Reviewerly/exHarmony huggingface dataset:
User VR Experience and Motivation Study in an Immersive 3D Geovisualization Environment Using a Game Engine for Landscape Design Teaching

Realistic 3D geovisualization is necessary to facilitate the perception of a landscape designer in relation to the environment, which is a determining factor in decision-making in landscape planning and management. In the field of landscape design teaching learning environments, game engines can offer an immersive 3D geovisualization mode through Virtual Reality technology, which, in addition, can be motivating for the student. Game engines allow designing the scenarios where videogames take place, but game engines can also be used for geovisualization tasks in landscape design teaching environments. In this article, we present the landscape workshop, using a Unity 3D game engine. Twenty-five architect students performed landscape design tasks and worked with an interactive 3D geovisualization low-immersive desktop screen environment. The perception of the 3D environment during geovisualization was analyzed through the Questionnaire on User eXperience in Immersive Virtual Environments, and the motivational factor with the Intrinsic Motivation Inventory. Results showed a high perception of the 3D environment during geovisualization in the nine subcategories (sense of presence, engagement, immersion, flow, usability, emotion, judgment, experience consequence, and technology adoption) analyzed. The game engine-based teaching approach carried out has been motivating for students, with values over 5 (in a 1–7 Likert scale) in the five subscales considered.
Solar Potential in Saudi Arabia for Southward-Inclined Flat-Plate Surfaces

The major objective of the present work is to investigate into the appropriate tilt angles of south-oriented solar panels in Saudi Arabia for maximum performance. This is done with the estimation of the annual energy sums received on surfaces with tilt angles in the range 15°–55° inclined to south at 82 locations covering all Saudi Arabia. The analysis shows that tilt angles of 20°, 25° and 30° towards south are the optimum ones depending on site. These optimum tilt angles define three distinct solar energy zones in Saudi Arabia. The variation of the energy sums in each energy zone on annual, seasonal and monthly basis is given; the analysis provides regression equations for the energy sums as function of time in each case. Furthermore, the spatial distribution of the annual global inclined solar energy in Saudi Arabia is shown in a solar map specially derived. The annual energy sums are found to vary between 1612 kWhm−2year−1 and 2977 kWhm−2year−1 across the country. Finally, the notion of a correction factor is introduced, defined, and employed. This factor can be used to correct energy values estimated by a reference ground albedo to those based on near-real ground albedo.
Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution

Liver tumor segmentation networks are generally based on U-shaped encoder-decoder network with 2D or 3D structure. However, 2D networks lose the inter-layer information of continuous slices and 3D networks might introduce unacceptable parameters for GPU memory. As a result, 2.5D networks were proposed to balance the memory consumption and 3D context. Different from the canonical 2.5D design, which utilizes a 2D network combined with RNN, we propose a new 2.5D design called UV-Net to encode the inter-layer information in the context of 3D convolution, and reconstruct the high-resolution results with 2D deconvolution. At the same time, the multi-scale convolution structure enables multi-scale feature extraction without extra computational cost, which effectively mines structured information, reduces information redundancy, strengthens independent features, and makes feature dimension sparse, to enhance network capacity and efficiency. Combined with the proposed preprocessing method of removing mean energy, UV-Net significantly outperforms the existing methods in liver tumor segmentation and especially improves the segmentation accuracy of small objects on the LiTS2017 dataset.
A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.
Optimized low-depth quantum circuits for molecular electronic structure using a separable-pair approximation

We present a classically tractable model that leads to optimized low-depth quantum circuits leveraging separable-pair approximations. The obtained circuits are well suited as a baseline circuit for emerging quantum hardware and can, in the long term, provide significantly improved initial states for quantum algorithms. The associated wave functions can be represented with linear memory requirement, which allows classical optimization of the circuits and naturally defines a minimum benchmark for quantum algorithms. In this work we employ directly determined pair-natural orbitals within a basis-set-free approach. This leads to accurate representation of the one- and many-body parts for weakly correlated systems and we explicitly illustrate how the model can be integrated into other quantum algorithms for stronger correlated systems.
A Hybridization of Dragonfly Algorithm Optimization and Angle Modulation Mechanism for 0-1 Knapsack Problems

