Reviewerly/exHarmony huggingface dataset:
Secure sparse watermarking on DWT-SVD for digital images

This paper suggests a sparse watermark image embedding scheme on the singular values of the pre-selected wavelet sub-band coefficients of the digital images. Dictionary learning (DL) method is used to make the watermark image sparse. Then a watermark decoder is designed based on the theory of compressed sensing (CS) in the framework of alternating direction method of multiplier (ADMM). The integration of DL and CS offers two benefits, firstly DL combined with singular value decomposition (SVD), enables protection against false positive detection (FPD) where the sparse watermark enhances embedding capacity at high imperceptibility of the hidden data. Simulation results show high visual quality for the watermarked images (PSNR = 60.45 dB, SSIM = 0.9997 and FSIM = 0.9979) and robustness (NCC value for the decoded watermark is ∼ 0.9) against various image processing operations including an unauthorized decoding - a typical value of 85% CS measurements lead to a watermarked image results in a FPD value of 0.42 that shows a visually indistinguishable watermark.
VR in customer-centered marketing: Purpose-driven design

Despite the annual growth of the virtual reality (VR) market, many businesses—especially small and medium-sized ones—have been slow to adopt VR in their marketing. As many forms of VR exist, it is important to understand how they can be used to achieve specific marketing objectives. Unfortunately, minimal guidance related to VR production for marketing purposes exists. To address this, our article proposes guidelines for strategically deploying VR marketing in customer-centered businesses. This research also builds on extant VR marketing literature to consider VR marketing objectives for each stage of the consumer journey and offers concrete recommendations for selecting and designing appropriate VR applications. The proposed guidelines are further exemplified by two small-scale cases from the Scottish tourism industry.
Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory

Digitization is changing our world, creating innovative finance channels and emerging technology such as cryptocurrencies, which are applications of blockchain technology. However, cryptocurrency price volatility is one of this technology’s main trade-offs. In this paper, we explore a time series analysis using deep learning to study the volatility and to understand this behavior. We apply a long short-term memory model to learn the patterns within cryptocurrency close prices and to predict future prices. The proposed model learns from the close values. The performance of this model is evaluated using the root-mean-squared error and by comparing it to an ARIMA model.
Online robot guidance and navigation in non-stationary environment with hybrid Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) provides an option to solve complex guidance and navigation problems with high-dimensional spaces, multiple objectives, and a large number of states and actions. The current HRL methods often use the same or similar reinforcement learning methods within one application so that multiple objectives can be easily combined. Since there is not a single learning method that can benefit all targets, hybrid Hierarchical Reinforcement Learning (hHRL) was proposed to use various methods to optimize the learning with different types of information and objectives in one application. The previous hHRL method, however, requires manual task-specific designs, which involves engineers' preferences and may impede its transfer learning ability. This paper, therefore, proposes a systematic online guidance and navigation method under the framework of hHRL, which generalizes training samples with a function approximator, decomposes the state space automatically, and thus does not require task-specific designs. The simulation results indicate that the proposed method is superior to the previous hHRL method, which requires manual decomposition, in terms of the convergence rate and the learnt policy. It is also shown that this method is generally applicable to non-stationary environments changing over episodes and over time without the loss of efficiency even with noisy state information.
Causal network inference in a dam system and its implications on feature selection for machine learning forecasting

A fundamental goal across many research fields is to explain possible mechanisms behind a phenomenon and infer the correct causal relationships between variables. In this work, we employed various causal inference methods to derive the causal network of a dam system from time series data. Here we explored the lagged effects of water levels in two dams, climate and weather variables, and domestic and agricultural water demands on each other. Among the methods considered, we demonstrated that convergent cross mapping (CCM), a method for inferring causal relationships in complex systems using time series data, is the most consistent with an actual dam system: (1) causal links were consistent with the direction of the physical flow of water, (2) the effects of climate and weather variables were successfully captured, (3) the time lags shed light on the dynamics of the dam system and possibly reflected planning schedules which are not explicit in the data. Our results captured both intuitive and counter-intuitive causal links, some of which were validated by domain experts. Using the resulting causal links to pre-select the input variables in machine learning-based forecasting models significantly reduces the prediction errors compared to using randomly selected features. Specifically, the best reduction in MAE is 4.2–4.4 meters, which corresponds to an improvement of 2.8–3.0 times lower than using random selection of features. CCM was also able to filter the top 20 significant predictors, where further addition of other variables yielded negligible improvement in the MAE. This is the first work that demonstrates successful inference of time-lagged causal network of endogenous and exogenous variables in a dam system.
Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room

