Reviewerly/exHarmony huggingface dataset:
Maximizing drilling information in greenfields exploration: Linking the fabric and geochemical footprint of the basement to the surface in South Australia

Detection of mineral system footprints in regions under thick cover is challenging. The difficulties are enhanced in regions with low-relief landscapes that are deeply weathered. This research examines how information from a single drill hole in an underexplored region can deliver a significant amount of information to assist in greenfields exploration. This study describes the geochemical dispersion processes through >500 m of cover based on observations from drill hole CDP008, and explores the possibility of recognising landscape features that link basement features with the surface. Our study revealed that: (1) the lower fluvial sandstone package contains a geochemical footprint of the underlying basement rocks, produced by vertical and lateral geochemical dispersion; whereas (2) the overlying sediments do not record any footprint of the basement rocks; and (3) the top limestone units are a chemical barrier for vertical geochemical dispersion due to their lack of permeability. Basement features identified from magnetic data are mimicked by linear surface landscape features that lie above them, which may potentially be associated with vertical geochemical dispersion processes, linking the basement with the surface. Hence from the point of view of mineral exploration, surface geochemical sampling should target these particular neotectonic/reactivation-associated features of the landscape. We suggest that in areas of deep cover, neotectonics/reactivation surface landscape features have the highest prospectivity to detect deep basement geochemical signatures at surface. The findings from this study may therefore impact approaches to mineral exploration under cover in similar landscape contexts around the world, such as regions in West Africa, India, China and Brazil.
Unpacking Practitioners’ Attitudes Towards Codifications of Design Knowledge for Voice User Interfaces

Recent HCI research has sought to develop guidelines—‘heuristics’, ‘best practices’, ‘principles’ and so on—for voice user interfaces (VUI) to aid both practitioners and researchers in improving the quality of VUI-based design. However, limited research is available on how such design knowledge is conceptualised and used by industry practitioners. We present a small interview-based study conducted with 9 experienced VUI industry practitioners. Their concerns range from terminological challenges associated with VUI design knowledge, the role of codifications of such knowledge like design guidelines alongside their practical design work, through to their views on the value of ‘harmonisation’ of VUI design knowledge. Given the complex—albeit preliminary—picture that emerges, we argue for HCI's deeper consideration of how design knowledge meshes with the contingencies of practice, so that VUI design knowledge—such as design guidelines developed in HCI—delivers the most potential value for industry practice.
Efficient Diffeomorphic Image Registration using Multi-Scale Dual-Phased Learning

Diffeomorphic registration faces challenges for high dimensional images, especially in terms of memory limits. Existing approaches either downsample/crop original images or approximate underlying transformations to reduce the model size. To mitigate this, we propose a Dividing and Down-sampling mixed Registration network (DDR-Net), a general architecture that preserves most of the image information at multiple scales while reducing memory cost. DDR-Net leverages the global context via downsampling the input and utilizes local details by dividing the input images to subvolumes. Such design fuses global and local information and obtains both coarse- and fine-level alignments in the final deformation fields. We apply DDR-Net to the OASIS dataset. The proposed simple yet effective architecture is a general method and could be extended to other registration architectures for better performance with limited computing resources.
Counterfactual Explainable Gastrointestinal and Colonoscopy Image Segmentation

Segmenting medical images accurately and reliably is crucial for disease diagnosis and treatment. Due to the wide assortment of objects’ sizes, shapes, and scanning modalities, it has become more challenging. Many convolutional neural networks (CNN) have recently been designed for segmentation tasks and achieved great success. This paper presents an optimized deep learning solution using DeepLabv3+ with ResNet-101 as its backbone. The proposed approach allows capturing variabilities of diverse objects. It provides improved and reliable quantitative and qualitative results in comparison to other state-of-the-art (SOTA) methods on two publicly available gastrointestinal and colonoscopy datasets. Few studies show the inadequacy of stable performance in varying object segmentation tasks, notwithstanding the sizes of objects. Our method has stable performance in the segmentation of large and small medical objects. The explainability of our robust model with benchmarking on SOTA approaches for both datasets will be fruitful for further research on biomedical image segmentation.
Balanced Allocation of Educational Resources Based on Parallel Genetic Algorithm

