Reviewerly/exHarmony huggingface dataset:
A study on algorithm to extract stone tool surfaces from measured point clouds of joining materials based on images

Stone tools were created and used for daily life during the Paleolithic and Jomon periods. Excavated stone tools and flakes are joined together to recreate the mother rock, and are referred to as a joining material. By analyzing the joining materials and reproducing the stone tool manufacturing process, various information such as the stone tool maker’s manufacturing intentions, behaviors, technical abilities, and living ranges can be obtained. Conventionally, the created joining materials were recorded with photographs and scale drawings. In recent years, as a more accurate and stable method, recording based on three-dimensional point clouds using 3D scanners has also been performed. Unfortunately, these types of measurements can obtain only outer flake surfaces of the joining material, which means the stone tools inside the joining material are hardly recognized. To obtain the assembly order of flakes and the spatial posture of the stone tools from the measured outer point clouds, it is necessary to identify each stone tool by recognizing outer flake surfaces by segmentation of surface point clouds. In this paper, we propose a method to segment each stone tool from surface point clouds obtained by 3D-measured joining materials.
Design of English Teaching Corpus Resource-Sharing System Based on XML Technology

It is well known that vocabulary is the cornerstone of language learning. Therefore, vocabulary teaching is very important in English language teaching. However, time-consuming and inefficient vocabulary education has always been a long-standing issue. Teachers utilize traditional teaching methods, which are monotonous, so students lose interest and desire in studying, resulting in a drop in academic performance and even resistance to English courses, creating a vicious circle. Therefore, improving the efficiency of vocabulary teaching has become a hot topic in the field of English education today. Corpus technology has begun to infiltrate the realm of English education as the times and technology have progressed. Because of its vast capacity and real corpus, the corpus is gradually being used in English classes. Therefore, this research will explore the application of corpus-based XML technology data-driven teaching in junior high school English vocabulary teaching. Data-driven vocabulary teaching based on corpus resources has been shown to considerably improve students’ vocabulary learning effects, but its promotion requires the assistance of education departments as well as the development of instructors’ professional theoretical understanding. In order to solve the problem of data processing and data sharing of heterogeneous data in the process of digital campus construction, this study aims at the campus network application system, using data exchange technology and XML’s good extensibility, self-description, separation of form and content, etc. It realizes data sharing and sharing in heterogeneous databases.
ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing

Pathology image segmentation is an essential step in early detection and diagnosis for various diseases. Due to its complex nature, precise segmentation is not a trivial task. Recently, deep learning has been proved as an effective option for pathology image processing. However, its efficiency is highly restricted by inconsistent annotation quality. In this article, we propose an accurate and noise-tolerant segmentation approach to overcome the aforementioned issues. This approach consists of two main parts: a preprocessing module for data augmentation and a new neural network architecture, ANT-UNet. Experimental results demonstrate that, even on a noisy dataset, the proposed approach can achieve more accurate segmentation with 6% to 35% accuracy improvement versus other commonly used segmentation methods. In addition, the proposed architecture is hardware friendly, which can reduce the amount of parameters to one-tenth of the original and achieve 1.7× speed-up.
Improved multi-reference makeup transfer with localized attention mechanism

Makeup transfer refers to the methodology of transferring the makeup style of a reference image to a source image. Previous works have achieved satisfactory results of transferring the entire style, but multi-reference localized makeup transfer is still challenging due to the diversity of makeup styles as well as a large variety of image content. Our method builds upon image segmentation in order to detect the facial silhouette of the portraits. In this study, an end-to-end multireference makeup transfer framework that generates the output image given multiple reference images. The deep learning (DL) network successfully applies the style from the desired regions of the target reference image to the source image without damaging the original facial features. As demonstrated in the experiment results, the makeup transfer utilizing partial style transfer, and achieve state-of-the-art performance on a wide range of makeup styles.
Learning Monocular 3D Human Pose Estimation With Skeletal Interpolation

Deep learning has achieved unprecedented accuracy for monocular 3D human pose estimation. However, current learning-based 3D human pose estimation still suffers from poor generalization. Inspired by skeletal animation, which is popular in game development and animation production, we put forward an simple, intuitive yet effective interpolation-based data augmentation approach to synthesize continuous and diverse 3D human body sequences to enhance model generalization. The Transformer-based lifting network, trained with the augmented data, utilizes the self-attention mechanism to perform 2D-to-3D lifting and successfully infer high-quality predictions in the qualitative experiment. The quantitative result of cross-dataset experiment demonstrates that our resulting model achieves superior generalization accuracy on the publicly available dataset.
Mitigating Closed-Model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition

In this work, we aim to enhance the system robustness of end-to-end automatic speech recognition (ASR) against adversarially-noisy speech examples. We focus on a rigorous and empirical "closed-model adversarial robustness" setting (e.g., on-device or cloud applications). The adversarial noise is only generated by closed-model optimization (e.g., evolutionary and zeroth-order estimation) without accessing gradient information of a targeted ASR model directly. We propose an advanced Bayesian neural network (BNN) based adversarial detector, which could model latent distributions against adaptive adversarial perturbation with divergence measurement. We further simulate deployment scenarios of RNN Transducer, Conformer, and wav2vec-2.0 based ASR systems with proposed adversarial detection system. Leveraging the proposed BNN based detection system, we improve detection rate by +2.77 to +5.42% (relative +3.03 to +6.26%) and reduce the word error rate by 5.02 to 7.47% on LibriSpeech datasets compared to the current model enhancement methods against the adversarial speech examples.
Variance reduction in feature hashing using MLE and control variate method

The feature hashing algorithm introduced by Weinberger et al. (2009) is a popular dimensionality reduction algorithm that compresses high dimensional data points into low dimensional data points that closely approximate the pairwise inner product. This algorithm has been used in many fundamental machine learning applications such as model compression (Chen et al. 2015), spam classification (Weinberger et al. 2009), compressing text classifiers (Joulin et al. 2016), large scale image classification (Mensink et al. 2012). However, a limitation of this approach is that the variance of its estimator for the inner product tends to be large for small values of the reduced dimensions, making the estimate less reliable. We address this challenge and suggest two simple and practical solutions in this work. Our approach relies on control variate (CV) and maximum likelihood estimator (MLE), which are popular variance reduction techniques used in statistics. We show that these methods lead to significant variance reduction in the inner product similarity estimation. We give theoretical bounds on the same and complement it via extensive experiments on synthetic and real-world datasets. Given the simplicity and effectiveness of our approach, we hope that it can be adapted in practice.
STUDY OF ATTITUDES FOR EDUCATIONAL INTERACTION IN A DIGITAL ENVIRONMENT IN A COVID-19 SITUATION

The publication presents and discusses the results of a scientific study, which has as a research priority the collection of information on the attitudes of employees in educational institutions for pedagogical interaction in the digital environment, in the situation of COVID-19. 1533 persons from four professional groups included in education were studied. The data from the stress sector found that over 65% of the respondents have good levels of stress resilience in the skills for adaptation to the emergency situation in terms of the presence of control in the situation, ability to manage time, preserved opportunities for concentration and lack of hopelessness. With regard to the anxiety sector, a low percentage of respondents from the four groups is reported, in which the presence of internal tension is registered, which does not depend on the type of settlement. In the motivation sector, a significantly optimistic tendency of perception was found on the part of the respondents, confirming relatively high levels of motivation for learning/teaching in the digital environment. At the level of emotional intelligence, a positive orientation is revealed among a large percentage of the respondents in the context of available mental resources or internal readiness for learning/teaching in the conditions of e-learning.
Deformable VisTR: Spatio Temporal Deformable Attention for Video Instance Segmentation

Video instance segmentation (VIS) task requires classifying, segmenting, and tracking object instances over all frames in a video clip. Recently, VisTR [1] has been proposed as end-to-end transformer-based VIS framework, while demonstrating state-of-the-art performance. However, VisTR is slow to converge during training, requiring around 1000 GPU hours due to the high computational cost of its transformer attention module. To improve the training efficiency, we propose Deformable VisTR, leveraging spatio-temporal deformable attention module that only attends to a small fixed set of key spatio-temporal sampling points around a reference point. This enables Deformable VisTR to achieve linear computation in the size of spatio-temporal feature maps. Moreover, it can achieve on par performance as the original VisTR with 10× less GPU training hours. We validate the effectiveness of our method on the Youtube-VIS benchmark. Code is available at https://github.com/skrya/DefVIS.
Mean-variance Based Risk-sensitive Reinforcement Learning with Interpretable Attention

Risk-sensitive reinforcement learning (RL) has been studied to address the risk and uncertainty in autonomous systems. While a comprehensive understanding for the behaviors of RL agents plays an important role, interpretability was rarely discussed in the context of risk-sensitivity RL. In this paper, we present an interpretable visualization scheme with attention mechanism in which a saliency map represents the relative influence degree of an input state on the decision-making of mean-variance based risk-sensitive RL. Through 2D navigation experiments, we demonstrate that our scheme provides the interpretability with regard to risk-sensitive levels.
Geometrically robust linear optics from non-Abelian geometric phases