The dragonfly algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous functions and engineering problems. To make this algorithm work in the binary space, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems, and uses DA to optimize the coefficients of the trigonometric function. Further, to improve the algorithm stability and convergence speed, an improved AMDA, called IAMDA, is proposed by adding one more coefficient to adjust the vertical displacement of the cosine part of the original generating function. To test the performance of IAMDA and AMDA, 12 zero-one knapsack problems are considered along with 13 classic benchmark functions. Experimental results prove that IAMDA has a superior convergence speed and solution quality as compared to other algorithms.
A Comparison of Soft Computing Methods for the Prediction of Wave Height Parameters

In the previous studies on the prediction of wave height parameters, only the significant wave height has been considered as the unknown parameter to be predicted. However, the other wave height parameters, which may be required for the design of coastal structures depending on their importance level, have been neglected. Therefore, in this study, novel soft computing methods were used to predict all wave height parameters required for the design of coastal structures. To this end, wave data were derived from a buoy located in Southwest Black Sea Coast. Then, Multi-layer Perceptron Neural Network (MLPNN) and Adaptive-Neuro Fuzzy Inference System (ANFIS) models were developed to predict wave height parameters. Various input combinations were selected to create seven different sub-models. These sub-models were applied using developed MLPNN and ANFIS models. Accuracy of sub-models were evaluated for each wave height parameters in terms of performance evaluation criteria. The results showed that the wave height parameters predicted by the MLPNN and ANFIS methods are similar and both methods yield results acceptable for design purposes. However, for maximum wave height, Hmax, ANFIS sub-model yields slightly better results.
Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms

Abstract Quantum chemistry is a key application area for noisy‐intermediate scale quantum (NISQ) devices, and therefore serves as an important benchmark for current and future quantum computer performance. Previous benchmarks in this field have focused on variational methods for computing ground and excited states of various molecules, including a benchmarking suite focused on the performance of computing ground states for alkali‐hydrides under an array of error mitigation methods. State‐of‐the‐art methods to reach chemical accuracy in hybrid quantum‐classical electronic structure calculations of alkali hydride molecules on NISQ devices from IBM are outlined here. It is demonstrated how to extend the reach of variational eigensolvers with symmetry preserving Ansätze. Next, it is outlined how to use quantum imaginary time evolution and Lanczos as a complementary method to variational techniques, highlighting the advantages of each approach. Finally, a new error mitigation method is demonstrated which uses systematic error cancellation via hidden inverse gate constructions, improving the performance of typical variational algorithms. These results show that electronic structure calculations have advanced rapidly, to routine chemical accuracy for simple molecules, from their inception on quantum computers a few short years ago, and they point to further rapid progress to larger molecules as the power of NISQ devices grows.
Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video

We propose a self-supervised visual learning method by predicting the variable playback speeds of a video. Without semantic labels, we learn the spatio-temporal visual representation of the video by leveraging the variations in the visual appearance according to different playback speeds under the assumption of temporal coherence. To learn the spatio-temporal visual variations in the entire video, we have not only predicted a single playback speed but also generated clips of various playback speeds and directions with randomized starting points. Hence the visual representation can be successfully learned from the meta information (playback speeds and directions) of the video. We also propose a new layer-dependable temporal group normalization method that can be applied to 3D convolutional networks to improve the representation learning performance where we divide the temporal features into several groups and normalize each one using the different corresponding parameters. We validate the effectiveness of our method by fine-tuning it to the action recognition and video retrieval tasks on UCF-101 and HMDB-51. All the source code is released in https://github.com/hyeon-jo/PSPNet.
Gaussian Process Regression for foreground removal in H <scp>i</scp> Intensity Mapping experiments