Higher education is one of the scarcest social resources, with high demand and low supply, and higher education is currently in limited supply in our country, making it difficult to resolve this contradiction. In addition to increasing investment in higher education as much as possible, the most important thing is to maximize the benefits of education through the rational allocation of resources. An evaluation index system of educational resource input-output was constructed, and a multiobjective function model of educational resource utilization efficiency and allocation efficiency was proposed. We should rationalize the allocation of resources and maximize the benefits of innovation and entrepreneurship education in colleges and universities. By combining particle swarm optimization with genetic algorithm, we can simulate and solve the model. The simulation results suggest that by optimizing the usage and allocation efficiency of innovation, it may be increased. College and university entrepreneurship education resources have increased by 18.72 percent and 20.98 percent, respectively, on average, and tend to be in a balanced state, which can realize the optimization of education resources allocation.
Flow-Adapter Architecture for Unsupervised Machine Translation

In this work, we propose a flow-adapter architecture for unsupervised NMT.It leverages normalizing flows to explicitly model the distributions of sentence-level latent representations, which are subsequently used in conjunction with the attention mechanism for the translation task.The primary novelties of our model are: (a) capturing language-specific sentence representations separately for each language using normalizing flows and (b) using a simple transformation of these latent representations for translating from one language to another.This architecture allows for unsupervised training of each language independently.While there is prior work on latent variables for supervised MT, to the best of our knowledge, this is the first work that uses latent variables and normalizing flows for unsupervised MT.We obtain competitive results on several unsupervised MT benchmarks.
Semi-Supervised Tumor Response Grade Classification from Histology Images of Colorectal Liver Metastases

Colorectal liver metastases (CLM) develop in almost half of patients with colon cancer. Response to systemic chemotherapy is the main determinant of patient survival. Due to the importance of assessing treatment response of CLM to chemotherapy for the patient prognosis, there is a need to classify tumor response grade (TRG) on histopathology slides (HPS). However, annotating HPS for training neural networks is a time-consuming task. In this work, we present an end-to-end approach for tissue classification of CLM slides leading to TRG prediction. A weakly-supervised model is first trained to perform tissue classification from sparse annotations, generating segmentation maps. Then, using features extracted for these maps, a secondary model is trained to perform the TRG classification. We demonstrate the feasibility of the proposed approach on a clinical dataset of 1450 HPS from 232 CLM patients by comparing our semi-supervised Mean Teacher approach with other supervised and semi-supervised methods. The proposed pipeline outperforms other models, achieving a classification accuracy of 94.4%. Based on the generated classification maps, the model is able to stratify patients into two TRG classes (1-2 vs 3-5) with an accuracy of 86.2%.
Fusion-Based Multimodal Medical Image Registration Combining Inter-Modality Metric and Disentanglement

Multimodal medical image registration is a challenging problem. Inter-modal metric and unifying images into the same domain are two feasible methods, but each has its emphasis. We proposed a fusion-based registration method to combine the advantages of the two ways. In the first stage, two sub-networks register images individually. The code sub-network adopts the disentanglement content code method, and the image sub-network adopts the inter-modality metric method. In the second stage, based on the preliminary information pro-vided by sub-networks, a fusion network is built to perform further registration in a more comprehensive perspective. We compare the typical registration methods quantitatively and qualitatively, including inter-modality, image-to-image translation, and fusion methods. Experiments demonstrate our method overcomes the limit of single methods and is comparable with the state-of-art iterative and learning-based approaches.
Nudging Behavior Change: Using In-Group and Out-Group Social Comparisons to Encourage Healthier Choices

This paper revisits concepts of nudge in the context of helping consumers to make healthier food choices. We introduce a novel form of social influence nudge not yet investigated by HCI scholars, the out-group social comparison, and test whether this form of nudging works at the point of checkout rather than the more conventional point of product consideration. Across two online experiments, we measure the effectiveness of using nutritional information nudges with added in-group (people like you) and out-group (people not like you) social comparisons. Our preliminary findings suggest that out-group social comparison nudges can be effective in encouraging both normal weight and overweight adults to reduce calories, even when these adults indicate that they do not typically change their diet behaviors. This research has implications for digital information design, interactive marketing, and public health.
Privacy-Friendly De-authentication with BLUFADE: Blurred Face Detection

Ideally, secure user sessions should start and end with authentication and de-authentication phases, respectively. While the user must pass the former to start a secure session, the latter&#x2019;s importance is often ignored or underestimated. Dangling or unattended sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, the research community focused on automated de-authentication systems. Unfortunately, no single approach offers security, privacy, and usability. For instance, although facial recognition-based methods might be a good fit for security and usability, they violate user privacy by constantly recording the user and the surrounding environment.In this work, we propose BLUFADE, a fast, secure, and transparent de-authentication system that takes advantage of blurred faces to preserve user privacy. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection continuously. To assess BLUFADE&#x2018;s practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. The former was used to train and evaluate the deauthentication system performances, the latter to assess the privacy and to increase variance in training data. We show that our approach outperforms state-of-the-art methods in detecting blurred faces, achieving up to 95% accuracy. Furthermore, we demonstrate that BLUFADE effectively de-authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements.
Visual sensitivity filtering based local maximum variation prior for blind image deblurring

