Reviewerly/exHarmony huggingface dataset:
Development of an Instrument to Measure Conceptualizations and Competencies About Conversational Agents on the Example of Smart Speakers

The concept of digital literacy has been introduced as a new cultural technique, which is regarded as essential for successful participation in a (future) digitized world. Regarding the increasing importance of AI, literacy concepts need to be extended to account for AI-related specifics. The easy handling of the systems results in increased usage, contrasting limited conceptualizations (e.g., imagination of future importance) and competencies (e.g., knowledge about functional principles). In reference to voice-based conversational agents as a concrete application of AI, the present paper aims for the development of a measurement to assess the conceptualizations and competencies about conversational agents. In a first step, a theoretical framework of “AI literacy” is transferred to the context of conversational agent literacy. Second, the “conversational agent literacy scale” (short CALS) is developed, constituting the first attempt to measure interindividual differences in the “(il) literate” usage of conversational agents. 29 items were derived, of which 170 participants answered. An explanatory factor analysis identified five factors leading to five subscales to assess CAL: storage and transfer of the smart speaker’s data input; smart speaker’s functional principles; smart speaker’s intelligent functions, learning abilities; smart speaker’s reach and potential; smart speaker’s technological (surrounding) infrastructure. Preliminary insights into construct validity and reliability of CALS showed satisfying results. Third, using the newly developed instrument, a student sample’s CAL was assessed, revealing intermediated values. Remarkably, owning a smart speaker did not lead to higher CAL scores, confirming our basic assumption that usage of systems does not guarantee enlightened conceptualizations and competencies. In sum, the paper contributes to the first insights into the operationalization and understanding of CAL as a specific subdomain of AI-related competencies.
DynEHR: Dynamic adaptation of models with data heterogeneity in electronic health records

Electronic health records (EHRs) provide an abundance of data for clinical outcomes modeling. The prevalence of EHR data has enabled a number of studies using a variety of machine learning algorithms to predict potential adverse events. However, these studies do not account for the heterogeneity present in EHR data, for example, various lengths of stay and frequencies of vitals. Therefore, studies limit the types of features extracted or the domain considered to provide a more homogeneous training set to machine learning models. The heterogeneity in this data represents important risk differences in each patient. In this work, we examine such data in an intensive care unit (ICU) setting, where the length of stay and the frequency of data gathered may vary significantly based upon the severity of patient condition. Therefore, it is unreasonable to use the same model for patients first entering the ICU versus those that have been there for above average lengths of stay. Developing multiple individual models to account for the various EHR data may be tedious and not account for rare cases well. We address this challenge by developing a dynamic model, based upon meta- learning, to adapt to data heterogeneity and generate predictions of various outcomes across the different lengths of data. We compare this technique against a set of benchmarks on a publicly- available ICU dataset (MIMIC-III) and demonstrate improved model performance by accounting for data heterogeneity.
Disaster Tweet Classification Based On Geospatial Data Using the BERT-MLP Method

as a popular social media in the world and even in Indonesia, Twitter has a variety of popular topics making these topics trending, including the topic of natural disasters that have occurred in Indonesia. The DKI Jakarta flood disaster in early 2020 made a big scene on trending twitter topics. This study aims to classify these tweets into "flooded" and "not flooded" predictions with the tweets and geospatial features. The model proposed for classifying is BERT-MLP. Bidirectional Encoder from Transformers (BERT) is used in the pre-trained model to classify these tweets and Multi Layer Perceptron (MLP) is used to classify geospatial features. The scenario designed for the model focuses on the preprocessing of tweets as follows without stopword removal, without stemming, with both, and without both. Once classified, the tweet will be visualized into a two-dimensional interactive map. The best scenario results have an accuracy of 82% in scenarios without stemming and with stopword removal. This is due to the stemming process eliminates some of the features in tweets around 6%. This study also shows the relationship between the influence of negative context tweets on the "not flooded" class with an orientation of 65% of the total data. However, defining manual stopwords can affect because stopword removal will not delete words that still have context related features to the topic.
CSANet for Video Semantic Segmentation With Inter-Frame Mutual Learning

