Reviewerly/exHarmony huggingface dataset:
Multivariate Chaotic Time Series Prediction Based on Improved Grey Relational Analysis

In multivariate chaotic time series prediction, correlation analysis is important for reducing input dimensions and improving prediction performance. Grey relational analysis (GRA) has proved to be an effective method for data correlation analysis, especially for inexact data and incomplete data. In GRA, points are usually regarded as objects, and the distance between points or the concave and convex degree are mostly used to measure the correlations. However, with discrete variables, correlation analysis results always tend to have some deviations when using prior GRA methods. Furthermore, GRA methods cannot directly use vector datasets. Therefore, in this paper, an improved GRA method is proposed based on vector projections. The input and output variables are expressed as vectors by linking two adjacent points. The vectors, instants of the points, are regarded as the objects, and the projection length of input variables to output variables is used to measure the correlations. The smaller the difference between the projection length and the input variables, the higher the correlation. Then, a hybrid variable selection and prediction model is proposed based on the improved GRA method for multivariate chaotic time series predictions, in order to overcome the negative effects of irrelevant and redundant variables caused by phase-space reconstruction. The experimental results based on the gas furnace dataset and San Francisco river runoff dataset demonstrate that the improved GRA method is effective for data correlation analysis, and the prediction accuracy is better than prior GRA-based methods.
Correlation Judgment and Visualization Features: A Comparative Study

Recent visualization research efforts have incorporated experimental techniques and perceptual models from the vision science community. Perceptual laws such as Weber's law, for example, have been used to model the perception of correlation in scatterplots. While this thread of research has progressively refined the modeling of the perception of correlation in scatterplots, it remains unclear as to why such perception can be modeled using relatively simple functions, e.g., linear and log-linear. In this paper, we investigate a longstanding hypothesis that people use visual features in a chart as a proxy for statistical measures like correlation. For a given scatterplot, we extract 49 candidate visual features and evaluate which best align with existing models and participant judgments. The results support the hypothesis that people attend to a small number of visual features when discriminating correlation in scatterplots. We discuss how this result may account for prior conflicting findings, and how visual features provide a baseline for future model-based approaches in visualization evaluation and design.
Detecting unfair recommendations in trust-based pervasive environments

In pervasive/ubiquitous computing environments, interacting users may evaluate their respective trustworthiness by using historical data coming from their past interactions. Nevertheless, when two users are at the first interaction, they have no historical data involving their own activities to be analyzed, and then use information (recommender-data) provided by other users (recommenders) who, in the past, have had interactions with one of the involved parties. Although this approach has proven to be effective, it might fail if dishonest recommenders provide unfair recommender-data. Indeed, such unfair data may lead to skewed evaluations, and therefore either increase the trustworthiness of a malicious user or reduce the one of a honest user in a fraudulent way. In this work, we propose an algorithm for identifying false recommender-data. Our attention is explicitly focused on recommender-data rather than recommenders. This because some recommenders could provide recommender-data containing only a limited (but specific) set of altered information. This is used by dishonest recommenders as a tactic to avoid being discovered. The proposed algorithm uses association rules to express a confidence-based measure (reputation rank), which is used as a reliability ranking of the recommender-data. The resulting approach has been compared with other existing ones in this field, resulting more accurate in finding out unfair recommender-data sent by dishonest recommenders.
SAR Image Retrieval Based on Unsupervised Domain Adaptation and Clustering

Efficiently retrieving synthetic aperture radar (SAR) image is an important yet challenging task in the remote sensing field. Due to the shortage of labeled SAR images for fine-tuning convolutional neural network (CNN) models, this letter presents an unsupervised domain adaptation model based on CNN to learn the domain-invariant feature between SAR images and optical aerial images for SAR image retrieving, which can alleviate the burden of manual labeling. We extend a deep CNN to a novel adversarial network by adding the domain discriminator and the pseudolabel predictor. We improve the adaptation capacity of the adversarial network by utilizing the class information of SAR training images, which is obtained by clustering. Compared with the other related methods, the proposed method can enhance retrieval performance with our SAR data set.
On Benchmarking of Automated Methods for Performing Exhaustive Reaction Path Search

