Reviewerly/exHarmony huggingface dataset:
Virtual and augmented reality for preoperative planning in plastic surgical procedures: A systematic review

BackgroundVirtual and augmented reality (VR and AR) are fast-developing technologies that allow the three-dimensional visualization of digital information.ObjectiveThis systematic review aimed to compare the application of VR and AR to conventional methods in preoperative planning of plastic surgical procedures.MethodA systematic literature search was conducted in Embase, Medline (Ovid), Web-of-Science, Cochrane, and Google Scholar databases on October 11, 2019. All literature comparing AR and/or VR with conventional methods for preoperative planning was collected. Only articles that studied at least one of the following outcomes were included: technical accuracy of the procedure, operative time, complications, and costs of total intervention.ResultsNo articles on VR were found. Six articles were found on interventions performed with AR assistance. AR showed to be significantly better for the accuracy of osteotomies in mandibular angle osteotomies and intraoral mandible distraction compared to conventional methods. For synostotic plagiocephaly and orbital hypertelorism correction, the use of AR demonstrated a precise osteotomy. Intraoperative perforator identification in DIEP flap procedures was more accurate with AR compared to Doppler ultrasound. Harvesting time (p < 0.012) and operative time (p < 0.01) in DIEP-flap procedures and mandibular angle osteotomies, respectively, were significantly reduced if AR was used. No articles were found regarding the costs of using AR for preoperative planning.ConclusionAR technology has the potential to assist the plastic surgeon in operating more accurately, safely, and fast. Studies on VR technology for preoperative planning in plastic surgery are lacking.More comparative studies are necessary, including data on clinical outcomes and cost-effectiveness.
ImageSense: An Intelligent Collaborative Ideation Tool to Support Diverse Human-Computer Partnerships

Professional designers create mood boards to explore, visualize, and communicate hard-to-express ideas. We present ImageCascade, an intelligent, collaborative ideation tool that combines individual and shared work spaces, as well as collaboration with multiple forms of intelligent agents. In the collection phase, ImageCascade offers fluid transitions between serendipitous discovery of curated images via ImageCascade, combined text- and image-based Semantic search, and intelligent AI suggestions for finding new images. For later composition and reflection, ImageCascade provides semantic labels, generated color palettes, and multiple tag clouds to help communicate the intent of the mood board. A study of nine professional designers revealed nuances in designers' preferences for designer-led, system-led, and mixed-initiative approaches that evolve throughout the design process. We discuss the challenges in creating effective human-computer partnerships for creative activities, and suggest directions for future research.
Covido‐pedago‐phobia

Straight out of the Diagnostic and Statistical Manual COVID‐19 edition comes Covido‐pedago‐phobia. Here the authors identify its manifestations and reflect on its prevalence.
Predicting and Diagnosing User Engagement with Mobile UI Animation via a Data-Driven Approach

Animation, a common design element in user interfaces (UI), can impact user engagement (UE) with mobile applications. To avoid impairing UE due to improper design of animation, designers rely on resource-intensive evaluation methods like user studies or expert reviews. To alleviate this burden, we propose a data-driven approach to assisting designers in examining UE issues with their animation designs. We first crowdsource UE assessments of mobile UI animations. Based on the collected data, we then build a novel deep learning model that captures both spatial and temporal features of animations to predict their UE levels. Evaluations show that our model achieves a reasonable accuracy. We further leverage the animation feature encoded by our model and a sample set of expert reviews to derive potential UE issues of a particular animation. Finally, we develop a proof-of-concept tool and evaluate its potential usage in actual design practices with experts
Blockchain Service Provider Selection Based on an Integrated BWM-Entropy-TOPSIS Method Under an Intuitionistic Fuzzy Environment

The emergence of blockchain technology has significantly changed the underlying infrastructure of existing information technology and will fundamentally affect the production modes of enterprises.However, because the application of blockchain is still in its infancy, it is difficult for an enterprise to develop a comprehensive assessment of various types of blockchain service providers in the market.Hence, enterprises need scientific decision tools to estimate which blockchain service provider is appropriate.However, few studies have focused on this phenomenon.Therefore, to address this challenge, this investigation proposes a novel integrated multi-attribute group decision-making (MAGDM) method to help enterprises estimate which blockchain vendor is more appropriate by considering more comprehensive influence factors.The proposed method is defined in an intuitionistic fuzzy environment and integrates entropy and the best-worst method (BWM) for comprehensive weighting of decision makers (DMs), subjective criteria and objective criteria in the decision-making process to make the decision results more reliable and reasonable.A numerical example and comparison are provided to illustrate the practicability and usefulness of the method.This study enriches the theory and methodology of blockchain technology and MAGDM analysis.
LNPSS: SAR image despeckling based on local and non-local features using patch shape selection and edges linking

Removal of speckle noise from Real SAR images is a significant task for the better segmentation, target detection, target recognition or other processing. In this paper, a novel complex strategy is presented based on carefully analyzing the effect of the speckle noise on the histogram. The histogram of a patch is expanded in the image corrupted with speckle noise. To overcome this, we introduced some rules to remove both noise and preserve the crucial features (edges). Owing to the use of patch shape selection automatically, the patch's histogram modification is more effective. Our method, also, is being able to reconstruct some missing edges by using the kirsch filter and edge linking approach. Our satisfactory results indicate that the proposed approach has a competitive despeckling performance in terms of both visual quality assessment and preserving vital details.
OCR-Nets: Variants of Pre-trained CNN for Urdu Handwritten Character Recognition via Transfer Learning

Deep Convolutional neural networks (CNN) have been among the utmost competitive neural network architectures and have set the state-of-the-art in various fields of computer vision. In this paper, we present OCR-Nets, variants of (AlexNet & GoogleNet) for recognition of handwritten Urdu characters through transfer learning. Our proposed networks are experimented using an integrated dataset. To compare the recognition rate with traditional character recognition methods and to confirm the fairness of the experiment an additional Urdu character dataset is manually generated with different fonts and size. The experimental result shows that OCR-AlexNet and OCR-GoogleNet produce significant performance gains of 96.3% and 94.7% averaged success rate respectively.
Audio Replay Spoof Attack Detection by Joint Segment-Based Linear Filter Bank Feature Extraction and Attention-Enhanced DenseNet-BiLSTM Network

Most automatic speaker verification (ASV) systems are vulnerable to various spoofing attacks. In recent years, there have been many methods were proposed for detecting spoofing attacks in ASV, and significant progress has been made. However, current methods have shown little improvements in replay spoof attack detection as they lack a more suitable model for replay detection. To address this issue, in this article, we propose a novel model based on attention-enhanced DenseNet-BiLSTM network and segment-based linear filter bank features. First, silent segments are selected from each speech signal by using a short-term zero-crossing rate and energy. If the total duration of silent segments only contains a very limited amount of data, the decaying tails will be selected instead. Second, the linear filter bank features are extracted from the selected segments in the relatively high-frequency domain. Finally, an attention-enhanced DenseNet-BiLSTM architecture which can avoid the problems of overfitting is built. To validate this model, we used two datasets, including BTAS2016 and ASVspoof2017. Experiments show that using the attention-enhanced DenseNet-BiLSTM model with the segment-based linear filter bank feature achieves the best performance. Compared with the baseline system based on constant Q cepstral coefficient and Gaussian mixture model (GMM), the proposed model can produce a relative improvement of 91.68% and 74.04% on the two data sets respectively.
A Benchmark Dataset and Comparison Study for Multi-modal Human Action Analytics

Large-scale benchmarks provide a solid foundation for the development of action analytics. Most of the previous activity benchmarks focus on analyzing actions in RGB videos. There is a lack of large-scale and high-quality benchmarks for multi-modal action analytics. In this article, we introduce PKU Multi-Modal Dataset (PKU-MMD), a new large-scale benchmark for multi-modal human action analytics. It consists of about 28,000 action instances and 6.2 million frames in total and provides high-quality multi-modal data sources, including RGB, depth, infrared radiation (IR), and skeletons. To make PKU-MMD more practical, our dataset comprises two subsets under different settings for action understanding, namely Part I and Part II. Part I contains 1,076 untrimmed video sequences with 51 action classes performed by 66 subjects, while Part II contains 1,009 untrimmed video sequences with 41 action classes performed by 13 subjects. Compared to Part I, Part II is more challenging due to short action intervals, concurrent actions and heavy occlusion. PKU-MMD can be leveraged in two scenarios: action recognition with trimmed video clips and action detection with untrimmed video sequences. For each scenario, we provide benchmark performance on both subsets by conducting different methods with different modalities under two evaluation protocols, respectively. Experimental results show that PKU-MMD is a significant challenge to many state-of-the-art methods. We further illustrate that the features learned on PKU-MMD can be well transferred to other datasets. We believe this large-scale dataset will boost the research in the field of action analytics for the community.
Adaptive Switching Weight Mean Filter for Salt and Pepper Image Denoising