Blind image deblurring aims to recover the latent sharp image from a given blurred image when the blur kernel is unknown. To solve this ill-posed problem, many researchers have studied various priors to guide the deblurring process. However, whether the image is sharp or not is mostly based on the subjective judgment of human eyes, and there is no unified objective standard. Since the response of blur to visual sensitivity can be modeled by a filtering method, we use human visual system (HVS) convolution filter to filter the target image to simulate the corresponding visual sensitivity. At the same time, we find that the local maximum variation values of the filtered image will be reduced after blurring process. Therefore, we propose a local maximum variation prior (LMV) based on visual sensitivity filtering and construct a new energy function according to LMV. Our method performs well in various scenarios. Compared with the most advanced algorithms, our model achieves good results on both benchmark datasets and natural images.
Exploration of Deep Neural Networks with Symmetric Simplicial Layers for On-Satellite Earth Observation Processing

Bringing artificial intelligence on-board of space crafts holds significant promise for enhancing the capabilities of space missions. On-board processing can enable responsive missions that are not limited by the latency and bandwidth of the communication to earth. However, in many cases dedicated solutions are required due to the resource constraint environment of satellites. In this contribution we explore architectures for on-board processing of earth observation imagery based on deep neural networks with Symmetric Simplicial (SymSim) layers. The performance of the networks are assessed for the task of plume and scenery classification in RGB earth observation pictures. We propose an extended SymSim algorithm and show its performance in small variants of ResNet compared to the same architectures with convolutional blocks.
DLA-VPS: Deep-Learning-Assisted Visual Parameter Space Analysis of Cosmological Simulations

Cosmologists often build a mathematics simulation model to study the observed universe. However, running a high-fidelity simulation is time consuming and thus can inconvenience the analysis. This is especially so when the analysis involves trying out a large number of simulation input parameter configurations. Therefore, selecting an input parameter configuration that can meet the needs of an analysis task has become an important part of the analysis process. In this work, we propose an interactive visual system that efficiently helps users understand the parameter space related to their cosmological data. Our system utilizes a GAN-based surrogate model to reconstruct the simulation outputs without running the expensive simulation. We also extract information learned by the deep neural-network-based surrogate models to facilitate the parameter space exploration. We demonstrate the effectiveness of our system via multiple case studies. These case study results demonstrate valuable simulation input parameter configuration and subregion analyses.
The Voight-Kampff Machine for Automatic Custom Gesture Rejection Threshold Selection

Gesture recognition systems using nearest neighbor pattern matching are able to distinguish gesture from non-gesture actions by rejecting input whose recognition scores are poor. However, in the context of gesture customization, where training data is sparse, learning a tight rejection threshold that maximizes accuracy in the presence of continuous high activity (HA) data is a challenging problem. To this end, we present the Voight-Kampff Machine (VKM), a novel approach for rejection threshold selection. VKM uses new synthetic data techniques to select an initial threshold that the system thereafter adjusts based on the training set size and expected gesture production variability. We pair VKM with a state-of-the-art custom gesture segmenter and recognizer to evaluate our system across several HA datasets, where gestures are interleaved with non-gesture actions. Compared to alternative rejection threshold selection techniques, we show that our approach is the only one that consistently achieves high performance.
On the structure of finite sets with the same representation functions

For any positive integer m, let Zm be the set of residue classes modulo m. For A⊆Zm and n‾∈Zm, let the representation function RA(n‾) denote the number of solutions of the equation n‾=a‾+a′‾ with unordered pairs (a‾,a′‾)∈A×A. Let M be an odd integer with M≥3 and m=2M. In this paper, we determine the structure of A,B⊆Zm with A∪B=Zm and |A∩B|=2 such that RA(n‾)=RB(n‾) for all n‾∈Zm.
Ca-Mt: A Self-Ensembling Model for Semi-Supervised Cardiac Segmentation with Elliptical Descriptor Based Contour-Aware

Nowadays, semi-supervised learning has achieved greatly in computer aided cardiac segmentation, which utilized unlabeled data to cut annotation cost and improve the network performance. However, most existing methods neglect the contour constrain of cardiac which often lead to inaccurate segmentation result, particularly around boundaries. In this paper, we proposed a novel self-ensembling approach for cardiac segmentation to leverage unlabeled data and contour information of labeled data. To achieve this, we develop an elliptical contour descriptor to describe the segmentation contour in several sets of parameters. Furthermore, a contour-aware loss is designed to minimize the difference be-tween target and prediction. To leverage the annotated data, we then introduce a self-ensembling method named teacher-student network which requires the prediction of teacher and student branches being consistent under random perturbation or transformation of input images. We integrate our contour-aware method into this framework and let it gradually learn the contour information from coarse to fine. Experiments on the 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) and Left Atrial Segmentation Challenge 2013 (LASC’13) datasets show that our approach improves the cardiac segmentation performance and outperforms existing semi-supervised segmentation approaches.
Quantifying Proactive and Reactive Button Input