In recent years, the importance of computational chemistry approaches has grown rapidly because of recent advances in computational software and hardware. Automated reaction path search is one of the promising techniques which would allow exploration of unknown chemistry using computers. Several methods have been developed so far, and comparison of the performance of existing methods would be a subject of interest to many chemists. In this paper, we present the performance of our single-component artificial force induced reaction method (SC-AFIR) to rectify the result shown in a recent report [ Grambow et al. J. Am. Chem. Soc. 2018 , 140 , 1035 - 1048 ] on the performance of the SC-AFIR method compared to four other methods. We discuss the flaws in their benchmark procedure and our thoughts on benchmarking of automated reaction path search methods.
Dynamical Blockade in a Single-Mode Bosonic System

We introduce a dynamical blockade phenomenon occurring in a nonlinear bosonic mode induced by a combination of continuous and pulsed excitations. We find that the underlying mechanism for the blockade is general, enhancing antibunching in the strongly nonlinear regime and inducing it in the weakly nonlinear regime, without fine-tuning the system parameters. Moreover, this mechanism shows advantages over existing blockade mechanisms and is suitable for implementation in a wide variety of systems due to its simplicity and universality.
DeepLight

We present a learning-based method to infer plausible high dynamic range (HDR), omnidirectional illumination given an unconstrained, low dynamic range (LDR) image from a mobile phone camera with a limited field of view (FOV). For training data, we collect videos of various reflective spheres placed within the camera's FOV, leaving most of the background unoccluded, leveraging that materials with diverse reflectance functions reveal different lighting cues in a single exposure. We train a deep neural network to regress from the LDR background image to HDR lighting by matching the LDR ground truth sphere images to those rendered with the predicted illumination using image-based relighting, which is differentiable. Our inference runs at interactive frame rates on a mobile device, enabling realistic rendering of virtual objects into real scenes for mobile mixed reality. Training on auto-exposed and white-balanced videos, we improve the realism of rendered objects compared to the state-of-the art methods for both indoor and outdoor scenes.
Approximation Ratios of Graph Neural Networks for Combinatorial Problems

In this paper, from a theoretical perspective, we study how powerful graph neural networks (GNNs) can be for learning approximation algorithms for combinatorial problems. To this end, we first establish a new class of GNNs that can solve a strictly wider variety of problems than existing GNNs. Then, we bridge the gap between GNN theory and the theory of distributed local algorithms. We theoretically demonstrate that the most powerful GNN can learn approximation algorithms for the minimum dominating set problem and the minimum vertex cover problem with some approximation ratios with the aid of the theory of distributed local algorithms. We also show that most of the existing GNNs such as GIN, GAT, GCN, and GraphSAGE cannot perform better than with these ratios. This paper is the first to elucidate approximation ratios of GNNs for combinatorial problems. Furthermore, we prove that adding coloring or weak-coloring to each node feature improves these approximation ratios. This indicates that preprocessing and feature engineering theoretically strengthen model capabilities.
Discovery of Useful Questions as Auxiliary Tasks

Arguably, intelligent agents ought to be able to discover their own questions so that in learning answers for them they learn unanticipated useful knowledge and skills; this departs from the focus in much of machine learning on agents learning answers to externally defined questions. We present a novel method for a reinforcement learning (RL) agent to discover questions formulated as general value functions or GVFs, a fairly rich form of knowledge representation. Specifically, our method uses non-myopic meta-gradients to learn GVF-questions such that learning answers to them, as an auxiliary task, induces useful representations for the main task faced by the RL agent. We demonstrate that auxiliary tasks based on the discovered GVFs are sufficient, on their own, to build representations that support main task learning, and that they do so better than popular hand-designed auxiliary tasks from the literature. Furthermore, we show, in the context of Atari2600 videogames, how such auxiliary tasks, meta-learned alongside the main task, can improve the data efficiency of an actor-critic agent.
Where is the learning in mobile technologies for learning?

Mobile technology – such as tablets, cell phones, and wearable devices – has the potential to play a useful role in promoting academic learning. This potential motivates this special issue on “Mobile Technology, Learning, and Achievement: Advances in Understanding and Measuring the Role of Mobile Technology in Education” edited by Matthew L. Bernacki, Jeffrey Greene, and Helen Crompton. Research on learning with mobile technology should focus on three research questions: Do students learn academic content better with mobile technology than with conventional media (media comparison question)? Which instructional features afforded by mobile technology cause learning (instructional method question)? Under what conditions do students learn academic content better with mobile technology than with conventional media (boundary condition question)? A research agenda is proposed.
Current state of whole slide imaging use in cytopathology: Pros and pitfalls

