target domain in terms of the statistical properties, which are defined by the learned energy function. For the purpose of building up a correspondence between two unpaired domains, the proposed framework simultaneously learns a pair of cooperative networks with cycle consistency, accounting for a two-way translation between two domains, by alternating MCMC teaching. Experiments show that the proposed framework is useful for unsupervised image-to-image translation and unpaired image sequence translation.
Towards modeling of human skilling for electrical circuitry using augmented reality applications

Abstract Augmented reality (AR) is a unique, hands-on tool to deliver information. However, its educational value has been mainly demonstrated empirically so far. In this paper, we present a modeling approach to provide users with mastery of a skill, using AR learning content to implement an educational curriculum. We illustrate the potential of this approach by applying this to an important but pervasively misunderstood area of STEM learning, electrical circuitry. Unlike previous cognitive assessment models, we break down the area into microskills—the smallest segmentation of this knowledge—and concrete learning outcomes for each. This model empowers the user to perform a variety of tasks that are conducive to the acquisition of the skill. We also provide a classification of microskills and how to design them in an AR environment. Our results demonstrated that aligning the AR technology to specific learning objectives paves the way for high quality assessment, teaching, and learning.
Mixture Modeling for Identifying Subtypes in Disease Course Mapping

Disease modeling techniques summarize the possible trajectories of progression from multimodal and longitudinal data. These techniques often assume that individuals form a homogeneous cluster, thus ignoring possible disease subtypes within the population. We extend a non-linear mixed-effect model used for disease course mapping with a mixture framework. We jointly estimate model parameters and subtypes with a tempered version of a stochastic approximation of the Expectation Maximisation algorithm. We show that our model recovers the ground truth parameters from synthetic data, in contrast to the naive solution consisting in post hoc clustering of individual parameters from a one-class model. Applications to Alzheimer’s disease data allows the unsupervised identification of disease subtypes associated with distinct relationship between cognitive decline and progression of imaging and biological biomarkers.