We construct a unified operator framework for quantum holonomies generated from bosonic systems. For a system whose Hamiltonian is bilinear in the creation and annihilation operators, we find a holonomy group determined only by a set of selected orthonormal modes obeying a stronger version of the adiabatic theorem. This photon-number independent description offers deeper insight as well as a computational advantage when compared to the standard formalism on geometric phases. In particular, a strong analogy between quantum holonomies and linear optical networks can be drawn. This relation provides an explicit recipe of how any linear optical quantum computation can be made geometrically robust in terms of adiabatic or nonadiabatic geometric phases.
Dynamic Multi-Scale Loss Balance for Object Detection

It is a common paradigm in object detection frameworks to perform multi-scale detection. However, each scale is treated equally during training. In this paper, we carefully study the objective imbalance of multi-scale detector training. We argue that the loss in each scale is neither equally important nor independent. Different from the existing solutions of setting fixed multi-task weights, we dynamically optimize the loss weight of each scale in the training process. Specifically, we propose an Adaptive Variance Weighting (AVW) to balance multi-scale loss according to the statistical variance. Then we develop a novel Reinforcement Learning Optimization (RLO) to decide the weighting scheme probabilistically during training. The proposed dynamic methods make better utilization of multi-scale training loss without extra computational complexity and learnable parameters for backpropagation. Experiments on Pascal VOC and MS COCO benchmark validate the effectiveness of our proposed methods.
Learning to Fuse Heterogeneous Features for Low-Light Image Enhancement

To see clearly in low-light scenarios, a series of learning-based techniques have been developed to improve visual quality. However, due to the absence of semantic-level features, the existing methods are perhaps less effective on semantic-oriented visual analysis tasks (e.g., saliency detection). To break down the limitation, we propose a new classification-driven enhancement method with heterogeneous feature fusion. Specifically, we construct a new low-light image enhancement network by integrating features acquired from the pre-trained classification network. Then, to better exploit the semantic-level information, we establish a Heterogeneous Feature Fusion (HF2) operation with channel-and-spatial attention to strength the effects of cross-domain features. HF2 acts on not only the fusion between classification and encoded features but also the fusion between encoded and decoded features. Extensive experiments are conducted to indicate our superiority against other state-of-the-art methods. The application on saliency detection further reveals our effectiveness in settling the semantic-oriented visual tasks.
Designing and Randomising Multiple-Choice Questions for E-Learning in Mathematics Using MVGEN

MVGEN is a multi-version question generation tool that allows creating multiple-choice questions with a large number of versions and multi-version tests using different randomisation techniques. These techniques include simple randomisation through shuffling of lists with keys and distractors or random selection of options from lists with several keys and distractors, but also more complex randomisation of parameters and variables using association tables. In this chapter, examples of multiple-choice questions in higher education mathematics are presented, and the design and randomisation techniques are explained.
Utilization of Digital Technologies to Enhance Assessments, Practices, and Equity in Inclusive Education

Inclusive education is purposed to focus on the distinct wants of students, assisting them to go through any challenges that may hinder them from attaining their full potential. However, the ever-changing dynamics in the sector of education like use of technology to accommodate new challenges such as COVID-19 among others complicate the aim of inclusive education if not mitigated. Utilization of digital technologies to enhance assessment practices is one such initiative. Inclusive assessment systems need cognizance of personalization through avenues like technological advances that will permit ‘anywhere, anytime' access to assessment. Assessment policies ought to involve all major stakeholders but in specific those who are nominated to represent others and who will be the primary consumers. This review has a twofold objective: review the digital assessment experiences and outline its challenges in an inclusive education environment with a view of mitigating them for more productive experiences.
Tele-Balint under the microscope: What really happens in Tele-Balint groups?

Balint groups provide a safe space for clinicians to discuss difficult cases, with the aim of deepening the clinician-patient relationship and providing space for self-introspection and personal development. During this COVID-19 period, mental health clinicians need a platform to undergo professional supervision and peer learning sessions, which can be provided for by Tele-Balint sessions. This study aims to understand the workings of Balint groups in a multi-disciplinary team, through exploring the experience and perceptions of mental health clinicians in a tertiary obstetrics- and paediatrics-focused hospital in Singapore towards Tele-Balint groups, and examining if these groups can address their professional needs.A mixed-methods study was conducted. 26 mental health clinicians who had participated in Tele-Balint groups since March 2020 completed a semi-structured questionnaire, and 12 of them were interviewed. Qualitative analysis of interview transcripts was performed.Qualitative analysis revealed 5 themes. The first 4: professional and personal growth of clinicians, providing emotional support to clinicians, burnout in clinicians: what contributes and what helps, and psychological safety, address whether Tele-Balint groups meet clinicians' needs. The last theme, evolution of nature of Balint groups, addresses whether Tele-Balint groups meet clinicians' needs during the time of a pandemic.Tele-Balint group participation was found to be beneficial in facilitating personal and professional growth, providing emotional support and preventing burnout, despite some limitations. Members should maintain flexibility towards the Balint process, in order to accommodate others who have differing needs, especially in a multi-disciplinary group.
VR-FAM: Variance-Reduced Encoder with Nonlinear Transformation for Facial Attribute Manipulation