Video semantic segmentation aims atgenerating temporal consistent segmentation results and is still a very challenging task in the deep learning era. In this work, we improve prior approaches from two aspects. On the network architecture level, we present the cross and self-attention network (CSANet). As opposed to prior methods, CSANet not only propagates temporal features from adjacent frames, but is also designed to aggregate spatial context within the current frame, which is shown to effectively improve the consistency and robustness of the extracted deep features. On the loss function level, we further propose the inter-frame mutual learning strategy which ensures the cross-attention module to focus on semantically correlated context regions, allowing the segmentation results at different frames to be collaboratively improved. By combining the above two novel designs, we show that our proposed method is able to deliver state-of-the-art performance on the Cityscapes and CamVid benchmarks.
Optimized Power Control for Over-the-Air Federated Edge Learning

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient solution for privacy-preserving distributed learning over wireless networks. Air-FEEL allows "one-shot" over-the-air aggregation of gradient/model-updates by exploiting the waveform superposition property of wireless channels, and thus promises an extremely low aggregation latency that is independent of the network size. However, such communication efficiency may come at a cost of learning performance degradation due to the aggregation error caused by the non-uniform channel fading over devices and noise perturbation. Prior work adopted channel inversion power control (or its variants) to reduce the aggregation error by aligning the channel gains, which, however, could be highly suboptimal in deep fading scenarios due to the noise amplification. To overcome this issue, we investigate the power control optimization for enhancing the learning performance of Air-FEEL. Towards this end, we first analyze the convergence behavior of the Air-FEEL by deriving the optimality gap of the loss-function under any given power control policy. Then we optimize the power control to minimize the optimality gap for accelerating convergence, subject to a set of average and maximum power constraints at edge devices. The problem is generally non-convex and challenging to solve due to the coupling of power control variables over different devices and iterations. To tackle this challenge, we develop an efficient algorithm by jointly exploiting the successive convex approximation (SCA) and trust region methods. Numerical results show that the optimized power control policy achieves significantly faster convergence than the benchmark policies such as channel inversion and uniform power transmission.
Duperation: Deliberate Lying in Postdigital, Postmodern Political Rhetoric

In this chapter I will begin with some definitions of keywords such as 'dupery', postdigital, post-truth, and postmodern. I will then proceed to consider how political 'duperation' manifests itself, considering, for example, how dupery has borrowed the techniques of advertising; how dupers, gravitating towards a medium which suits their purposes – social media, have reduced and degraded language; and, consequently, the very process of democracy. I will consider also the paradoxes and duplicities of the various tropes deployed by dupers including the authentic, anti-elite avatar, the resenting and revengeful avatar, and the surveilling avatar. I will examine how the emotional and the visceral have become the rhetorical levers in duperation. The chapter will conclude with some recommendations about how we might begin to resist duperation.
Optimal Transport for UAV D2D Distributed Learning: Example using Federated Learning

Federated Learning (FL) is a novel distributed learning paradigm in which local learning models are simultaneously trained using the stored data on multiple devices, then ultimately aggregated into a global model. A promising use case of FL is the training of a global model using the data collected by unmanned aerial vehicles (UAVs) during their flight, which is invaluable in scenarios in which an infrastructure cannot be accessed (e.g., disaster). However, this is challenging as limited resources are to be distributed between flight time, sensing, processing, and communication. In this paper, we address the resource problem for a set of heterogeneous UAVs with different computation and communication capabilities from distributed point of view. We propose the usage of Device-to-Device (D2D) communication to fairly distribute the data so-far collected by UAVs with different capabilities by posing it as an optimal transport problem. Our contribution is two-fold: (1) We obtain the fairest distribution of data given the UAVs’ computational capabilities such that global learning time is minimal; (2) We devise a scheme using Optimal Transport (OT) to achieve such a fair distribution between UAVs. The performance of the proposed techniques is demonstrated in an FL setting with different UAV topologies with the FL training done using the MNIST dataset.
High Energy Physics Calorimeter Detector Simulation Using Generative Adversarial Networks With Domain Related Constraints

