Reviewerly/exHarmony huggingface dataset:
Performance of predictive supervised classification models of trace elements in magnetite for mineral exploration

Magnetite is a reliable indicator mineral for exploration because it records petrogenetic processes and discriminate deposit types. Binary discriminant diagrams have limits to accurately predict deposit types (prediction accuracy ~40%). This study aims to determine the best predictive supervised multivariate classification method using the geochemical composition of magnetite in order to provide a model usable by industry and government for mineral exploration. After screening a comprehensive database of ~30 k magnetite analyses, ~17 k observations are selected for our study. These data are from 303 different deposits that belong to 9 major deposit types (BIF, Fe-Ti, IOCG, IOA, Ni-Cu-PGE, Porphyry, VMS, Skarn, V) and a varied class of non-mineralized rocks (Country rocks). We tested the three most promising supervised machine learning algorithms on our dataset (Naive Bayes, K-Nearest Neighbor, Random Forest) with 2 open source statistical platforms: Orange and R. The Random Forest (RF) algorithm yield the best predictive outcome on untransformed data with prediction accuracies of 0.80 with Orange and 0.81 with R. We also tested our RF model on three case studies: 1) IOCG-like deposits, 2) porphyry Cu-Au and Cu skarn, and 3) Scandinavian Ni-Cu-PGE deposits. Our model was able to effectively predict the deposit type for the first two case studies to the large family of Porphyry/IOCG/Skarn. For the 3rd case study, almost 61% of the observations were correctly identified as belonging to Ni-Cu-PGE deposits. Our RF model is therefore accurate enough to be used with confidence for mineral exploration.
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search

Modeling user's historical feedback is essential for Click-Through Rate Prediction in personalized search and recommendation. Existing methods usually only model users' positive feedback information such as click sequences which neglects the context information of the feedback. In this paper, we propose a new perspective for context-aware users' behavior modeling by including the whole page-wisely exposed products and the corresponding feedback as contextualized page-wise feedback sequence. The intra-page context information and inter-page interest evolution can be captured to learn more specific user preference. We design a novel neural ranking model RACP(Recurrent Attention over Contextualized Page sequence), which utilizes page-context aware attention to model the intra-page context. A recurrent attention process is used to model the cross-page interest convergence evolution as denoising the interest in the previous pages. Experiments on public and real-world industrial datasets verify our model's effectiveness.
Multi-modal Interactive Video Retrieval with Temporal Queries

This paper presents the version of vitrivr participating at the Video Browser Showdown (VBS) 2022. vitrivr already supports a wide range of query modalities, such as color and semantic sketches, OCR, ASR and text embedding. In this paper, we briefly introduce the system, then describe our new approach to queries specifying temporal context, ideas for color-based sketches in a competitive retrieval setting and a novel approach to pose-based queries.
Selecting Privacy-Enhancing Technologies for Managing Health Data Use

Privacy protection for health data is more than simply stripping datasets of specific identifiers. Privacy protection increasingly means the application of privacy-enhancing technologies (PETs), also known as privacy engineering. Demands for the application of PETs are not yet met with ease of use or even understanding. This paper provides a scope of the current peer-reviewed evidence regarding the practical use or adoption of various PETs for managing health data privacy. We describe the state of knowledge of PETS for the use and exchange of health data specifically and build a practical perspective on the steps needed to improve the standardization of the application of PETs for diverse uses of health data.
Partial multi-label learning based on sparse asymmetric label correlations

In many real-world applications, an instance from the training dataset of multi-label learning (MLL) often has some irrelevant labels. Traditional MLL and partial label learning (PLL) cannot deal with this problem very well. This has given rise to partial multi-label learning (PML). In this setting, it is very challenging to distinguish between the ground-truth labels and noisy labels. Most of the existing PML methods focus on identifying the ground-truth labels using label correlations, while they ignore the fact that the real label correlations often have been corrupted due to the noisy labels. Moreover, the existing PML methods usually consider the label correlations to be symmetric. However, in real-world applications, the label correlations are asymmetric. To address the above problems, we present partial multi-label learning based on sparse asymmetric label correlations (PML-SALC). PML-SALC integrates asymmetric label correlation learning and multi-label classifier learning into a unified framework. It utilizes the sparse asymmetric label correlation matrix to alleviate the negative influence of noisy labels to obtain label confidence. Moreover, PML-SALC models the relationship between the feature and label confidence, which makes the model smoother and more robust. The extensive experimental results show that the PML-SALC achieves state-of-the-art performance, which validates the effectiveness of the proposed method.
Visual Fidelity Effects on Expressive Self-avatar in Virtual Reality: First Impressions Matter