Abstract Whole slide imaging ( WSI ) allows generation of large whole slide images and their navigation with zoom in and out like a true virtual microscope. It has become widely used in surgical pathology for many purposes, such as education and training, research activity, teleconsultation, and primary diagnosis. However, in cytopathology, the use of WSI has been lagging behind histology, mainly due to the cytological specimen's characteristics, as groups of cells of different thickness are distributed throughout the slide. To allow the same focusing capability of light microscope, slides have to be scanned at multiple focal planes, at the cost of longer scan times and larger file size. These are the main technical pitfalls of WSI for cytopathology, partly overcome by solutions like liquid‐based preparations. Validation studies for the use in primary diagnosis are less numerous and more heterogeneous than in surgical pathology. WSI has been proved effective for training students and successfully used in proficiency testing, allowing the creation of digital cytology atlases. Longer scan times are also a barrier for use in rapid on‐site evaluation, but WSI retains its advantages of easy sharing of images for consultation, multiple simultaneous viewing in different locations, the possibility of unlimited annotations and easy integration with medical records. Moreover, digital slides set the laboratory free from reliance on a physical glass slide, with no more concern of fading of stain or slide breakage. Costs are still a problem for small institutions, but WSI can also represent the beginning of a more efficient way of working.
Learning Fused Pixel and Feature-Based View Reconstructions for Light Fields

In this paper, we present a learning-based framework for light field view synthesis from a subset of input views. Building upon a light-weight optical flow estimation network to obtain depth maps, our method employs two reconstruction modules in pixel and feature domains respectively. For the pixel-wise reconstruction, occlusions are explicitly handled by a disparity-dependent interpolation filter, whereas inpainting on disoccluded areas is learned by convolutional layers. Due to disparity inconsistencies, the pixel-based reconstruction may lead to blurriness in highly textured areas as well as on object contours. On the contrary, the feature-based reconstruction well performs on high frequencies, making the reconstruction in the two domains complementary. End-to-end learning is finally performed including a fusion module merging pixel and feature-based reconstructions. Experimental results show that our method achieves state-of-the-art performance on both synthetic and real-world datasets, moreover, it is even able to extend light fields' baseline by extrapolating high quality views without additional training.
Guidelines for immersive virtual reality in psychological research

Virtual reality (VR) holds immense promise as a research tool to deliver results that are generalizable to the real world. However, the methodology used in different VR studies varies substantially. While many of these approaches claim to use ‘immersive VR’, the different hardware and software choices lead to issues regarding reliability and validity of psychological VR research. Questions arise about quantifying presence, the optimal level of graphical realism, the problem of being in dual realities and reproducibility of VR research. We discuss how VR research paradigms could be evaluated and offer a list of practical recommendations to have common guidelines for psychological VR research.
GTIKF- Gabor-Transform Incorporated K-Means and Fuzzy C Means Clustering for Edge Detection in CT and MRI

The detection of edges is the one of the important stage in the application, associated with the machine vision, computer vision and the image processing. It is most commonly and highly preferred in the area were the extraction or the detection of the attribute are necessary. As the manual methods of diagnosis in the medical images acquired from the CT (computed tomography) and the MRI (magnetic resonance images) are very tedious and as well as time consuming, the paper puts forth the methodology to detect the edges in the CT and the MRI by employing Gabor Transform as well as the soft and the hard clustering. This proposed method is highly preferred among the image with dynamic variations. The technique used in the paper is evaluated using 4500 instance of the MRI and 3000 instance of CT. The results on the basis of the figure of merit (FOM) and Misclassification rate (MCR) are compared with other standard approaches and the performance was evinced.
Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting

Accurate forecasting of solar photovoltaic (PV) power for the next day plays an important role in unit commitment, economic dispatch, and storage system management. However, forecasting solar PV power in high temporal resolution such as five-minute resolution is challenging because most of PV forecasting models can only achieve the same temporal resolution as their predictors(i.e., weather variables), whose temporal resolution is usually low (i.e., hourly). To address this challenge, similarity-based forecasting models (SBFMs) are advocated in this paper to forecast PV power in high temporal resolution using low temporal resolution weather variables. To effectively generalize the model for different scenarios of available weather data, three forecasting models (i.e., basic SBFM, categorical SBFM, and hierarchical SBFM) are proposed. As a case study, the PV power generated by the solar panels on the rooftop of a commercial building is forecasted for the next day with a five-minute resolution under three different scenarios of available weather data. The leave-one-out cross-validation analysis reveals that using only two or three weather variables, the proposed SBFMs can achieve higher forecasting accuracy than several benchmark models.
Does label smoothing mitigate label noise