The fine-grained localization of clinicians in the operating room (OR) is a key component to design the new generation of OR support systems. Computer vision models for person pixel-based segmentation and body-keypoints detection are needed to better understand the clinical activities and the spatial layout of the OR. This is challenging, not only because OR images are very different from traditional vision datasets, but also because data and annotations are hard to collect and generate in the OR due to privacy concerns. To address these concerns, we first study how joint person pose estimation and instance segmentation can be performed on low resolutions images with downsampling factors from 1x to 12x. Second, to address the domain shift and the lack of annotations, we propose a novel unsupervised domain adaptation method, called AdaptOR, to adapt a model from an in-the-wild labeled source domain to a statistically different unlabeled target domain. We propose to exploit explicit geometric constraints on the different augmentations of the unlabeled target domain image to generate accurate pseudo labels and use these pseudo labels to train the model on high- and low-resolution OR images in a self-training framework. Furthermore, we propose disentangled feature normalization to handle the statistically different source and target domain data. Extensive experimental results with detailed ablation studies on the two OR datasets MVOR+ and TUM-OR-test show the effectiveness of our approach against strongly constructed baselines, especially on the low-resolution privacy-preserving OR images. Finally, we show the generality of our method as a semi-supervised learning (SSL) method on the large-scale COCO dataset, where we achieve comparable results with as few as 1% of labeled supervision against a model trained with 100% labeled supervision. Code is available at https://github.com/CAMMA-public/HPE-AdaptOR.
Contrast-Enhanced Semi-supervised Text Classification with Few Labels

Traditional text classification requires thousands of annotated data or an additional Neural Machine Translation (NMT) system, which are expensive to obtain in real applications. This paper presents a Contrast-Enhanced Semi-supervised Text Classification (CEST) framework under label-limited settings without incorporating any NMT systems. We propose a certainty-driven sample selection method and a contrast-enhanced similarity graph to utilize data more efficiently in self-training, alleviating the annotation-starving problem. The graph imposes a smoothness constraint on the unlabeled data to improve the coherence and the accuracy of pseudo-labels. Moreover, CEST formulates the training as a “learning from noisy labels” problem and performs the optimization accordingly. A salient feature of this formulation is the explicit suppression of the severe error propagation problem in conventional semi-supervised learning. With solely 30 labeled data per class for both training and validation dataset, CEST outperforms the previous state-of-the-art algorithms by 2.11% accuracy and only falls within the 3.04% accuracy range of fully-supervised pre-training language model fine-tuning on thousands of labeled data.
Axiomatization of Aggregates in Answer Set Programming

The paper presents a characterization of logic programs with aggregates based on many-sorted generalization of operator SM that refers neither to grounding nor to fixpoints. This characterization introduces new symbols for aggregate operations and aggregate elements, whose meaning is fixed by adding appropriate axioms to the result of the SM transformation. We prove that for programs without positive recursion through aggregates our semantics coincides with the semantics of the answer set solver Clingo.
Measurement method and recent progress of vision-based deflection measurement of bridges: a technical review

The deflection that can reflect the vertical stiffness of a bridge plays an important role in the structural evaluation and health monitoring of bridges. In the past 20 years, the bridge deflection measurement methods based on computer vision and photogrammetry have been gradually applied to the field measurement due to the advantages of noncontact measurement, simple experimental setup, and easy installation. The technical research progress of vision-based bridge deflection measurement is reported from four aspects: basic principles, measurement methods, influencing factors, and applications. Basic principles mainly include camera calibration, three-dimensional (3D) stereo vision, photogrammetry, feature detection, and matching. For measurement methods, the single-camera two-dimensional measurement, the dual-camera 3D measurement, the quasistatic measurement based on photogrammetry, the multipoint dynamic measurement based on the displacement-relay videometrics and the deflection measurement based on UAV platform are introduced, respectively. In the section of influencing factors, this part summarizes the work of many researchers on the effects of camera imaging factors, calibration factors, algorithm factors, and environmental factors on measurement results. The field measurement results at different measurement distances and measurement accuracy based on these are presented in terms of applications. Finally, the future development trends of vision-based bridge deflection measurement are expected.
DFR-ST: Discriminative feature representation with spatio-temporal cues for vehicle re-identification