We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift HI intensity mapping, and present an open-source Python toolkit for doing so. We use MeerKAT and SKA1-MID-like simulations of 21cm foregrounds (including polarisation leakage), HI cosmological signal and instrumental noise. We find that it is possible to use GPR as a foreground removal technique in this context, and that it is better suited in some cases to recover the HI power spectrum than Principal Component Analysis (PCA), especially on small scales. GPR is especially good at recovering the radial power spectrum, outperforming PCA when considering the full bandwidth of our data. Both methods are worse at recovering the transverse power spectrum, since they rely on frequency-only covariance information. When halving our data along frequency, we find that GPR performs better in the low frequency range, where foregrounds are brighter. It performs worse than PCA when frequency channels are missing, to emulate RFI flagging. We conclude that GPR is an excellent foreground removal option for the case of single-dish, low redshift HI intensity mapping in the absence of missing frequency channels. Our Python toolkit gpr4im and the data used in this analysis are publicly available on GitHub.
Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers

The ease of access to pre-trained transformers has enabled developers to leverage large-scale language models to build exciting applications for their users.While such pre-trained models offer convenient starting points for researchers and developers, there is little consideration for the societal biases captured within these model risking perpetuation of racial, gender, and other harmful biases when these models are deployed at scale.In this paper, we investigate gender and racial bias across ubiquitous pre-trained language models, including GPT-2, XLNet, BERT, RoBERTa, ALBERT and Dis-tilBERT.We evaluate bias within pre-trained transformers using three metrics: WEAT, sequence likelihood, and pronoun ranking.We conclude with an experiment demonstrating the ineffectiveness of word-embedding techniques, such as WEAT, signaling the need for more robust bias testing in transformers.
Exploring the Feedback Quality of an Automated Writing Evaluation System Pigai

The study made an exploration of the feedback quality of an Automated Writing Evaluation system (AWE) Pigai, which has been widely applied in English teaching and learning in China. The study not only focused on the diagnostic precision of the feedback but also investigated the students’ perceptions of the feedback use in their daily writing practices. Taking 104 university students’ final exam essays as the research materials, the paired sample t-test was conducted to compare the mean number of errors identified by Pigai and professional teachers. It was found that Pigai feedback could not so well diagnose the essays as the human feedback given by the experienced teachers, however, it was quite competent in identifying lexical errors. The analysis of students’ perceptions indicated that most students thought Pigai feedback was multi-functional, but it was inadequate in identifying the collocation errors and giving suggestions in syntactic use. The implications and limitations of the study were discussed at the end of the paper.
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance.
Digital Transformation in the Restaurant Industry: Current Developments and Implications

Restaurants are an important area of the hospitality industry.This industry sector has not only experienced severe consequences of the recent lockdowns, but it has also seen the rise of digital technologies.As known from other industries, the digital transformation impacts products, processes and business models alike.Starting from the classical distinction of a restaurant's front-and back-of-thehouse processes, this paper presents an overview on current developments in the restaurant industry and based on an analysis of current digital services, it derives some implications for future directions.Among the observations are that restaurants need to cover more touch points, provide more individualized offerings and strive for more automation as well as integration of their systems.
Linear embedding of nonlinear dynamical systems and prospects for efficient quantum algorithms

The simulation of large nonlinear dynamical systems, including systems generated by discretization of hyperbolic partial differential equations, can be computationally demanding. Such systems are important in both fluid and kinetic computational plasma physics. This motivates exploring whether a future error-corrected quantum computer could perform these simulations more efficiently than any classical computer. We describe a method for mapping any finite nonlinear dynamical system to an infinite linear dynamical system (embedding) and detail three specific cases of this method that correspond to previously-studied mappings. Then we explore an approach for approximating the resulting infinite linear system with finite linear systems (truncation). Using a number of qubits only logarithmic in the number of variables of the nonlinear system, a quantum computer could simulate truncated systems to approximate output quantities if the nonlinearity is sufficiently weak. Other aspects of the computational efficiency of the three detailed embedding strategies are also discussed.
Approximate reasoning with fuzzy rule interpolation: background and recent advances