Figure 1: Study 1 used bespoke self-portrait avatars (C-1), while study 2 used standard avatars (A1-4) for a female and a male identity.The behaviour of two expressive avatars with realistic (A-1, A-3) and cartoonish (A-2, A-4) appearances were observed when they face a mirror and doing a presentation (C-2).The expressions driven by eye-tracking (B-1) and lip-tracking (B-2) were recreated based on the facial action units provided by VIVE Eye and Facial Tracking SDK (Sranipal) face blendShape document.Selected examples (V01-V37) are shown from 47 blendShapes in Study 2, the squeeze (V01+V13) is blended by two units.
Investigating Self-supervised Pretraining Frameworks for Pathological Speech Recognition

We investigate the performance of self-supervised pretraining frameworks on pathological speech datasets used for automatic speech recognition (ASR). Modern end-to-end models require thousands of hours of data to train well, but only a small number of pathological speech datasets are publicly available. A proven solution to this problem is by first pretraining the model on a huge number of healthy speech datasets and then fine-tuning it on the pathological speech datasets. One new pretraining framework called self-supervised learning (SSL) trains a network using only speech data, providing more flexibility in training data requirements and allowing more speech data to be used in pretraining. We investigate SSL frameworks such as the wav2vec 2.0 and WavLM models using different setups and compare their performance with different supervised pretraining setups, using two types of pathological speech, namely, Japanese electrolaryngeal and English dysarthric. Our results show that although SSL has shown success with minimally resourced healthy speech, we do not find this to be the case with pathological speech. The best supervised setup outperforms the best SSL setup by 13.9% character error rate in electrolaryngeal speech and 16.8% word error rate in dysarthric speech.
Federated Learning for Face Recognition with Gradient Correction

With increasing appealing to privacy issues in face recognition, federated learning has emerged as one of the most prevalent approaches to study the unconstrained face recognition problem with private decentralized data. However, conventional decentralized federated algorithm sharing whole parameters of networks among clients suffers from privacy leakage in face recognition scene. In this work, we introduce a framework, FedGC, to tackle federated learning for face recognition and guarantees higher privacy. We explore a novel idea of correcting gradients from the perspective of backward propagation and propose a softmax-based regularizer to correct gradients of class embeddings by precisely injecting a cross-client gradient term. Theoretically, we show that FedGC constitutes a valid loss function similar to standard softmax. Extensive experiments have been conducted to validate the superiority of FedGC which can match the performance of conventional centralized methods utilizing full training dataset on several popular benchmark datasets.
Do corporate characteristics improve sustainability disclosure Evidence from the UAE

The study examines the relationship between corporate attributes and ESG disclosure among selected companies in United Arab Emirates. The corporate attributes are company size, profitability, liquidity, industry sensitivity and leverage. The descriptive statistics reveals a maximum disclosure score of 78 and a minimum score of 32. The results of the regression analysis show that only company size, industry sensitivity and liquidity are significant determinants of ESG disclosure in UAE. Our findings show that firms with larger asset base and good degree of liquidity disclose more ESG information. However, firms in the service sector are more inclined to ESG disclosure than manufacturing firms. We recommend that the board of directors should make sustainability reporting a front burner in its corporate strategy. In addition, the regulatory authorities should legalise ESG disclosure among UAE firms. Finally, lenders of corporate funds should make certified ESG report as a major condition precedent to obtaining funds.
Selection of Significant Features of Objects in the Classification Data Processing

The formation of a characteristic space in classification problems can be divided into two stages: the choice of the initial description of objects and the formation of an informative description of objects on the basis of a reduction in the dimension of the space of the original description
Blockchain technology: challenges and opportunities for banks

