Reviewerly/exHarmony huggingface dataset:
Trace elements in sulfides from the Maozu Pb-Zn deposit, Yunnan Province, China: Implications for trace-element incorporation mechanisms and ore genesis

Abstract The Sichuan-Yunnan-Guizhou Pb-Zn metallogenic province (SYGMP) is an important region for Pb-Zn resources in China. However, considerable controversy remains as to whether the Pb-Zn deposits are Mississippi Valley Type (MVT). The Maozu deposit, a typical example of the carbonate-hosted Pb-Zn deposits in the SYGMP, occurs in the late Ediacaran Dengying Formation and its ore bodies are divided into three types: lower layer (LL), vein layer (VL), and upper layer (UL) ore bodies based on their spatial relationship. In this study, laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) was used to systematically analyze the trace-element compositions of sphalerite and galena in these three ore bodies. The results show that sphalerite is characterized by Cd and Ge enrichment; Fe, Mn, and Co depletion; and local In and Sn enrichment. Most of these elements likely appear as solid solutions in sphalerite and show a wide compositional variation, which is probably related to the medium- and low-temperature mixing of the ore-forming fluids. The local enrichment of In and Sn is likely attributed to the long-distance migration of ore-forming fluids through In-Sn-bearing volcaniclastic rocks. In vs. Sn and (Cu + Sb) vs. (Ag + Ge) show strong correlations and similar element distribution in the mapped images, indicating that these elements may be incorporated into sphalerite via a coupled substitution for Zn as 2In3+ + Sn4+ + 2☐ ↔ 5Zn2+ (☐ = vacancies) and 4(Cu+ + Sb3+) + (Ge4+ + 2Ag+) + 2☐ ↔ 13Zn2+. Galena is enriched in Ag and Sb with minor Cd and Se and depleted in Bi, and most of the elements may occur as solid solutions. Ag vs. Sb in galena displays a strong positive correlation, implying the coupled substitution of Ag+ + Sb3+ ↔ 2Pb2+. Notably, the majority of the trace-element concentrations gradually decrease in the order LL → UL except Fe, Co, Cu, and Ge, while Fe, In, and Sn in sphalerite and Ag and Sb in galena have the highest concentration in the VL, indicating that the VL is a secondary migration channel for the ore-forming fluids. Furthermore, the trace-element compositions of the sulfides in the Maozu Pb-Zn deposit are consistent with the typical MVT deposit (hosted in the carbonate sequence) but are markedly different from sedimentary exhalative (SEDEX), volcanogenic massive sulfide (VMS) and skarn-type deposits. Based on these results, as well as the geological and geochemical characteristics of the deposit, the Maozu Pb-Zn deposit is an MVT deposit.
CollaboVR: A Reconfigurable Framework for Creative Collaboration in Virtual Reality

Writing or sketching on whiteboards is an essential part of collaborative discussions in business meetings, reading groups, design sessions, and interviews. However, prior work in collaborative virtual reality (VR) systems has rarely explored the design space of multi-user layouts and interaction modes with virtual whiteboards. In this paper, we present CollaboVR, a reconfigurable framework for both co-located and geographically dispersed multi-user communication in VR. Our system unleashes users’ creativity by sharing freehand drawings, converting 2D sketches into 3D models, and generating procedural animations in real-time. To minimize the computational expense for VR clients, we leverage a cloud architecture in which the computational expensive application (Chalktalk) is hosted directly on the servers, with results being simultaneously streamed to clients. We have explored three custom layouts – integrated, mirrored, and projective – to reduce visual clutter, increase eye contact, or adapt different use cases. To evaluate CollaboVR, we conducted a within-subject user study with 12 participants. Our findings reveal that users appreciate the custom configurations and real-time interactions provided by CollaboVR. We have open sourced CollaboVR at https://github.com/snowymo/CollaboVR to facilitate future research and development of natural user interfaces and real-time collaborative systems in virtual and augmented reality.
Renovating Parsing R-CNN for Accurate Multiple Human Parsing

