Reviewerly/exHarmony huggingface dataset:
Bringing in the technological, ethical, educational and social-structural for a new education data governance

The need for a comprehensive education data governance – the regulation of who collects what data, how it is used and why – continues to grow. Technologically, data can be collected by third parties, rendering schools unable to control their use. Legal frameworks partially achieve data governance as businesses continue to exploit existing loopholes. Education data use practices undergo no prior ethical reviews. And at a personal level, students have no agency over these practices. In other words, there is no coherent and meaningful oversight and data governance framework that ensures accountable data use in an increasingly digitalised education sector. In this article, I contextualise the issues arising from education data transactions in one school district in the United States. The case study helps to contextualise what needs governance, who may access education data and how the district governs data use and transactions, emphasising the need for a coherent education data governance but also the limitations of such isolated efforts.
Steganography in IoT: Information Hiding with APDS-9960 Proximity and Gestures Sensor

This article describes a steganographic system for IoT based on an APDS-9960 gesture sensor. The sensor is used in two modes: as a trigger or data input. In trigger mode, gestures control when to start and finish the embedding process; then, the data come from an external source or are pre-existing. In data input mode, the data to embed come directly from the sensor that may detect gestures or RGB color. The secrets are embedded in time-lapse photographs, which are later converted to videos. Selected hardware and steganographic methods allowed for smooth operation in the IoT environment. The system may cooperate with a digital camera and other sensors.
Automatic detection of geological lineaments in central Turkey based on test image analysis using satellite data

The Hamurcu and Başdere study areas, part of the Central Anatolian Volcanic Provence (CAVP), are associated with metalliferous ore deposits. The assumption that hydrothermal alterations might be related to faults and fractures makes mapping the lineaments around the study areas vital. Accordingly, geological lineaments in the vicinity of the studied areas in Central Anatolia (Turkey) were delineated using Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR) data. We adopted a new methodology for determining the optimal values of six parameters used in a fully automated extraction of lineaments. The lineaments identified in the region including the study areas strike mainly the NW-SE direction in the PALSAR data. Moreover, hydrothermal alterations around the towns of Hamurcu and Başdere are directly related to the NW-SE oriented faults, indicating that the lineations in the region might be carefully scrutinized in the exploration of mineralization zones. As a result, it was demonstrated in this study that the most appropriate parameter values specified for automatic lineament extraction successfully map geological structures around both study areas and can confidently be employed in different geographical locations.
SA-DPNet: Structure-aware dual pyramid network for salient object detection

Salient object detection aims at highlighting the most visually distinctive objects in the scene. Previous deep learning based works mainly focus on designing different integration strategies of multi-level features to improve the quality of prediction. However, due to the negligence of spatial structure coherence in predicted saliency maps, they fail to produce satisfactory results in complex scenarios. In this work, we present a structure-aware dual pyramid network (SA-DPNet) for salient object detection. By explicitly formulating spatial location information and spatial covariance features into the self-attention mechanism, a structure-aware spatial non-local block is proposed in SA-DPNet to learn the spatial-sensitive global context. With the proposed edge loss and adversarial loss, the edge structure context and patch-based global structure context are introduced to refine the structural coherence of the predicted results. Comprehensive experimental results on six RGB saliency benchmark datasets and three RGB-D saliency benchmark datasets demonstrate the superiority of proposed SA-DPNet over other state-of-the-art methods, both quantitatively and visually.
Visual saliency detection via a recurrent residual convolutional neural network based on densely aggregated features

Current visual saliency detection algorithms based on deep learning suffer from reduced detection effect in complex scenes owing to ineffective feature expression and poor generalization. The present study addresses this issue by proposing a recurrent residual network based on dense aggregated features. Firstly, different levels of dense convolutional features are extracted from the ResNeXt101 network. Then, the features of all layers are aggregated under an Atrous spatial pyramid pooling operation, which makes comprehensive use of all possible saliency cues. Finally, the residuals are learned recurrently under a deep supervision mechanism to achieve continuous optimization of the saliency map. Application of the proposed algorithm to publicly available datasets demonstrates that the dense aggregation of features not only enhances the aggregation of effective information within a single layer, but also enhances external interactions between information at different feature levels. As a result, the proposed algorithm provides better detection ability than that of current state-of-the-art algorithms.
Quantum color image encryption based on a novel 3D chaotic system