This paper explores blockchain technology's potential implications for banks. This study is based on qualitative-based interviews with three professional bankers in different European banks that are dealing with the challenges of blockchain. The results indicate that blockchain technology is both a threat to and an opportunity for banks, providing support to the argument that blockchain is a disruptive technology that will create new opportunities for banks and new risks to their business. Nonetheless, the current lack of regulation and some technical limitations of the technology mean that blockchain is an opportunity for rather than a risk to banks. Blockchain technology may vastly improve banking processes' efficiency and therefore the products and services banks offer customers, particularly in areas such as lending, payments and capital markets.
Guided regularized random forest feature selection for smartphone based human activity recognition

Human activity recognition (HAR) is an eminent area of research due to its extensive scope of applications in remote health monitoring, sports, smart home, and many more. Smartphone-based HAR systems use high-dimensional sensor data to infer human physical activities. Researchers continuously endeavor to select pertinent and non-redundant features without compromising the classification accuracy. In this work, our aim is to build an efficient HAR model that not only extracts the most relevant features from the 3-axial accelerometer and gyroscope signal data but also enhances the classification accuracy of the HAR system, without data loss using time-frequency domain features. We propose a feature selection method based on guided regularized random forest (GRRF) to determine the most pertinent and non-redundant features to reduce the time to recognize the human activities efficiently. After selecting the most relevant features, a support vector machine (SVM) is used to identify various human physical activities. The UCI public dataset and a self-collected dataset are used to assess the generalization capability and performance of the proposed feature selection method. Eventually, the accuracy reached 99.10% and 99.30% on the self-collected and UCI HAR datasets, respectively.
DeepStruct: Pretraining of Language Models for Structure Prediction

We introduce a method for improving the structural understanding abilities of language models.Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models on a collection of task-agnostic corpora to generate structures from text.Our structure pretraining enables zero-shot transfer of the learned knowledge that models have about the structure tasks.We study the performance of this approach on 28 datasets, spanning 10 structure prediction tasks including open information extraction, joint entity and relation extraction, named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, factual probe, intent detection, and dialogue state tracking.We further enhance the pretraining with the task-specific training sets.We show that a 10B parameter language model transfers non-trivially to most tasks and obtains state-of-the-art performance on 21 of 28 datasets that we evaluate. 1
Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition

Human Activity Recognition (HAR) is increasingly used in a variety of applications, including health care, fitness tracking, and rehabilitation. To reduce the impact on the user’s daily activities, wearable technologies have been advanced throughout the years. In this study, an improved smart insole-based HAR system is proposed. The impact of data segmentation, sensors used, and feature selection on HAR was fully investigated. The Support Vector Machine (SVM), a supervised learning algorithm, has been used to recognise six ambulation activities: downstairs, sit to stand, sitting, standing, upstairs, and walking. Considering the impact that data segmentation can have on the classification, the sliding window size was optimised, identifying the length of 10 s with 50% of overlap as the best performing. The inertial sensors and pressure sensors embedded into the smart insoles have been assessed to determine the importance that each one has in the classification. A feature selection technique has been applied to reduce the number of features from 272 to 227 to improve the robustness of the proposed system and to investigate the importance of features in the dataset. According to the findings, the inertial sensors are reliable for the recognition of dynamic activities, while pressure sensors are reliable for stationary activities; however, the highest accuracy (94.66%) was achieved by combining both types of sensors.
Lightweight single image super-resolution with attentive residual refinement network

In recent years, deep convolutional neural network (CNN) based single image super-resolution (SISR) methods have been demonstrated impressive performance in terms of quantitative metrics and visual effects. Most CNN-based SISR methods can learn the complex non-linear mapping between low-resolution (LR) images and their corresponding high-resolution (HR) images due to the powerful representation capabilities of deep convolutional neural networks. However, as the depth and width of the SISR networks increase, the parameters of SISR networks will increase dramatically, leading to huge computational cost and large memory consumption, making them impractical in real-world applications. To attack the above issues, we propose an accurate and lightweight deep convolutional neural network, named Attentive Residual Refinement Network (ARRFN), to recover the high-resolution image from the original low-resolution image directly for SISR. In general, our proposed ARRFN consists of three parts, a feature extraction block, a stack of attentive residual refinement blocks (ARRFB), and a multi-scale separable upscaling module (MSSU), respectively. Specifically, our ARRFB consists of two branches, a regular residual learning branch and an attentive residual refinement branch. The former conducts regular residual learning by two residual blocks while the latter refines the residual information from the two residual blocks of the former branch with an attentive residual mechanism to further enhance the representation capabilities of the network. Furthermore, a multi-scale separable upsampling module (MSSU) is proposed to replace the regular upsampling operation for better SR results. Extensive experiments on several standard benchmarks show that the proposed method outperforms state-of-the-art SR methods in terms of quantitative metrics, visual quality, memory footprint, and inference time.
Deep Learning-Based Vehicle Classification for Low Quality Images