Abstract Approximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with an unmatched observation. This differs from classical rule-based inference that requires direct pattern matching between observations and the given rules. FRI techniques have been continuously investigated for decades, resulting in various types of approach. Traditionally, it is typically assumed that all antecedent attributes in the rules are of equal significance in deriving the consequents. Recent studies have shown significant interest in developing enhanced FRI mechanisms where the rule antecedent attributes are associated with relative weights, signifying their different importance levels in influencing the generation of the conclusion, thereby improving the interpolation performance. This survey presents a systematic review of both traditional and recently developed FRI methodologies, categorised accordingly into two major groups: FRI with non-weighted rules and FRI with weighted rules. It introduces, and analyses, a range of commonly used representatives chosen from each of the two categories, offering a comprehensive tutorial for this important soft computing approach to rule-based inference. A comparative analysis of different FRI techniques is provided both within each category and between the two, highlighting the main strengths and limitations while applying such FRI mechanisms to different problems. Furthermore, commonly adopted criteria for FRI algorithm evaluation are outlined, and recent developments on weighted FRI methods are presented in a unified pseudo-code form, easing their understanding and facilitating their comparisons.
Prompting Contrastive Explanations for Commonsense Reasoning Tasks

Many commonsense reasoning NLP tasks involve choosing between one or more possible answers to a question or prompt based on knowledge that is often implicit.Large pretrained language models (PLMs) can achieve near-human performance on such tasks, while providing little human-interpretable evidence of the underlying reasoning they use.In this work, we show how to use these same models to generate such evidence: inspired by the contrastive nature of human explanations, we use PLMs to complete explanation prompts which contrast alternatives according to the key attribute(s) required to justify the correct answer (for example, peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts peanuts are usually salty while raisins raisins raisins raisins raisins raisins raisins raisins raisins raisins raisins raisins raisins raisins raisins raisins raisins are sweet).Conditioning model decisions on these explanations improves performance on two commonsense reasoning benchmarks, as compared to previous non-contrastive alternatives.These explanations are also judged by humans to be more relevant for solving the task, and facilitate a novel method to evaluate explanation faithfulness.
Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?

Do state-of-the-art natural language understanding models care about word order?Not always!We found 75% to 90% of the correct predictions of BERT-based classifiers, trained on many GLUE tasks, remain constant after input words are randomly shuffled.Although BERT embeddings are famously contextual, the contribution of each individual word to classification is almost unchanged even after its surrounding words are shuffled.BERTbased models exploit superficial cues (e.g. the sentiment of keywords in sentiment analysis; or the word-wise similarity between sequencepair inputs in natural language inference) to make correct decisions when tokens are randomly shuffled.Encouraging models to capture word order information improves the performance on most GLUE tasks and SQuAD 2.0.Our work suggests that many GLUE tasks are not challenging machines to understand the meaning of a sentence.
Shortformer: Better Language Modeling using Shorter Inputs

Increasing the input length has been a driver of progress in language modeling with transformers.We identify conditions where shorter inputs are not harmful, and achieve perplexity and efficiency improvements through two new methods that decrease input length.First, we show that initially training a model on short subsequences before moving on to longer ones both reduces overall training time and, surprisingly, substantially improves perplexity.Second, we show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens when generating sequences that exceed the maximal length the transformer can handle at once.Existing methods require computationally expensive relative position embeddings; we introduce a simple alternative of adding absolute position embeddings to queries and keys instead of to word embeddings, which efficiently produces superior results.We show that these recurrent models also benefit from short input lengths.Combining these techniques speeds up training by a factor of 1.65, reduces memory usage, and substantially improves perplexity on WikiText-103, without adding any parameters. 1