Reviewerly/exHarmony huggingface dataset:
First steps in the development of a support application for easy-to-read adaptation

Abstract The application of the easy-to-read (E2R) methodology is one of the ways to achieve cognitive accessibility and specifically, it is a path that guarantees the right of access to information of people with reading comprehension difficulties and thus improves their daily life. This methodology includes a set of guidelines and recommendations whose goal is to present clear and easily understood documents. Such guidelines are used in the manual processes of (a) adapting existing documents and (b) producing new materials. These processes are very time and human-resource consuming, due to the need of involving E2R experts as well as people with cognitive disabilities. In order to alleviate such manual processes, we are currently investigating the development of methods, based on Artificial Intelligence (AI) techniques, to support the E2R adaptation of documents in a (semi)-automatic fashion. The main goal of this research is to help E2R experts in their daily tasks of (a) assessing a particular document with respect to the E2R guidelines and (b) transforming such a document according to the E2R methodology. In this paper we present our initial efforts toward the development of an AI-based application for supporting the E2R adaptation of documents. These efforts are the elicitation of E2R needs and informal requirements and the design of an application called FACILE.
Distraction detection of lectures in e-learning using machine learning based on human facial features and postural information

While e-learning lectures allow students to learn at their own pace, it is difficult to manage students' concentration, which prevents them from receiving valuable information from lectures. Therefore, we propose a method for detecting student distraction during e-learning lectures using machine learning, based on human face and posture information that can be collected using only an ordinary web camera. In this study, we first collected video data of the faces of subjects taking e-learning lectures and used the OpenFace and GAST-Net libraries to obtain face and posture information. Next, from the face and posture data, we extracted features such as the area of the eyes and mouth, the angle of the gaze direction, and the angle of the neck and shoulders. Finally, we used various machine learning models, such as random forest and XGBoost, to detect states of distraction during e-learning lectures. The results show that our binary classification models trained only on the individual's data achieved more than 90% recall.
Quality control procedure for 1-minute pyranometric measurements of global and shadowband-based diffuse solar irradiance

A novel automatic filtering methodology is proposed for 1-min GHI and DIF data using surface measurements obtained at five sites located in various climate zones of Argentina. The methodology contemplates DIF information obtained through the use of a shadowband or shading ring instead of the shading ball that requires a solar tracker. Three different flags are presented for suspicious or erroneous data identified by the filters of the methodology, which respond to the weight or strictness of the corresponding filter. The methodology uses filters established in the literature and proposes new filters capable of identifying slight misalignments of the shadowband as well as measurements below a minimum threshold empirically established for heavily overcast or thunderstorm conditions. A statistical analysis based on data obtained at a high-quality reference station and the modification of a pre-existing methodology is also presented. In general terms and at all sites, it is observed that the total amount of suspicious data increases significantly as the solar zenith angle increases. Overall, the methodology is able to identify and flag 15.0–44.6% erroneous or suspicious values, depending on site. The proposed methodology is applicable at any world station where the global and diffuse irradiance components are both measured.
COMMUTE: Communication-efficient transfer learning for multi-site risk prediction

We propose a communication-efficient transfer learning approach (COMMUTE) that effectively incorporates multi-site healthcare data for training a risk prediction model in a target population of interest, accounting for challenges including population heterogeneity and data sharing constraints across sites.We first train population-specific source models locally within each site. Using data from a given target population, COMMUTE learns a calibration term for each source model, which adjusts for potential data heterogeneity through flexible distance-based regularizations. In a centralized setting where multi-site data can be directly pooled, all data are combined to train the target model after calibration. When individual-level data are not shareable in some sites, COMMUTE requests only the locally trained models from these sites, with which, COMMUTE generates heterogeneity-adjusted synthetic data for training the target model. We evaluate COMMUTE via extensive simulation studies and an application to multi-site data from the electronic Medical Records and Genomics (eMERGE) Network to predict extreme obesity.Simulation studies show that COMMUTE outperforms methods without adjusting for population heterogeneity and methods trained in a single population over a broad spectrum of settings. Using eMERGE data, COMMUTE achieves an area under the receiver operating characteristic curve (AUC) around 0.80, which outperforms other benchmark methods with AUC ranging from 0.51 to 0.70.COMMUTE improves the risk prediction in a target population with limited samples and safeguards against negative transfer when some source populations are highly different from the target. In a federated setting, it is highly communication efficient as it only requires each site to share model parameter estimates once, and no iterative communication or higher-order terms are needed.
Linear regression analysis