In order to improve the security of quantum color images, this study proposes a novel algorithm for quantum color image encryption based on three-dimensional chaotic systems. The encrypted quantum color image is represented by the novel quantum image representation for color digital image model. The original image is first perturbed by the Arnold transform and then the RGB channel is perturbed separately from the chaotic sequence generated by the new three-dimensional chaotic system by the xor operation. Evaluation metrics such as histogram, information entropy, and correlation of neighboring pixels of the image are analyzed using MATLAB. The simulation results show that the pixel values of the encrypted image are uniformly distributed and the algorithm enhances the security of quantum color images. The specific quantum circuit diagram of the encryption algorithm is given in the paper. The superposition and entanglement properties of quantum physics greatly improve the efficiency of complex image processing algorithms, and the overall complexity of the circuit is O(n), which is efficient and implementable.
Learning a discriminative SPD manifold neural network for image set classification

Performing pattern analysis over the symmetric positive definite (SPD) manifold requires specific mathematical computations, characterizing the non-Euclidian property of the involved data points and learning tasks, such as the image set classification problem. Accompanied with the advanced neural networking techniques, several architectures for processing the SPD matrices have recently been studied to obtain fine-grained structured representations. However, existing approaches are challenged by the diversely changing appearance of the data points, begging the question of how to learn invariant representations for improved performance with supportive theories. Therefore, this paper designs two Riemannian operation modules for SPD manifold neural network. Specifically, a Riemannian batch regularization (RBR) layer is firstly proposed for the purpose of training a discriminative manifold-to-manifold transforming network with a novelly-designed metric learning regularization term. The second module realizes the Riemannian pooling operation with geometric computations on the Riemannian manifolds, notably the Riemannian barycenter, metric learning, and Riemannian optimization. Extensive experiments on five benchmarking datasets show the efficacy of the proposed approach.
Comparing low sensory enabling (<scp>LSE</scp>) and high sensory enabling (<scp>HSE</scp>) virtual product presentation modes in e‐commerce

Abstract Online shops have become increasingly interactive, using different technologies to create virtual experiences that attempt to simulate a realistic product experience. We explore the impact of high sensory enabling (HSE) virtual product presentation modes using state‐of‐the‐art virtual reality (VR) technology that allows consumers to imitate natural movement and interactions via head‐mounted displays (HMD) and dual hand VR controllers. This will compare the HSE virtual product presentation mode with a typical low sensory enabling (LSE) virtual product presentation mode that utilises conventional computer screens, along with mouse and keyboard inputs, on a desktop computer. For the HSE virtual product presentation mode, the results show significantly higher values for the studied variables, including presence, perceived diagnosticity, attitude towards product, and purchase intention. Shopping frequency has a moderating effect on the significant differences of presence between presentation modes. Our research contributes to theory by building on attitude theory, cue summation theory, as well as repetitive learning and memory to explore and explain the effects of HSE virtual product presentation modes on the constructs considered. For managers and industry leaders, this study identifies the importance of using state‐of‐the‐art technology when creating HSE virtual experiences for their products.
A Text Segmentation Approach for Automated Annotation of Online Customer Reviews, Based on Topic Modeling

Online customer review classification and analysis have been recognized as an important problem in many domains, such as business intelligence, marketing, and e-governance. To solve this problem, a variety of machine learning methods was developed in the past decade. Existing methods, however, either rely on human labeling or have high computing cost, or both. This makes them a poor fit to deal with dynamic and ever-growing collections of short but semantically noisy texts of customer reviews. In the present study, the problem of multi-topic online review clustering is addressed by generating high quality bronze-standard labeled sets for training efficient classifier models. A novel unsupervised algorithm is developed to break reviews into sequential semantically homogeneous segments. Segment data is then used to fine-tune a Latent Dirichlet Allocation (LDA) model obtained for the reviews, and to classify them along categories detected through topic modeling. After testing the segmentation algorithm on a benchmark text collection, it was successfully applied in a case study of tourism review classification. In all experiments conducted, the proposed approach produced results similar to or better than baseline methods. The paper critically discusses the main findings and paves ways for future work.
Probabilistic Evaluation of Counterfactual Queries

chapter Share on Probabilistic Evaluation of Counterfactual Queries Authors: Alexander Balke Search about this author , Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 237–254https://doi.org/10.1145/3501714.3501733Online:04 March 2022Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Preservice Teachers’ Knowledge and Attitudes toward Digital-Game-Based Language Learning