Multiple human parsing aims to segment various human parts and associate each part with the corresponding instance simultaneously. This is a very challenging task due to the diverse human appearance, semantic ambiguity of different body parts, and complex background. Through analysis of multiple human parsing task, we observe that human-centric global perception and accurate instance-level parsing scoring are crucial for obtaining high-quality results. But the most state-of-the-art methods have not paid enough attention to these issues. To reverse this phenomenon, we present Renovating Parsing R-CNN (RP R-CNN), which introduces a global semantic enhanced feature pyramid network and a parsing re-scoring network into the existing high-performance pipeline. The proposed RP R-CNN adopts global semantic representation to enhance multi-scale features for generating human parsing maps, and regresses a confidence score to represent its quality. Extensive experiments show that RP R-CNN performs favorably against state-of-the-art methods on CIHP and MHP-v2 datasets. Code and models are available at https://github.com/soeaver/RP-R-CNN.
Novel predictive model to improve the accuracy of collaborative filtering recommender systems

The recommendation problem involves the prediction of a set of items that maximize the utility for users. Numerous factors, such as the filtering method and similarity measure, affect the prediction accuracy. We propose a novel prediction mechanism that can be applied to collaborative filtering recommender systems. This prediction mechanism consists of a novel adaptable predictive model, called inheritance-based prediction (INH-BP), and a suitable heuristic search algorithm. INH-BP enables the customization of the predictor to suit the user context. It helps in defining a user interest print (UIP) matrix and employs an optimization algorithm such as a genetic algorithm. The UIP matrix should reflect the degree of user satisfaction based on the concept levels instead of the instance level. The optimization algorithm is used to determine the optimal predictor for each user. A set of experiments were conducted to compare INH-BP with Resnick’s well-known adjusted weighted sum. Two benchmark datasets, MovieLens-100K and MovieLens-Last, were used. Both prediction methods were employed using different collaborative filtering techniques. The results demonstrate the superiority of INH-BP and its capability to achieve an accurate prediction irrespective of the number of k-neighbors and their quality. In addition, the results show that INH-BP alleviates the cold start and sparsity issues.
Temporal Coherence or Temporal Motion: Which Is More Critical for Video-Based Person Re-identification?

Video-based person re-identification aims to match pedestrians with the consecutive video sequences. While a rich line of work focuses solely on extracting the motion features from pedestrian videos, we show in this paper that the temporal coherence plays a more critical role. To distill the temporal coherence part of video representation from frame representations, we propose a simple yet effective Adversarial Feature Augmentation (AFA) method, which highlights the temporal coherence features by introducing adversarial augmented temporal motion noise. Specifically, we disentangle the video representation into the temporal coherence and motion parts and randomly change the scale of the temporal motion features as the adversarial noise. The proposed AFA method is a general lightweight component that can be readily incorporated into various methods with negligible cost. We conduct extensive experiments on three challenging datasets including MARS, iLIDS-VID, and DukeMTMC-VideoReID, and the experimental results verify our argument and demonstrate the effectiveness of the proposed method.
Recognition of human activities for wellness management using a smartphone and a smartwatch: A boosting approach

Mobile health applications are considered to be powerful tools for activity-based wellness management. With the availability of multimodal sensors in smart devices used in our daily lives, it is possible to track human activity and deliver context-aware wellness services. The embedded sensors in naturally used devices such as smartphones, smartwatches, and wearables contain rich information that can be integrated for human activity recognition. Our research demonstrates how powerful boosting algorithms can extract knowledge for human activity classification in a real-life setting. Our results show that boosting classifiers outperform traditional machine learning classifiers in the detection of basic human activities such as walking, standing, sitting, exercise, and sleeping. Further, we perform feature engineering to compare the potential of a smartphone and a smartwatch in activity detection. Our feature engineering strategy provides directions about the selection of sensor features for improvement in classification of basic human activities. The theoretical and practical implications of this research for activity-based wellness management are also discussed.
Detection of Anomalies in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System

Industry 4.0 will make manufacturing processes smarter but this smartness requires more environmental awareness, which in case of Industrial Internet of Things, is realized by the help of sensors. This article is about industrial pharmaceutical systems and more specifically, water purification systems. Purified water which has certain conductivity is an important ingredient in many pharmaceutical products. Almost every pharmaceutical company has a water purifying unit as a part of its interdependent systems. Early detection of faults right at the edge can significantly decrease maintenance costs and improve safety and output quality, and as a result, lead to the production of better medicines. In this article, with the help of a few sensors and data mining approaches, an anomaly detection system is built for CHRIST Osmotron water purifier. This is a practical research with real-world data collected from SinaDarou Labs Co. Data collection was done by using six sensors over two-week intervals before and after system overhaul. This gave us normal and faulty operation samples. Given the data, we propose two anomaly detection approaches to build up our edge fault detection system. The first approach is based on supervised learning and data mining, e.g., by support vector machines. However, since we cannot collect all possible faults data, an anomaly detection approach is proposed based on normal system identification which models the system components by artificial neural networks. Extensive experiments are conducted with the data set generated in this study to show the accuracy of the data-driven and model-based anomaly detection methods.
PrGCN: Probability prediction with graph convolutional network for person re-identification