Linear regression is one of the most broadly applied statistical techniques to investigate the linear relationship between one single dependent variable and one or more than one independent variable. It is also considered the foundation of statistical learning theory. In this entry, we illustrate the standard linear regression model, review regression coefficient estimation approaches, and discuss major assumptions underlying the linear regression model. In addition, we present three extensions of the linear regression model, including the generalized linear model (GLM), the hierarchical linear model (HLM), and multivariate regression model.
Intelligent tutoring systems: a history and an example of an ITS for science

In this entry, we define Intelligent Tutoring Systems (ITSs) and present a description of their core components. We outline a history of the development of ITSs with a focus on key issues that have driven change and innovation in ITSs from their inception to present day. We also present a brief case study on a specific ITS, Inq-ITS (Inquiry Intelligent Tutoring System; inqits.com), that is currently in use in 50 states in the US as well as many countries globally (12+) for the assessment of students' science competencies, and in turn, scaffolding students' science competencies.
Climate change: Prediction of solar radiation using advanced machine learning techniques

Changes in the quantity of solar energy reaching the Earth's surface have a straight influence on the climate as air temperatures have their origin in the absorption of radiant energy from the Sun. Solar radiation is the largest and purest source of renewable energy on earth with an extremely low carbon footprint required for power generation. Knowing expected solar radiation beforehand has a great utility not only from the economic perspective but also from the social perspective, especially for the countries like UAE which are part of the Sun Belt. The main objective of this paper is to explore three different univariate time series modeling techniques, namely: seasonal auto-regressive integrated moving average (SARIMA), K-nearest neighbors (KNN) and recursive neural network-long short term memory (RNN-LSTM) to predict the monthly average solar radiation per day in UAE. The best model was selected based on the root mean square error on training as well as testing datasets. The results of this study show that for solar radiation forecasting the machine learning techniques RNN-LSTM and KNN significantly outperform the classical time series modeling technique of SARIMA. While RNN-LSTM and KNN perform almost equally well, RNN-LSTM has a slight edge over KNN for both short-term and long-term forecasting. The findings of this paper throw light on how different univariate time series forecasting models perform in predicting solar radiation, which depends on multiple tangible and intangible atmospheric parameters which are difficult to capture or not readily available historically. Easy, quick, and accurate methods for short-term and long-term solar radiation prediction would help UAE (and other countries in Sun-Belt) to effectively manage the sporadic nature of solar radiation due to climate changes which sometimes compromise the functioning of electricity grids and in turn the electricity supply of photovoltaic plants.
An Improved Density Peak Clustering Algorithm for Multi-Density Data

Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is particularly important. However, for multi-density data, when one parameter cannot satisfy all data, clustering often cannot achieve good results. Moreover, the subjective selection of cluster centers through decision diagrams is often not very convincing, and there are also certain errors. In view of the above problems, in order to achieve better clustering of multi-density data, this paper improves the density peak clustering algorithm. Aiming at the selection of parameter dc, the K-nearest neighbor idea is used to sort the neighbor distance of each data, draw a line graph of the K-nearest neighbor distance, and find the global bifurcation point to divide the data with different densities. Aiming at the selection of cluster centers, the local density and distance of each data point in each data division is found, a γ map is drawn, the average value of the γ height difference is calculated, and through two screenings the largest discontinuity point is found to automatically determine the cluster center and the number of cluster centers. The divided datasets are clustered by the DPC algorithm, and then the clustering results are perfected and integrated by using the cluster fusion rules. Finally, a variety of experiments are designed from various perspectives on various artificial simulated datasets and UCI real datasets, which demonstrate the superiority of the F-DPC algorithm in terms of clustering effect, clustering quality, and number of samples.
The machines are learning: How artificial intelligence is already affecting journalism education

Recent developments in artificial intelligence (AI) pose considerable challenges to journalism educators. Sophisticated natural language processing systems have shown that the completion of academic assessment tasks does not necessarily require human-like artificial general intelligence. Already, AI can be deployed to assist with or independently complete assessment tasks for students or to provide an unacknowledged context for students completing assessment tasks. Educators teaching in journalism studies must rethink assessment, particularly as the journalism industry increasingly embraces AI technology.
Computational Models of Anaphora