There is a good body of literature about digital-game-based language learning (DGBL), but research has mainly focused on students as game players rather than as future educators. This paper reports on a research conducted among 154 teacher candidates at a higher-education institution in Spain regarding the adoption of digital games in education. It analyzes the participants’ knowledge of and attitudes toward digital games in foreign language learning. Quantitative and qualitative data were gathered through a pre/post-test, digital game presentations, and student blog posts. The research comprised five stages associated with critical thinking skills (definition, selection, demonstration, discussion, and reflection), including a game learning module. In the first two stages, preservice teachers completed the module activities and selected different games aimed at teaching English to children in preschool and elementary education. In the last two, they illustrated, discussed, and evaluated the digital games in class following a rubric and reflected on their perception in blog posts. In this four-week research based on a mixed method and convenience sampling, quantitative and qualitative data were gathered through a pre- and post-test survey about student perceptions toward the use of video game in the classroom, class discussion, and blog posts. Statistical data analysis unveiled gender-based differences related to gameplay frequency and genre preferences. The Wilcoxon signed-rank test was used as a nonparametric statistical hypothesis test to compare the two sets of scores resulting from the same participants, and it showed a significant difference (p ≤ 0.05) after the treatment in two of the five dimensions in the survey about teacher candidates’ attitudes toward game usage in education, namely, usefulness (U) and preference for video games (PVG). Research findings revealed preservice teachers’ positive attitudes but lack of practical knowledge about the use of digital games in foreign-language learning.
QEST: Quantized and Efficient Scene Text Detector Using Deep Learning

Scene text detection is complicated and one of the most challenging tasks due to different environmental restrictions, such as illuminations, lighting conditions, tiny and curved texts, and many more. Most of the works on scene text detection have overlooked the primary goal of increasing model accuracy and efficiency, resulting in heavy-weight models that require more processing resources. A novel lightweight model has been developed in this article to improve the accuracy and efficiency of scene text detection. The proposed model relies on ResNet50 and MobileNetV2 as backbones with quantization used to make the resulting model lightweight. During quantization, the precision has been changed from float32 to float16 and int8 for making the model lightweight. In terms of inference time and Floating-Point Operations Per Second, the proposed method outperforms the state-of-the-art techniques by around 30–100 times. Here, well-known datasets, i.e., ICDAR2015 and ICDAR2019, have been utilized for training and testing to validate the performance of the proposed model. Finally, the findings and discussion indicate that the proposed model is more efficient than the existing schemes.
A comprehensive benchmark analysis for sand dust image reconstruction

Numerous sand dust image enhancement algorithms have been proposed in recent years. To our best acknowledge, however, most methods evaluated their performance with no-reference way using few selected real-world images from internet. It is unclear how to quantitatively analysis the performance of the algorithms in a supervised way and how we could gauge the progress in the field. Moreover, due to the absence of large-scale benchmark datasets, there are no well-known reports of data-driven based method for sand dust image enhancement up till now. To advance the development of deep learning-based algorithms for sand dust image reconstruction, while enabling supervised objective evaluation of algorithm performance. In this paper, we presented a comprehensive perceptual study and analysis of real-world sand dust images, then constructed a Sand-dust Image Reconstruction Benchmark (SIRB) for training Convolutional Neural Networks (CNNs) and evaluating algorithms performance. In addition, we adopted the existing image transformation neural network trained on SIRB as baseline to illustrate the generalization of SIRB for training CNNs. Finally, we conducted the qualitative and quantitative evaluation to demonstrate the performance and limitations of the state-of-the-arts (SOTA), which shed light on future research in sand dust image reconstruction.
Compact Graph Structure Learning via Mutual Information Compression

Graph Structure Learning (GSL) recently has attracted considerable attentions in its capacity of optimizing graph structure as well as learning suitable parameters of Graph Neural Networks (GNNs) simultaneously. Current GSL methods mainly learn an optimal graph structure (final view) from single or multiple information sources (basic views), however the theoretical guidance on what is the optimal graph structure is still unexplored. In essence, an optimal graph structure should only contain the information about tasks while compress redundant noise as much as possible, which is defined as ”minimal sufficient structure”, so as to maintain the accurancy and robustness. How to obtain such structure in a principled way? In this paper, we theoretically prove that if we optimize basic views and final view based on mutual information, and keep their performance on labels simultaneously, the final view will be a minimal sufficient structure. With this guidance, we propose a Compact GSL architecture by MI compression, named CoGSL. Specifically, two basic views are extracted from original graph as two inputs of the model, which are refinedly reestimated by a view estimator. Then, we propose an adaptive technique to fuse estimated views into the final view. Furthermore, we maintain the performance of estimated views and the final view and reduce the mutual information of every two views. To comprehensively evaluate the performance of CoGSL, we conduct extensive experiments on several datasets under clean and attacked conditions, which demonstrate the effectiveness and robustness of CoGSL.
Data sharing practices across knowledge domains: A dynamic examination of data availability statements in <i>PLOS ONE</i> publications