This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 × 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.
Improving Neural Cross-Lingual Abstractive Summarization via Employing Optimal Transport Distance for Knowledge Distillation

Current state-of-the-art cross-lingual summarization models employ multi-task learning paradigm, which works on a shared vocabulary module and relies on the self-attention mechanism to attend among tokens in two languages. However, correlation learned by self-attention is often loose and implicit, inefficient in capturing crucial cross-lingual representations between languages. The matter worsens when performing on languages with separate morphological or structural features, making the cross-lingual alignment more challenging, resulting in the performance drop. To overcome this problem, we propose a novel Knowledge-Distillation-based framework for Cross-Lingual Summarization, seeking to explicitly construct cross-lingual correlation by distilling the knowledge of the monolingual summarization teacher into the cross-lingual summarization student. Since the representations of the teacher and the student lie on two different vector spaces, we further propose a Knowledge Distillation loss using Sinkhorn Divergence, an Optimal-Transport distance, to estimate the discrepancy between those teacher and student representations. Due to the intuitively geometric nature of Sinkhorn Divergence, the student model can productively learn to align its produced cross-lingual hidden states with monolingual hidden states, hence leading to a strong correlation between distant languages. Experiments on cross-lingual summarization datasets in pairs of distant languages demonstrate that our method outperforms state-of-the-art models under both high and low-resourced settings.
Coarse-to-Fine Generative Modeling for Graphic Layouts

Even though graphic layout generation has attracted growing attention recently, it is still challenging to synthesis realistic and diverse layouts, due to the complicated element relationships and varied element arrangements. In this work, we seek to improve the performance of layout generation by incorporating the concept of regions, which consist of a smaller number of elements and appears like a simple layout, into the generation process. Specifically, we leverage Variational Autoencoder (VAE) as the overall architecture and decompose the decoding process into two stages. The first stage predicts representations for regions, and the second stage fills in the detailed position for each element within the region based on the predicted region representation. Compared to prior studies that merely abstract the layout into a list of elements and generate all the element positions in one go, our approach has at least two advantages. First, by the two-stage decoding, our approach decouples the complex layout generation task into several simple layout generation tasks, which reduces the problem difficulty. Second, the predicted regions can help the model roughly know what the graphic layout looks like and serve as global context to improve the generation of detailed element positions. Qualitative and quantitative experiments demonstrate that our approach significantly outperforms the existing methods, especially on the complex graphic layouts.
Burned and Blinded: Escalation Risks of Intelligence Loss from Countercyber Operations in Crisis

As the limitations of purely defensive cyber operations continue to be demonstrated in the continuing pressure of hostile cyberthreats, the U.S. government has introduced new doctrine to shape countercyber operations (CCO) leveraging offensive options to degrade threat capabilities and infrastructure. Planners have only begun to understand the broader implications of these new concepts in difficult periods of crisis. The article explores the parallels to other strategic early warning and intelligence capabilities, surfacing distinctions based on the unique dynamics of cyberconflict to identify scenarios in which CCO successes may prove potentially destabilizing and lead to greater escalation risk.
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes. High-performance GNN models always depend on both rich features and complete edge information in graph. However, such information could possibly be isolated by different data holders in practice, which is the so-called data isolation problem. To solve this problem, in this paper, we propose VFGNN, a federated GNN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GNN models. Specifically, we split the computation graph into two parts. We leave the private data (i.e., features, edges, and labels) related computations on data holders, and delegate the rest of computations to a semi-honest server. We also propose to apply differential privacy to prevent potential information leakage from the server. We conduct experiments on three benchmarks and the results demonstrate the effectiveness of VFGNN.