Robust similarity measurement is an important issue for person re-identification (ReID). Most existing ReID models estimate the similarity between query and gallery images by computing their Euclidean distances while ignoring the rich context information contained in the image space. In this paper, we propose a graph convolutional network (GCN) based method to improve the similarity measurement in ReID, which regards the ReID task as a prediction problem of the link probability between node pairs. Our method is named as PrGCN (Probability GCN), in which each person is regarded as an instance node. Firstly, an Instance Centered Sub-graphs (ICS) is constructed for each instance node to depict its rich local context information. Secondly, the constructed ICS is input to a GCN to infer and predict the link probability of node pairs, followed by a similarity ranking between the query and gallery images according to the predicted probabilities. Extensive experiments show that the proposed method improves the mAP and Top-1 accuracy of ReID significantly, yielding better or comparable results to the state-of-the-art methods on various benchmarks (Market1501, DukeMTMC-ReID and CUHK03). In addition, we validate that the proposed PrGCN can be easily embedded into other deep learning architectures to replace Euclidean distance metric and achieve significant performance improvements.
Privacy-Preserving Contact Tracing and Public Risk Assessment Using Blockchain for COVID-19 Pandemic

Due to the number of confirmed cases and casualties of the new COVID-19 virus diminishing day after day, several countries around the world are discussing how to return to the new normal way of life. In order to keep the spread of the disease under control and avoid a second wave of infection, one alternative being considered is the utilization of contact tracing. However, despite several alternatives being available, contact tracing still faces issues in terms of maintaining user privacy and security, making its mass adoption quite difficult. Based on that, a novel framework for contact tracing using blockchain as its infrastructure is presented. By integrating blockchain with contact tracing applications, user privacy can be guaranteed, while also providing people and government bodies with a complete public view of all confirmed cases. Moreover, we also investigate how public locations can aid in the contact tracing process by measuring the risk of exposure to COVID-19 to the general public and advertising it in a blockchain. By doing so, these locations can effectively report potential infection risks, while also guaranteeing privacy and trustworthiness in the information. Lastly, numerical results are shown in different scenarios and conclusions are drawn.
TENet: Triple Excitation Network for Video Salient Object Detection

In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations. These excitation mechanisms are designed following the spirit of curriculum learning and aim to reduce learning ambiguities at the beginning of training by selectively exciting feature activations using ground truth. Then we gradually reduce the weight of ground truth excitations by a curriculum rate and replace it by a curriculum complementary map for better and faster convergence. In particular, the spatial excitation strengthens feature activations for clear object boundaries, while the temporal excitation imposes motions to emphasize spatio-temporal salient regions. Spatial and temporal excitations can combat the saliency shifting problem and conflict between spatial and temporal features of VSOD. Furthermore, our semi-curriculum learning design enables the first online refinement strategy for VSOD, which allows exciting and boosting saliency responses during testing without re-training. The proposed triple excitations can easily plug in different VSOD methods. Extensive experiments show the effectiveness of all three excitation methods and the proposed method outperforms state-of-the-art image and video salient object detection methods.
Self-paced and self-consistent co-training for semi-supervised image segmentation

Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-supervised segmentation with a self-paced and self-consistent co-training method. To help distillate information from unlabeled images, we first design a self-paced learning strategy for co-training that lets jointly-trained neural networks focus on easier-to-segment regions first, and then gradually consider harder ones.This is achieved via an end-to-end differentiable loss inthe form of a generalized Jensen Shannon Divergence(JSD). Moreover, to encourage predictions from different networks to be both consistent and confident, we enhance this generalized JSD loss with an uncertainty regularizer based on entropy. The robustness of individual models is further improved using a self-ensembling loss that enforces their prediction to be consistent across different training iterations. We demonstrate the potential of our method on three challenging image segmentation problems with different image modalities, using small fraction of labeled data. Results show clear advantages in terms of performance compared to the standard co-training baselines and recently proposed state-of-the-art approaches for semi-supervised segmentation
On the c-differential uniformity of certain maps over finite fields