As the importance of research data gradually grows in sciences, data sharing has come to be encouraged and even mandated by journals and funders in recent years. Following this trend, the data availability statement has been increasingly embraced by academic communities as a means of sharing research data as part of research articles. This article presents a quantitative study of which mechanisms and repositories are used to share research data in PLOS ONE articles. We offer a dynamic examination of this topic from the disciplinary and temporal perspectives based on all statements in English-language research articles published between 2014 and 2020 in the journal. We find a slow yet steady growth in the use of data repositories to share data over time, as opposed to sharing data in the article and/or supplementary materials; this indicates improved compliance with the journal’s data sharing policies. We also find that multidisciplinary data repositories have been increasingly used over time, whereas some disciplinary repositories show a decreasing trend. Our findings can help academic publishers and funders to improve their data sharing policies and serve as an important baseline dataset for future studies on data sharing activities.
Deep vanishing point detection: Geometric priors make dataset variations vanish

Deep learning has improved vanishing point detection in images. Yet, deep networks require expensive annotated datasets trained on costly hardware and do not generalize to even slightly different domains, and minor problem variants. Here, we address these issues by injecting deep vanishing point detection networks with prior knowledge. This prior knowledge no longer needs to be learned from data, saving valuable annotation efforts and compute, unlocking realistic few-sample scenarios, and reducing the impact of domain changes. Moreover, the interpretability of the priors allows to adapt deep networks to minor problem variations such as switching between Manhattan and non-Manhattan worlds. We seamlessly incorporate two geometric priors: (i) Hough Transform – mapping image pixels to straight lines, and (ii) Gaussian sphere – mapping lines to great circles whose intersections denote vanishing points. Experimentally, we ablate our choices and show comparable accuracy to existing models in the large-data setting. We validate our model's improved data efficiency, robustness to domain changes, adaptability to non-Manhattan settings.
Effects of Virtual Room Size and Objects on Relative Translation Gain Thresholds in Redirected Walking

This paper investigates how the size of virtual space and objects within it affect the threshold range of relative translation gains, a Redirected Walking (RDW) technique that scales the user&#x2019;s movement in virtual space in different ratios for the width and depth. While previous studies assert that a virtual room&#x2019;s size affects relative translation gain thresholds on account of the virtual horizon&#x2019;s location, additional research is needed to explore this assumption through a structured approach to visual perception in Virtual Reality (VR). We estimate the relative translation gain thresholds in six spatial conditions configured by three room sizes and the presence of virtual objects (3 &#x00D7; 2), which were set according to differing Angles of Declination (AoDs) between eye-gaze and the forward-gaze. Results show that both size and virtual objects significantly affect the threshold range, it being greater in the large-sized condition and furnished condition. This indicates that the effect of relative translation gains can be further increased by constructing a perceived virtual movable space that is even larger than the adjusted virtual movable space and placing objects in it. Our study can be applied to adjust virtual spaces in synchronizing heterogeneous spaces without coordinate distortion where real and virtual objects can be leveraged to create realistic mutual spaces.
Fourier Document Restoration for Robust Document Dewarping and Recognition

State-of-the-art document dewarping techniques learn to predict 3-dimensional information of documents which are prone to errors while dealing with documents with irregular distortions or large variations in depth. This paper presents FDRNet, a Fourier Document Restoration Network that can restore documents with different distortions and improve document recognition in a reliable and simpler manner. FDRNet focuses on high-frequency components in the Fourier space that capture most structural information but are largely free of degradation in appearance. It dewarps documents by a flexible Thin-Plate Spline transformation which can handle various deformations effectively without requiring deformation annotations in training. These features allow FDRNet to learn from a small amount of simply labeled training images, and the learned model can dewarp documents with complex geometric distortion and recognize the restored texts accurately. To facilitate document restoration research, we create a benchmark dataset consisting of over one thousand camera documents with different types of geometric and photometric distortion. Extensive experiments show that FDRNet outperforms the state-of-the-art by large margins on both dewarping and text recognition tasks. In addition, FDRNet requires a small amount of simply labeled training data and is easy to deploy. The proposed dataset is available at https://sg-vilab.github.io/event/warpdoc/.