Generative Adversarial Networks (GANs) have gained notoriety by generating highly realistic images.The present work explores GAN for simulating High Energy Physics detectors, interpreting detector output as three-dimensional images.The demands and requirements of a scientific simulation are quite stringent, as compared to the domain of visual images.Image characteristics such as pixel intensity and sparsity, for example, have very different distributions.Moreover, detector simulation requires conditioning on physics inputs, and domain knowledge becomes essential.We, therefore, adjust the pre-processing and incorporate physics-based constraints in the loss function.We also introduce a multi-step training process based on transfer learning by breaking up the task complexity.Validation of the results primarily consists of a detailed comparison to full Monte Carlo in terms of several physics quantities where a high level of agreement is found (ranging from a few percent up to 10% across a large particle energy range).In addition, we assess the performance by physics unrelated metrics, thereby proving further the variability and pertinence through diverse standpoints.We have demonstrated that an image generation technique from vision can successfully simulate highly complex physics processes while achieving a speedup of more than three orders of magnitude in comparison to the standard Monte Carlo.
A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming

Neuro-encoded expression programming (NEEP) that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. Genetic programming with linear representation uses nature-inspired operators (e.g., crossover, mutation) to tune expressions and finally search out the best explicit function to simulate data. The encoding mechanism is essential for genetic programmings to find a desirable solution efficiently. However, the linear representation methods manipulate the expression tree in discrete solution space, where a small change of the input can cause a large change of the output. The unsmooth landscapes destroy the local information and make difficulty in searching. The neuro-encoded expression programming constructs the gene string with recurrent neural network (RNN) and the weights of the network are optimized by powerful continuous evolutionary algorithms. The neural network mappings smoothen the sharp fitness landscape and provide rich neighborhood information to find the best expression. The experiments indicate that the novel approach improves training efficiency and reduces test errors on several well-known symbolic regression problems.
Towards Explainable Group Formation by Knowledge Map based Genetic Algorithm

In recent years, machine learning of increasing complexity is being applied to problems in education. However, there is an increasing call for transparency and understanding into how the results of complex models are derived, leading to explainable AI gaining attention. The application of machine learning to automated group formation for collaborative work from learning system logs and other data has been progressing. Building on previous research in this field, we propose a group formation method that is based on a combination of course knowledge structures, reading behavior, and assessment analysis to create optimal heterogenous and homogeneous working groups using a genetic algorithm. The characteristics of each group are presented for explanation as a visualized knowledge map showing the strengths and weaknesses of each group, and are in the structure form of curriculum. We also present a case study of applying the method to junior high school mathematics log data, and provide explanation in a visualized form of standardized curriculum of group characteristics that are often referenced for learning design by teachers.
Beyond site suitability: Investigating temporal variability for utility-scale solar-PV using soft computing techniques

Site suitability is highly essential for utility-scale solar-PV exploration, however, beyond this phase, the need to investigate solar resource variability in suitable site is very vital to strategic planning and grid-integration. This study investigates solar resource variability in a suitable site obtained from geographical information system (GIS)-based site suitability analysis using soft computing techniques. The Western Cape Province, South Africa was used as a case study and climatological, topographic, and location factors were considered for the site suitability analysis for solar-PV in the province. Five soft computing techniques were applied on a candidate site chosen from the final suitability map. These are: gradient descent with adaptive learning rate neural network (GDALRNN), resilient backpropagation neural network (RBNN), adaptive neurofuzzy inference system (ANFIS), genetic algorithm-based ANFIS (GA-ANFIS) and particle swarm optimization based ANFIS (PSO-ANFIS). Each model was trained and tested with 70% and 30% of the data obtained from the representative site respectively. The GDALRNN model outperforms all other models with a root mean square error (RMSE) of 0.0316, coefficient of variation of RMSE (CV(RMSE)) of 4.7590%, relative mean bias error of −0.2951%, mean absolute percentage error of 4.0998, robust coefficient of variation of 0.0403, skill score of 10.7345 and computational time of 9.71 s. Finally, the possibility of integrating resource variability investigation into site suitability and the relevance of simple intelligent models over complex hybrid ones in solar resource variability modelling were established.
Efficient Human Pose Estimation by Learning Deeply Aggregated Representations