When giving input with a button, users follow one of two strategies: (1) react to the output from the computer or (2) proactively act in anticipation of the output from the computer. We propose a technique to quantify reactiveness and proactiveness to determine the degree and characteristics of each input strategy. The technique proposed in this study uses only screen recordings and does not require instrumentation beyond the input logs. The likelihood distribution of the time interval between the button inputs and system outputs, which is uniquely determined for each input strategy, is modeled. Then the probability that each observed input/output pair originates from a specific strategy is estimated along with the parameters of the corresponding likelihood distribution. In two empirical studies, we show how to use the technique to answer questions such as how to design animated transitions and how to predict a player’s score in real-time games.
Video Stabilization Using ORB Detector

Capturing a video may result in unwanted effects such as image blurring, distortion due to the noise, etc. To obtain a jitter-free video, this study aims to perform Digital Video Stabilization using an ORB (Oriented FAST Rotated BRIEF) detector to find feature points. It makes use of a Square filter with a moving average window in order to obtain a more stable output video. The goal of this proposed study is to implement the novel methodology to stabilize videos effectively.
Accelerated Phase Deviation Elimination for Measuring Moving Object Shape with Phase-Shifting-Profilometry

Eliminating the phase deviation caused by object motion plays a vital role to obtain the precise phase map to recover the object shape with phase-shifting-profilometry. Pixel-by-pixel phase retrieval using the least-squares algorithm has been widely employed to eliminate the phase deviation caused by moving object. However, pixel-level operation can only eliminate phase deviation within a limited range, and will bring high computational burden. In this paper, we propose an image-level phase compensation method with stochastic gradient descent (SGD) algorithm to accelerate the phase deviation elimination. Since the iteration calculation is implemented at the image-level, the proposed method can accelerate the convergence significantly. Furthermore, since the proposed algorithm is able to correct the phase deviation within (−π,π), the algorithm can tolerate a greater motion range. In addition to simulation experiments, we consider 2-D motion of the object, and conduct a series of comparative experiments to validate the effectiveness of the proposed method in a larger motion range.
Lessons in place: Thoreau and Indigenous philosophy

Some have argued that Indigenous and European Americans learned much from each other along the border. This paper examines the fate of the influence of Indigenous philosophy by considering the work of Henry David Thoreau. First, it summarizes the argument of Native Pragmatism: Rethinking the Roots of American Philosophy about the influence of Indigenous thought on American philosophy. In the second section, it discusses American philosophies of resistance in relation to the process of colonization. In the third, it considers the later work of Thoreau as an example of the declining influence of Native thought on nineteenth‐century American philosophy. On one hand, Thoreau adopted a philosophy of place that seems to emerge directly from his experiences with Indigenous Americans. On the other, he overtly sets aside the Indigenous conception of relational agency in favor of a Romantic conception of individuals over against both nature and civilization.
R-GEFS: Condorcet Rank Aggregation with Graph Theoretic Ensemble Feature Selection Algorithm for Classification

In the last few years, ensemble learning has received more interest primarily for the task of classification. It is based on the postulation that combining the output of multiple experts is better than the output of any individual expert. Ensemble feature selection may improve the performance of the learning algorithms and has the ability to obtain more stable and robust results. However, during the process of feature aggregation and selection, selected feature subset may contain high levels of inter-feature redundancy. To address this issue, a novel algorithm based on feature rank aggregation and graph theoretic technique for ensemble feature selection (R-GEFS) with the fusion of Pearson and Spearman correlation metrics is proposed. The method works by aggregation of the profile of preferences of five feature rankers as the base feature selectors. Then similar features are grouped into clusters using graph theoretic approach. The most representative feature strongly co-related to target decision classes is drawn from each cluster. The efficiency and effectiveness of the R-GEFS algorithm are evaluated through an empirical study. Extensive experiments on 15 diverse benchmark datasets are carried out to compare R-GEFS with seven state-of-the-art feature selection models with respect to four popular classifiers, namely decision tree, k nearest neighbor, random forest, and support vector machine. The proposed method turns out to be effective by selecting smaller feature subsets with lesser computational complexities and it assists in increasing the classification accuracy.