We give some classes of power maps with low c-differential uniformity over finite fields of odd characteristic, for $$c=-1$$ . Moreover, we give a necessary and sufficient condition for a linearized polynomial to be a perfect c-nonlinear function and investigate conditions when perturbations of perfect c-nonlinear (or not) function via an arbitrary Boolean or p-ary function is perfect c-nonlinear. In the process, we obtain a class of polynomials that are perfect c-nonlinear for all $$c\ne 1$$ , in every characteristic. The affine, extended affine and CCZ-equivalence is also looked at, as it relates to c-differential uniformity.
A multi-scale image watermarking based on integer wavelet transform and singular value decomposition

Image watermarking technique is one of effective solutions to protect copyright, and it is applied to a variety of information security application domains. It needs to meet four requirements of imperceptibility, robustness, capacity and security. A multi-scale and secure image watermarking method is proposed in this work, which is based on the Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD). Four IWT sub-bands are firstly obtained after 1-level IWT on the host image, and the corresponding singular diagonal matrices of four sub-bands can be obtained using SVD. Then, each singular diagonal matrix is divided into four non-overlapping sections in terms of the size of embedding watermark. Particularly, the size of upper left part is same as the size of watermark. The watermark can be directly embedded into four upper left parts afterwards by multiplying different scaling factors to complete the final watermarking operation. Especially, a novel optimized authentication mechanism is designed to resolve the false positive problem, which exists in the SVD-based watermarking algorithms. In addition, three-dimensional optimal mapping algorithm is proposed to search the optimal scaling factors through a novel objective evaluation function, and it can significantly improve the imperceptibility and robustness. The experimental test and comparison analysis illustrate that the proposed watermark scheme demonstrates a high imperceptibility with peak signal to noise ratio values of 45 dB and strong robustness with average normalized correlation values of 0.92.
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data

Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization.To mitigate this issue, we propose a regularized fine-tuning method.Our method introduces two types of regularization for better calibration: (1) On-manifold regularization, which generates pseudo on-manifold samples through interpolation within the data manifold.Augmented training with these pseudo samples imposes a smoothness regularization to improve in-distribution calibration.(2) Off-manifold regularization, which encourages the model to output uniform distributions for pseudo off-manifold samples to address the over-confidence issue for OOD data.Our experiments demonstrate that the proposed method outperforms existing calibration methods for text classification in terms of expectation calibration error, misclassification detection, and OOD detection on six datasets.Our code can be found at https://github.com/Lingkai-Kong/
Tucker decomposition-based temporal knowledge graph completion

Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the knowledge graphs. To enrich the existing knowledge graphs, recent years have witnessed that many algorithms for link prediction and knowledge graphs embedding have been designed to infer new facts. But most of these studies focus on the static knowledge graphs and ignore the temporal information which reflects the validity of knowledge. Developing the model for temporal knowledge graphs completion is an increasingly important task. In this paper, we build a new tensor decomposition model for temporal knowledge graphs completion inspired by the Tucker decomposition of order-4 tensor. Furthermore, to further improve the basic model performance, we provide three kinds of methods including cosine similarity, contrastive learning, and reconstruction-based to incorporate the prior knowledge into the proposed model. Because the core tensor contains a large number of parameters on the proposed model, thus we present two embedding regularization schemes to avoid the over-fitting problem. By combining these two kinds of regularization with the proposed model, our model outperforms baselines with an explicit margin on three temporal datasets (i.e. ICEWS2014, ICEWS05-15, GDELT).
Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in Variational Autoencoder Based Voice Conversion

An effective approach for voice conversion (VC) is to disentangle linguistic content from other components in the speech signal. The effectiveness of variational autoencoder (VAE) based VC (VAE-VC), for instance, strongly relies on this principle. In our prior work, we proposed a cross-domain VAE-VC (CDVAE-VC) framework, which utilized acoustic features of different properties, to improve the performance of VAE-VC. We believed that the success came from more disentangled latent representations. In this paper, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech. More specifically, we first investigate the effectiveness of incorporating the generative adversarial networks (GANs) with CDVAE-VC. Then, we consider the concept of domain adversarial training and add an explicit constraint to the latent representation, realized by a speaker classifier, to explicitly eliminate the speaker information that resides in the latent code. Experimental results confirm that the degree of disentanglement of the learned latent representation can be enhanced by both GANs and the speaker classifier. Meanwhile, subjective evaluation results in terms of quality and similarity scores demonstrate the effectiveness of our proposed methods.