Vehicle re-identification (re-ID) aims to discover and match the target vehicles from a gallery image set taken by different cameras on a wide range of road networks. It is crucial for lots of applications such as security surveillance and traffic management. The remarkably similar appearances of distinct vehicles and the significant changes in viewpoints and illumination conditions pose grand challenges to vehicle re-ID. Conventional solutions focus on designing global visual appearances without sufficient consideration of vehicles' spatio-temporal relationships in different images. This paper proposes a discriminative feature representation with spatio-temporal clues (DFR-ST) for vehicle re-ID. It is capable of building robust features in the embedding space by involving appearance and spatio-temporal information. The proposed DFR-ST constructs an appearance model for a multi-grained visual representation by a two-stream architecture and a spatio-temporal metric to provide complementary information based on this multi-modal information. Experimental results on four public datasets demonstrate DFR-ST outperforms the state-of-the-art methods, which validates the effectiveness of the proposed method.
FAULT-INDUCED GOLD SATURATION OF A SINGLE AURIFEROUS FLUID IS A KEY PROCESS FOR OROGENIC GOLD DEPOSIT FORMATION

Abstract In orogenic gold deposits, the mechanism by which a hydrothermal fluid precipitates gold in laminated quartz veins remains elusive. The Kanowna Belle deposit hosts gold mineralization in structurally controlled quartz-pyrite laminated veins that cut volcano-sedimentary and granitic rocks of the Kalgoorlie terrane, Australia. Veins show microtextural evidence for multiphased emplacement, corresponding to three distinct pyrite growth zones in which Au enrichment in the pyrite is attributed to high fluid influx. To monitor the chemical and isotopic evolution of the auriferous fluid leading to gold precipitation, we combine in situ multiple sulfur isotope analyses with trace element composition of gold-bearing pyrite growth zones: cores (Au ≤149 ppm; δ34S –3.3 to +4.2‰; As ≤2.5 wt %; Ni ≤4,022 ppm; Te ≤416 ppm), Au-rich oscillatory zoned mantles (Au ≤2,251 ppm; δ34S –8.4 to +0.1‰; As ≤4.5 wt %; Ni ≤1,111 ppm; Te ≤829 ppm), and rims (Au ≤264 ppm; δ34S –6.0 to +2.8‰; As ≤1.4 wt %; Ni ≤2,113 ppm; Te ≤229 ppm). The positive and consistent Δ33S of each zone (Δ33S = +0.3 ± 0.2‰; n = 160) indicates that one single reservoir was tapped during the evolution of the mineralizing system. The gold-rich pyrite mantle zones precipitated from a fluid undergoing SO42−/H2S fluctuations associated with phase separation due to rapid and repeated releases in fluid pressure. This study demonstrates that the “fault-valve” process applies a first-order control on the precipitation of gold from a single auriferous fluid reservoir.
Predicting sea surface temperatures with coupled reservoir computers

Abstract. Sea surface temperature (SST) is a key factor in understanding the greater climate of the Earth, and it is also an important variable when making weather predictions. Methods of machine learning have become ever more present and important in data-driven science and engineering, including in important areas for Earth science. Here, we propose an efficient framework that allows us to make global SST forecasts using a coupled reservoir computer method that we have specialized to this domain, allowing for template regions that accommodate irregular coastlines. Reservoir computing is an especially good method for forecasting spatiotemporally complex dynamical systems, as it is a machine learning method that, despite many randomly selected weights, is highly accurate and easy to train. Our approach provides the benefit of a simple and computationally efficient model that is able to predict SSTs across the entire Earth's oceans. The results are demonstrated to generally follow the actual dynamics of the system over a forecasting period of several weeks.
Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process

Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy. However, UAV devices are usually limited in terms of resources such as battery, memory, and CPU. Some of the UAV devices may not be able to build a local learning models due to their resources capacity. Hence, there is a great need to select the adequate UAVs at each FL round, that are able to build a local DL model based on their resource capacities. In this paper, we design a novel and S-MEC-enabled framework that optimizes the selection of UAV participants at each FL training round, named FedSel. FedSel considers the available UAVs along with their resource capacities, in terms of energy, CPU, and memory, to determine which UAV device is able to participant in the FL process. Thus, we formulate the UAV selection problem as an Integer Linear Program, which considers the aforementioned constraints. We also prove that this problem is NP-hard, and suggest a Tabu Search (TS) metaheuristic-based approach to resolve it. Moreover, FedSel is built on top of blockchain technology, in order to ensure a secure selection of UAV participants, and hence building reliable FL-based models. Simulation results validate the efficiency of our FedSel scheme in balancing computational load among available UAVs and optimizing the UAV selection process.
Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained learning aims to classify a query image into one of a set of support categories with fine-grained differences. Although learning different objects' local differences via Deep Neural Networks has achieved success, how to exploit the query-support cross-image object semantic relations in Transformer-based architecture remains under-explored in the few-shot fine-grained scenario. In this work, we propose a Transformer-based double-helix model, namely HelixFormer, to achieve the cross-image object semantic relation mining in a bidirectional and symmetrical manner. The HelixFormer consists of two steps: 1) Relation Mining Process (RMP) across different branches, and 2) Representation Enhancement Process (REP) within each individual branch. By the designed RMP, each branch can extract fine-grained object-level Cross-image Semantic Relation Maps (CSRMs) using information from the other branch, ensuring better cross-image interaction in semantically related local object regions. Further, with the aid of CSRMs, the developed REP can strengthen the extracted features for those discovered semantically-related local regions in each branch, boosting the model's ability to distinguish subtle feature differences of fine-grained objects. Extensive experiments conducted on five public fine-grained benchmarks demonstrate that HelixFormer can effectively enhance the cross-image object semantic relation matching for recognizing fine-grained objects, achieving much better performance over most state-of-the-art methods under 1-shot and 5-shot scenarios.
NeuralPassthrough: Learned Real-Time View Synthesis for VR

Virtual reality (VR) headsets provide an immersive, stereoscopic visual experience, but at the cost of blocking users from directly observing their physical environment. Passthrough techniques are intended to address this limitation by leveraging outward-facing cameras to reconstruct the images that would otherwise be seen by the user without the headset. This is inherently a real-time view synthesis challenge, since passthrough cameras cannot be physically co-located with the user's eyes. Existing passthrough techniques suffer from distracting reconstruction artifacts, largely due to the lack of accurate depth information (especially for near-field and disoccluded objects), and also exhibit limited image quality (e.g., being low resolution and monochromatic). In this paper, we propose the first learned passthrough method and assess its performance using a custom VR headset that contains a stereo pair of RGB cameras. Through both simulations and experiments, we demonstrate that our learned passthrough method delivers superior image quality compared to state-of-the-art methods, while meeting strict VR requirements for real-time, perspective-correct stereoscopic view synthesis over a wide field of view for desktop-connected headsets.
An MLP-based Algorithm for Efficient Contrastive Graph Recommendations

Graph-based recommender systems (GBRSs) have achieved promising performance by incorporating the user-item bipartite graph using the Graph Neural Network (GNN). Among GBRSs, the information from each user and item's multi-hop neighbours is effectively conveyed between nodes through neighbourhood aggregation and message passing. Although effective, existing neighbourhood information aggregation and passing functions are usually computationally expensive. Motivated by the emerging contrastive learning technique, we design a simple neighbourhood construction method in conjunction with the contrastive objective function to simulate the neighbourhood information processing of GNN. In addition, we propose a simple algorithm based on Multilayer Perceptron (MLP) for learning users and items' representations with extra non-linearity while lowering computational burden compared with multi-layers GNNs. Our extensive empirical experiments on three public datasets demonstrate that our proposed model, i.e. MLP-CGRec, can reduce the GPU memory consumption and training time by up to 24.0% and 33.1%, respectively, without significantly degenerating the recommendation accuracy in comparison with competitive baselines.
Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer Network

Next Point-of-Interest (POI) recommendation is a pivotal issue for researchers in the field of location-based social networks. While many recent efforts show the effectiveness of recurrent neural network-based next POI recommendation algorithms, several important challenges have not been well addressed yet: (i) The majority of previous models only consider the dependence of consecutive visits, while ignoring the intricate dependencies of POIs in traces; (ii) The nature of hierarchical and the matching of sub-sequence in POI sequences are hardly model in prior methods; (iii) Most of the existing solutions neglect the interactions between two modals of POI and the density category. To tackle the above challenges, we propose an auto-correlation enhanced multi-modal Transformer network (AutoMTN) for the next POI recommendation. Particularly, AutoMTN uses the Transformer network to explicitly exploits connections of all the POIs along the trace. Besides, to discover the dependencies at the sub-sequence level and attend to cross-modal interactions between POI and category sequences, we replace self-attention in Transformer with the auto-correlation mechanism and design a multi-modal network. Experiments results on two real-world datasets demonstrate the ascendancy of AutoMTN contra state-of-the-art methods in the next POI recommendation.