Interpreting anaphoric references is a fundamental aspect of our language competence that has long attracted the attention of computational linguists. The appearance of ever-larger anaphorically annotated data sets covering more and more anaphoric phenomena in ever-greater detail has spurred the development of increasingly more sophisticated computational models; as a result, the most recent state-of-the-art neural models are able to achieve impressive performance by leveraging linguistic, lexical, discourse, and encyclopedic information. This article provides a thorough survey of anaphora resolution (coreference) throughout this development, reviewing the available data sets and covering both the preneural history of the field and—in more detail—current neural models, including research on less-studied aspects of anaphoric interpretation such as bridging reference resolution and discourse deixis interpretation.
Klein–Gordon equation for electrically charged particles with new energy-momentum operator

We address the Klein-Gordon equation for a spinless charged particle in the presence of an electromagnetic (EM) field, and focus on its known shortcoming, related to the existence of solutions with a negative probability density. We disclose a principal way to overcome this shortcoming, using our recent results obtained in the analysis of quantum phase effects for charges and dipoles, which prove the need to abandon the customary definition of the momentum operator for a charged particle in an EM field through its canonical momentum, and to adopt the more general definition of this operator through the sum of mechanical and electromagnetic momenta for the charged particle in an EM field. We show that the ap-plication of the new energy-momentum operator to the Klein-Gordon equation actually eliminates solutions with negative probability density. Some implications of the obtained results are discussed
Robust Adaptive Ensemble Adversary Reinforcement Learning

Reinforcement learning needs to learn policies through trial and error. The unstable policies in the early stage of training make it expensive (and time-consuming) to train directly in the real environment, which may cause disastrous consequences. The popular solution is to use the simulator to train the policy and deploy it in a real environment. However, the modeling error and external disturbance between the simulation and the real environment may fail the physical deployment, resulting in the sim2real transfer problem. In this letter, we propose a novel robust adversarial reinforcement learning framework, which uses the ensemble training of multi-adversarial agents that can adaptively adjust adversaries' strength to enhance RL policy's robustness. More specifically, we take the accumulative reward as feedback and construct a PID controller to adjust the adversary's output magnitude to perform the adversarial training well. Experiments in the simulated and the real environment show that our algorithm improves the generalization ability of the policy for the modeling error and the uncertain disturbance simultaneously, outperforming the next best prior methods across all domains. The algorithm was further proven to be effective in a sim2real transfer task through the load experiment of a real racing drone, and the tracking performance is better than the PID-based flight controller.
Chapter 10. COVID-19 conspiracy theories as affective discourse

This chapter analyzes online reactive comments to news reports on COVID-19 conspiracy theories, with a focus on hate speech on the origin(s) of the coronavirus. Largely mediated by feelings and ‘polarized emotionality’ (Boler and Davis 2018), COVID-origin conspiracy theories mobilize distinct affective publics online: commenters include supporters of China-origin theories and US-origin theories, and non-believers of conspiracy theories respectively. Informed by the theoretical framework of Critical Discourse Analysis (CDA), this study explores (i) the emotion-triggering discursive strategies surrounding COVID-origin conspiracy theories and (ii) the linguistic devices that realize these strategies. The analysis is based on 849 reactive comments to five Hong Kong-based COVID-origin news reports posted on Facebook. The study has found that such reactive comments to COVID-origin conspiracy theories share similar discourse strategies with those identified in the literature of discriminatory discourse and (covert) hate speech. Both believers and non-believers of conspiracy theories tap into the topoi of danger and threats, and strategically employ referentials, scare tactics, quasi-fallacious argumentations, and intensification/mitigation to legitimize their ideological stances. These strategies stir up negative emotions channeled through COVID-origin conspiracy theories. This chapter reveals the intricate relationship of conspiracy theories, affect, and hate speech in mediatized society.
R-classify: Extracting research papers’ relevant concepts from a controlled vocabulary