Facial attribute manipulation (FAM) aims to infer desired facial images by modifying specific attributes while keeping others unchanged. Existing works suffer from the entanglement of facial attributes, leading to unexpected artifacts and the loss of facial identity information after editing. To alleviate these issues, we propose a novel FAM framework based on StyleGAN, termed VR-FAM, which can meet the requirements of FAM—editing ability, distortion, and fidelity. First, we propose a variance-reduced encoder to make the latent space close to the one of StyleGAN. Second, we present a nonlinear latent transformation network, which can convert the source latent code to target latent code in line with the nonlinear latent space of StyleGAN. Experimentally, we evaluate the proposed FAM framework on the benchmark FFHQ dataset and demonstrate the improvement gain over the recently published models in terms of edit accuracy and fidelity.
Zeroth-Order Randomized Subspace Newton Methods

Zeroth-order methods have become important tools for solving problems where we have access only to function evaluations. However, the zeroth-order methods only using gradient approximations are n times slower than classical first-order methods for solving n-dimensional problems. To accelerate the convergence rate, this paper proposes the zeroth order randomized subspace Newton (ZO-RSN) method, which estimates projections of the gradient and Hessian by random sketching and finite differences. This allows us to compute the Newton step in a lower dimensional subspace, with small computational costs. We prove that ZO-RSN can attain lower iteration complexity than existing zeroth order methods for strongly convex problems. Our numerical experiments show that ZO-RSN can perform black-box attacks under a more restrictive limit on the number of function queries than the state-of-the-art Hessian-aware zeroth-order method.
RCANet: Row-Column Attention Network for Semantic Segmentation

Establishing high-order interactions among pixels and object parts is one of the most fundamental problems in semantic segmentation. The recent proposals are based on non-local methods which utilize the self-attention mechanism to capture the long-range correlations. However, non-local methods could be very expensive, both theoretically and experimentally. Moreover, non-local methods are typically designed to address spatial correlations rather than feature correlations across channels. In this work, we propose a Row-Column Attention Network (RCANet) to encode globally contextual information. It consists of a row-wise intra-channel attention module and a column-wise intra-channel attention module, followed by a cross-channel interaction module. We conduct experiments on two datasets: Cityscapes and ADE20K. The results show that our method is comparable to the state-of-the-art methods for semantic segmentation.
A Multi-Sensor Interacted Vehicle-Tracking Algorithm with Time-Varying Observation Error

Vehicle tracking in the field of intelligent transportation has received extensive attention in recent years. Multi-sensor-based vehicle tracking system is widely used in some critical environments. However, in the actual scenes, the observation error of each sensor is often different and time varying because of the environmental change and the channel difference. Therefore, in this paper, we propose a multi-sensor interacted vehicle-tracking algorithm with time-varying observation error (MI-TVOE). The algorithm establishes a jointed and time-varying observation error model for each sensor to indicate the variation of observation noise. Then, we develop a multi-sensor interacted vehicle-tracking algorithm which can predict the statistical information of a time-varying observation error and fuse the tracking result of each sensor to provide a global estimation. Simulation results show that the proposed MI-TVOE algorithm can significantly improve the tracking performance compared to the single-sensor-based tracking method, the traditional unscented Kalman filter (UKF), the apdative UKF method (AUKF) and the multi-error fused UKF method (MEF-UKF), which will be well applied to the complex tracking scenes and will reduce the computational complexity with time-varying observation error. The experiments in this paper also prove the superiority of the proposed MI-TVOE algorithm in complex environments.
Expressive Latent Feature Modelling for Explainable Matrix Factorisation-based Recommender Systems

The traditional matrix factorisation (MF)-based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This article introduces an MF-based explainable recommender system framework that utilises the user-item rating data and the available item information to model meaningful user and item latent features. These features are exploited to enhance the rating prediction accuracy and the recommendation explainability. Our proposed feature-based explainable recommender system framework utilises these meaningful user and item latent features to explain the recommendation without relying on private or outer data. The recommendations are explained to the user using text message and bar chart. Our proposed model has been evaluated in terms of the rating prediction accuracy and the reasonableness of the explanation using six real-world benchmark datasets for movies, books, video games, and fashion recommendation systems. The results show that the proposed model can produce accurate explainable recommendations.