We propose Adaptive Switching Weight Mean Filter (ASWMF) to remove the salt and pepper noise. Instead of using median or mean, ASWMF assigns value of a switching weight mean (SWM) to grey value of the centre pixel of an adaptive window. SWM is evaluated by eliminating all noisy pixels from the adaptive window and putting a low weight for pixels on the diagonals and a high weight for pixels outside the diagonals. ASWMF can remove noise with various noise levels effectively. It not only successes for low-density denoising, but also removes medium-density and high-density noise impressively. In experiments, we compare denoising results with other similar denoising methods. According to intuition as well as error metrics such as the peak signal-to-noise ratio and the structure similarity, ASWMF outperforms other methods.
Generalized Product Quantization Network for Semi-Supervised Image Retrieval

Image retrieval methods that employ hashing or vector quantization have achieved great success by taking advantage of deep learning. However, these approaches do not meet expectations unless expensive label information is sufficient. To resolve this issue, we propose the first quantization-based semi-supervised image retrieval scheme: Generalized Product Quantization (GPQ) network. We design a novel metric learning strategy that preserves semantic similarity between labeled data, and employ entropy regularization term to fully exploit inherent potentials of unlabeled data. Our solution increases the generalization capacity of the quantization network, which allows overcoming previous limitations in the retrieval community. Extensive experimental results demonstrate that GPQ yields state-of-the-art performance on large-scale real image benchmark datasets.
P–nets: Deep Polynomial Neural Networks

Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose Π-Nets, a new class of DCNNs. Π-Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. Π-Nets can be implemented using special kind of skip connections and their parameters can be represented via high-order tensors. We empirically demonstrate that Π-Nets have better representation power than standard DCNNs and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, Π-Nets produce state-of-the-art results in challenging tasks, such as image generation. Lastly, our framework elucidates why recent generative models, such as StyleGAN, improve upon their predecessors, e.g., ProGAN.
Symmetry-Assisted Preparation of Entangled Many-Body States on a Quantum Computer

Starting from the Quantum-Phase-Estimate (QPE) algorithm, a method is proposed to construct entangled states that describe correlated many-body systems on quantum computers. Using operators for which the discrete set of eigenvalues is known, the QPE approach is followed by measurements that serve as projectors on the entangled states. These states can then be used as inputs for further quantum or hybrid quantum-classical processing. When the operator is associated to a symmetry of the Hamiltonian, the approach can be seen as a quantum--computer formulation of symmetry breaking followed by symmetry restoration. The method proposed in this work, called Discrete Spectra Assisted (DSA), is applied to superfluid systems. By using the blocking technique adapted to qubits, the full spectra of a pairing Hamiltonian is obtained.
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. In this paper, we propose label-consistency based graph neural network (LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification. We further show the superiority of LC-GNN in sparse scenarios with only a handful of labeled nodes.
From English to Code-Switching: Transfer Learning with Strong Morphological Clues

Linguistic Code-switching (CS) is still an understudied phenomenon in natural language processing.The NLP community has mostly focused on monolingual and multi-lingual scenarios, but little attention has been given to CS in particular.This is partly because of the lack of resources and annotated data, despite its increasing occurrence in social media platforms.In this paper, we aim at adapting monolingual models to code-switched text in various tasks.Specifically, we transfer English knowledge from a pre-trained ELMo model to different code-switched language pairs (i.e., Nepali-English, Spanish-English, and Hindi-English) using the task of language identification.Our method, CS-ELMo, is an extension of ELMo with a simple yet effective position-aware attention mechanism inside its character convolutions.We show the effectiveness of this transfer learning step by outperforming multilingual BERT and homologous CS-unaware ELMo models and establishing a new state of the art in CS tasks, such as NER and POS tagging.Our technique can be expanded to more English-paired code-switched languages, providing more resources to the CS community.
Blockchain-empowered decentralised trust management for the Internet of Vehicles security

Internet of Vehicles (IoV) requires trust management to implement effective authentication and authorization of nodes, as it is not possible to establish trusted connections to the roadside components that eventually a vehicle will meet during its journey, ahead of time. To cope with this issue, dynamic access control is required, where authorizations are granted considering security policies and the node trustworthiness. However, decentralized trust management is a preferable solution, but it implies a considerable consumption of energy. Consumption is further exacerbated by the means needed to protect from attacks the trust management entities themselves. This work proposes suitable trust management for the IoT and IoV by exploiting the eventual consistency and security guarantees of blockchain. The design of a solution based on such technology is described, and an empirical assessment of its protection degree is provided.
Designing normative theories for ethical and legal reasoning: LogiKEy framework, methodology, and tool support

A framework and methodology—termed LogiKEy—for the design and engineering of ethical reasoners, normative theories and deontic logics is presented. The overall motivation is the development of suitable means for the control and governance of intelligent autonomous systems. LogiKEy's unifying formal framework is based on semantical embeddings of deontic logics, logic combinations and ethico-legal domain theories in expressive classic higher-order logic (HOL). This meta-logical approach enables the provision of powerful tool support in LogiKEy: off-the-shelf theorem provers and model finders for HOL are assisting the LogiKEy designer of ethical intelligent agents to flexibly experiment with underlying logics and their combinations, with ethico-legal domain theories, and with concrete examples—all at the same time. Continuous improvements of these off-the-shelf provers, without further ado, leverage the reasoning performance in LogiKEy. Case studies, in which the LogiKEy framework and methodology has been applied and tested, give evidence that HOL's undecidability often does not hinder efficient experimentation.
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation

Human motion segmentation based on transfer subspace learning is a rising interest in action-related tasks. Although progress has been made, there are still several issues within the existing methods. First, existing methods transfer knowledge from source data to target tasks by learning domain-invariant features, but they ignore to preserve domain-specific knowledge. Second, the transfer subspace learning is employed in either low-level or high-level feature spaces, but few methods consider fusing multi-level features for subspace learning. To this end, we propose a novel multi-mutual consistency induced transfer subspace learning framework for human motion segmentation. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces and reduces the distribution gap between them through a multi-mutual consistency learning strategy. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Our model also conducts the transfer subspace learning on different layers to capture multi-level structural information. Further, to preserve the temporal correlations, we project the learned representations into a block-like space. The proposed model is efficiently optimized by using the Augmented Lagrange Multiplier (ALM) algorithm. Experimental results on four human motion datasets demonstrate the effectiveness of our method over other state-of-the-art approaches.
TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting

We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person. Without using any paired data for supervision, the proposed method can be trained in an unsupervised manner by exploiting invariance properties of three orthogonal factors of variation including motion, structure, and view-angle. Specifically, with loss functions carefully derived based on invariance, we train an auto-encoder to disentangle the latent representations of such factors given the source and target video clips. This allows us to selectively transfer motion extracted from the source video seamlessly to the target video in spite of structural and view-angle disparities between the source and the target. The relaxed assumption of paired data allows our method to be trained on a vast amount of videos needless of manual annotation of source-target pairing, leading to improved robustness against large structural variations and extreme motion in videos. We demonstrate the effectiveness of our method over the state-of-the-art methods. Code, model and data are publicly available on our project page (https://yzhq97.github.io/transmomo).
Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects

When we humans look at a video of human-object interaction, we can not only infer what is happening but we can even extract actionable information and imitate those interactions. On the other hand, current recognition or geometric approaches lack the physicality of action representation. In this paper, we take a step towards more physical understanding of actions. We address the problem of inferring contact points and the physical forces from videos of humans interacting with objects. One of the main challenges in tackling this problem is obtaining ground-truth labels for forces. We sidestep this problem by instead using a physics simulator for supervision. Specifically, we use a simulator to predict effects, and enforce that estimated forces must lead to same effect as depicted in the video. Our quantitative and qualitative results show that (a) we can predict meaningful forces from videos whose effects lead to accurate imitation of the motions observed, (b) by jointly optimizing for contact point and force prediction, we can improve the performance on both tasks in comparison to independent training, and (c) we can learn a representation from this model that generalizes to novel objects using few shot examples.