In the past few decades, we saw a proliferation of scientific articles available online.This data-rich environment offers several opportunities but also challenges, since it is problematic to explore these resources and identify all the relevant content.Hence, it is crucial that they are appropriately annotated with their relevant concepts so to increase their chance of being properly indexed and retrieved.In this paper, we present R-Classify, a web tool that assists users in identifying the most relevant concepts according to a large-scale ontology of research areas in the field of Computer Science.
A self-attentive model for tracing knowledge and engagement in parallel

Knowledge tracing refers to the task of modeling students’ evolving knowledge state according to their historical learning trajectories. Although many research has devoted to exploiting the latent information of question-concept bipartite graph, most of them need to split the graph into question-question and concept-concept graph to obtain questions’ and concepts’ representation respectively. This operation will cause information loss of original bipartite graph. To this end, an adaptive heterogeneous graph embedding module is proposed to utilize the latent information of graph adequately. Besides, many works ignore that students’ engagement could also impact students’ learning performance. Therefore, a student engagement encoder and a student knowledge state encoder are designed to capture students’ engagement and knowledge respectively. To be specific, the question-concept bipartite graph is input to graph embedding module directly to obtain each question’s and concept’s representation, the two encoders use these representation and students’ learning history to encode their engagement and knowledge state.
Resource-theoretic efficacy of the single copy of a two-qubit entangled state in a sequential network

How best one can recycle a given quantum resource, mitigating the various difficulties involved in its preparation and preservation, is of considerable importance for ensuring efficient applications in quantum technology. Here we demonstrate quantitatively the resource theoretic advantage of reusing a single copy of a two-qubit entangled state towards information processing. To this end, we consider a scenario of sequential entanglement detection of a given two-qubit state by multiple independent observers on each of the two spatially separated wings. In particular, we consider equal numbers of sequential observers on the two wings. We first determine the upper bound on the number of observers who can detect entanglement employing suitable entanglement witness operators. In terms of the parameters characterizing the entanglement consumed and the robustness of measurements, we then compare the above scenario with the corresponding scenario involving multiple pairs of entangled qubits shared among the two wings. This reveals a clear resource theoretic advantage of recycling a single copy of a two-qubit entangled state in the sequential network.
A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection

Detecting human-object interactions is essential for comprehensive understanding of visual scenes. In particular, spatial connections between humans and objects are important cues for reasoning interactions. To this end, we propose a skeleton-aware graph convolutional network for human-object interaction detection, named SGCN4HOI. Our network exploits the spatial connections between human keypoints and object keypoints to capture their fine-grained structural interactions via graph convolutions. It fuses such geometric features with visual features and spatial configuration features obtained from human-object pairs. Furthermore, to better preserve the object structural information and facilitate human-object interaction detection, we propose a novel skeleton-based object keypoints representation. The performance of SGCN4HOI is evaluated in the public benchmark V-COCO dataset. Experimental results show that the proposed approach outperforms the state-of-the-art pose-based models and achieves competitive performance against other models.
A Ranking Weight Based Roulette Wheel Selection Method for Comprehensive Learning Particle Swarm optimization

This paper proposes a ranking weight based roulette wheel selection (RWRWS) method for a promising particle swarm optimizer, called comprehensive learning particle swarm optimizer (CLPSO), to further improve its optimization performance. Specifically, the proposed RWRWS adopts a non-linear weight function to enhance the selection probabilities of promising personal best positions during the exemplar construction. In this way, it is expected that the construction efficiency of generating a promising leading exemplar for each particle could be improved and thus the optimization performance of CLPSO is expectedly elevated. To validate the feasibility and effectiveness of RWRWS, we carry out extensive experiments on a widely acknowledged benchmark problem set by comparing it with other three selection methods, namely the fitness-based roulette wheel selection (FRWS), the ranking based roulette wheel selection (RRWS), and the tournament selection (TS). Experimental results demonstrate that RWRWS helps CLPSO attain the best overall performance among the four selection methods.
Multimodal Detection of Unknown Objects on Roads for Autonomous Driving

Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems struggle with handling the unexpected. In order to safely operate on public roads, the identification of objects from unknown classes remains a crucial task. In this paper, we propose a novel pipeline to detect unknown objects. Instead of focusing on a single sensor modality, we make use of lidar and camera data by combining state-of-the art detection models in a sequential manner. We evaluate our approach on the Waymo Open Perception Dataset and point out current research gaps in anomaly detection.