Label smoothing is commonly used in training deep learning models, wherein one-hot training labels are mixed with uniform label vectors. Empirically, smoothing has been shown to improve both predictive performance and model calibration. In this paper, we study whether label smoothing is also effective as a means of coping with label noise. While label smoothing apparently amplifies this problem --- being equivalent to injecting symmetric noise to the labels --- we show how it relates to a general family of loss-correction techniques from the label noise literature. Building on this connection, we show that label smoothing is competitive with loss-correction under label noise. Further, we show that when distilling models from noisy data, label smoothing of the teacher is beneficial; this is in contrast to recent findings for noise-free problems, and sheds further light on settings where label smoothing is beneficial.
SE-PPFM: A Searchable Encryption Scheme Supporting Privacy-Preserving Fuzzy Multikeyword in Cloud Systems

Cloud computing provides an appearing application for compelling vision in managing big-data files and responding queries over a distributed cloud platform. To overcome privacy revealing risks, sensitive documents and private data are usually stored in the clouds in a cipher-based manner. However, it is inefficient to search the data in traditional encryption systems. Searchable encryption is a useful cryptographic primitive to enable users to retrieve data in ciphertexts. However, the traditional searchable encryptions provide lower search efficiency and cannot carry out fuzzy multikeyword queries. To solve this issue, in this article, we propose a searchable encryption that supports privacy-preserving fuzzy multikeyword search (SE-PPFM) in cloud systems, which is built by asymmetric scalar-product-preserving encryptions and Hadamard product operations. In order to realize the functionality of efficient fuzzy searches, we employ Word2vec as the primitive of machine learning to obtain a fuzzy correlation score between encrypted data and queries predicates. We analyze and evaluate the performance in terms of token of multikeyword, retrieval and match time, file retrieval time and matching accuracy, etc. The experimental results show that our scheme can achieve a higher efficiency in fuzzy multikeyword ciphertext search and provide a higher accuracy in retrieving and matching procedure.
Exploring the Cognitive Load of Expert and Novice Map Users Using EEG and Eye Tracking

The main objective of this research is to explore the cognitive processes of expert and novice map users during the retrieval of map-related information, within varying difficulty levels (i.e., easy, moderate, hard), by using eye tracking and electroencephalogram (EEG). In this context, we present a spatial memory experiment consisting of a large number of stimuli to study the effect of task difficulty on map users’ behavior through cognitive load measurements. Next to the reaction time and success rate, we used fixation and saccade related eye tracking metrics (i.e., average fixation duration, the number of fixations per second, saccade amplitude and saccade velocity), and EEG power spectrum (i.e., event-related changes in alpha and theta frequency bands) to identify the cognitive load. While fixation metrics indicated no statistically significant difference between experts and novices, saccade metrics proved the otherwise. EEG power spectral density analysis, on the other side, suggested an increase in theta (i.e., event-related synchronization) and a decrease in alpha (except moderate tasks) (i.e., event-related desynchronization) at all difficulty levels of the task for both experts and novices, which is an indicator of cognitive load. Although no significant difference emerged between two groups, we found a significant difference in their overall performances when the participants were classified as good and relatively bad learners. Triangulating EEG results with the recorded eye tracking data and the qualitative analysis of focus maps indeed provided a detailed insight on the differences of the individuals’ cognitive processes during this spatial memory task.
The Media Spectacle of a Techno-City: COVID-19 and the South Korean Experience of the State of Emergency

This essay provides a critical observation of the South Korean government's distinctive management of COVID-19 with particular reference to the state of emergency. It reveals that the success of South Korea's handling of the pandemic is largely attributed by a majority of Western media to the efficient deployment of both information and communication technologies and Confucian collectivism, two components that seem contradictory yet not incompatible under the rubric of techno-Orientalism. Analyzing the intensification of surveillance and the rapid datafication of society, this essay argues that the current state of emergency is not a breakdown of normality but a continuation of the state of crisis and disaster that rules a developing country like South Korea. In doing so, the essay seeks to facilitate a critical discussion about a new mode of democracy in the era of pandemic that increasingly grapples with tensions between individual freedom and public health.
Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net

Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the image pre-processing pipeline. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved averaged Dice similarity coefficients of 0.97 on T2-weighted anatomical images, and 0.96 on T2*-weighted echo planar imaging, demonstrating robust performance of our approach across various MRI protocols.