In this paper, we propose an efficient human pose estimation network (DANet) by learning deeply aggregated representations. Most existing models explore multi-scale infonnation mainly from features with different spatial sizes. Powerful multi-scale representations usually rely on the cascaded pyramid framework. This framework largely boosts the performance but in the meanwhile makes networks very deep and complex. Instead, we focus on exploiting multi-scale information from layers with different receptive-field sizes and then making full of use this infonnation by improving the fusion method. Specifically, we propose an orthogonal attention block (OAB) and a second-order fusion unit (SFU). The OAB learns multi-scale infonnation from different layers and enhances them by encouraging them to be diverse. The SFU adaptively selects and fuses diverse multi-scale infonnation and suppress the redundant ones. With the help of OAB and SFU, our networks could achieve comparable or even better accuracy with much smaller model complexity. Specifically, our DANet-72 achieves 71.0 in AP score on COCO val2017 with only 1.0G FLOPS. Its speed on a CPU platfonn achieves 58 Persons-Per-Second (PPS).
Attending From Foresight: A Novel Attention Mechanism for Neural Machine Translation

Machines translation (MT) is an essential task in natural language processing or even in artificial intelligence. Statistical machine translation has been the dominant approach to MT for decades, but recently neural machine translation achieves increasing interest because of its appealing model architecture and impressive translation performance. In neural machine translation, an attention model is used to identify the aligned source words for the next target word, i.e., target foresight word, to select translation context. However, it does not make use of any information about this target foresight word at all. Previous work proposed an approach to improve the attention model by explicitly accessing this target foresight word and demonstrating substantial alignment tasks. However, this approach cannot be applied in machine translation tasks where the target foresight word is unavailable. This paper proposes several novel enhanced attention models by introducing hidden information (such as part-of-speech) of the target foresight word for the translation task. We incorporate the novel enhanced attention employing hidden information about the target foresight word into both recurrent and self-attention-based neural translation models and theoretically justify that such hidden information can make translation prediction easier. Empirical experiments on four datasets further verify that the proposed attention models deliver significant improvements in translation quality.
Out-of-Core Surface Reconstruction via Global TGV Minimization

We present an out-of-core variational approach for surface reconstruction from a set of aligned depth maps. Input depth maps are supposed to be reconstructed from regular photos or/and can be a representation of terrestrial LIDAR point clouds. Our approach is based on surface reconstruction via total generalized variation minimization (TGV) because of its strong visibility-based noise-filtering properties and GPU-friendliness. Our main contribution is an out-of-core OpenCL-accelerated adaptation of this numerical algorithm which can handle arbitrarily large real-world scenes with scale diversity.
Engineering Lab in Immersive VR—An Embodied Approach to Training Wafer Preparation

This paper reports an immersive virtual reality lab (iVRLab) training environment that offers college students an immersive and embodied experience in engineering lab work. The iVRLab provides a simple and safe environment for students to learn the complex lab operations to process and prepare silicon wafers. It features highly embodied interactions congruent to the actual body movements to manipulate the lab devices and materials in the physical world. Fourteen college students participate the lab training and the study results reveal positive learning effects, confidence levels of accomplishment, and embodied experiences. Students’ cognitive load is also measured, and its relationship with the embodied experiences is examined and discussed. The study offers a reference for peer researchers and practitioners for the design and implementation of immersive VR systems for engineering lab training. It also sketches a complementary approach during the time like the pandemic to practicing authentic lab work without having to be in a real classroom or laboratory.
Natural language processing as a technique for conducting text‐based research

Abstract Research in discourse processing has provided us with a strong foundation for understanding the characteristics of text and discourse, as well as their influence on our processing and representation of texts. However, recent advances in computational techniques have allowed researchers to examine discourse processes in new ways. The purpose of the current paper is to build on prior work in this domain and describe how new methodologies that consider the multi‐dimensional nature of texts can serve as a complement to the existing literature. We focus on natural language processing (NLP) methodologies, in which computers calculate information about the linguistic and semantic properties of language data. We first provide a context for the origins of computational discourse analysis through the integration of research across computer science and psychology. We then provide an overview of different NLP methodologies and describe prior work that has leveraged these techniques to advance theoretical perspectives of discourse comprehension and production. Finally, we propose new areas of research that integrate these advances with traditional research methodologies in the field.
Personalized scientific and technological literature resources recommendation based on deep learning

To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers’ titles and abstracts. Secondly, an academic literature topic model is built using term frequency–inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers’ preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress.