Reviewerly/exHarmony huggingface dataset:
Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function

Purpose Automatic liver segmentation from abdominal computed tomography (CT) images is a fundamental task in computer‐assisted liver surgery programs. Many liver segmentation algorithms are very sensitive to fuzzy boundaries and heterogeneous pathologies, especially when the data are scarce. To solve these problems, we propose an automatic liver segmentation framework based on three‐dimensional (3D) convolutional neural networks with a hybrid loss function. Methods Two networks are incorporated in our method with the first being a liver shape autoencoder that is trained to obtain compressed codes of liver shapes, and the second being a liver segmentation network that is trained with a hybrid loss function. The design of the hybrid loss function is comprised of three parts. The first part is an adaptively weighted cross‐entropy loss, which pays more attention to misclassified pixels. The second part is an edge‐preserving smoothness loss, which guarantees that the adjacent pixels with the same label have similar outputs, while dissimilar for pixels with different labels. The third part of the loss is a shape constraint to model high‐level structural differences based on the learned shape codes. Both networks use 3D operations for data processing. In our experiments, data augmentation is performed at both the training and the test stage. Results We extensively evaluated our method on two datasets: the Segmentation of the Liver Competition 2007 (Sliver07), and the Combined (CT‐MR) Healthy Abdominal Organ Segmentation (CHAOS) Challenge. Finally, with only 20 training scans, we achieved the best score of 82.55 on the Sliver07 challenge, and a score of 83.02 on the CHAOS challenge. Conclusions In this study, we proposed a novel hybrid loss to overcome the difficulties in liver segmentation. The quantitative and qualitative results demonstrate that our method is highly suited for pathological liver segmentation, even when trained with a small dataset.
Competencia digital docente y el uso de la realidad aumentada en la enseñanza de ciencias en Educación Secundaria Obligatoria.

Today, technology is a major issue in society, so it is essential to have acquired digital competence in order to be successful in it. An example of the growing use of technology in education is the didactic use of Augmented Reality, which offers the area of science a wealth of possibilities to facilitate the teaching-learning process. The aim of this study is to understand the impact of digital competence on the use of augmented reality as a teaching resource in science-related classes. The method used is quantitative, non-experimental, descriptive, correlational and predictive. The data collection has been carried out through an ad hoc questionnaire on digital teaching competence and augmented reality with different levels of response. The results show that augmented reality is a resource that has been used by teachers teaching science, presenting them with an average level of digital competence. We conclude by stating that teachers who use augmented reality are more digitally competent than those who do not..
Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search

As hashing becomes an increasingly appealing technique for large-scale image retrieval, multi-label hashing is also attracting more attention for the ability to exploit multi-level semantic contents. In this paper, we propose a novel deep hashing method for scalable multi-label image search. Unlike existing approaches with conventional objectives such as contrast and triplet losses, we employ a rank list, rather than pairs or triplets, to provide sufficient global supervision information for all the samples. Specifically, a new rank-consistency objective is applied to align the similarity orders from two spaces, the original space and the hamming space. A powerful loss function is designed to penalize the samples whose semantic similarity and hamming distance are mismatched in two spaces. Besides, a multi-label softmax cross-entropy loss is presented to enhance the discriminative power with a concise formulation of the derivative function. In order to manipulate the neighborhood structure of the samples with different labels, we design a multi-label clustering loss to cluster the hashing vectors of the samples with the same labels by reducing the distances between the samples and their multiple corresponding class centers. The state-of-the-art experimental results achieved on three public multi-label datasets, MIRFLICKR-25K, IAPRTC12 and NUS-WIDE, demonstrate the effectiveness of the proposed method.
Teaching Temporal Logics to Neural Networks

We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics. In this work we focus on linear-time temporal logic (LTL), as it is widely used in verification. We train a Transformer on the problem to directly predict a solution, i.e. a trace, to a given LTL formula. The training data is generated with classical solvers, which, however, only provide one of many possible solutions to each formula. We demonstrate that it is sufficient to train on those particular solutions to formulas, and that Transformers can predict solutions even to formulas from benchmarks from the literature on which the classical solver timed out. Transformers also generalize to the semantics of the logics: while they often deviate from the solutions found by the classical solvers, they still predict correct solutions to most formulas.
On the role of planning in model-based deep reinforcement learning

Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hypothesis, the resulting diversity of model-based methods has also made it difficult to track which components drive success and why. In this paper, we seek to disentangle the contributions of recent methods by focusing on three questions: (1) How does planning benefit MBRL agents? (2) Within planning, what choices drive performance? (3) To what extent does planning improve generalization? To answer these questions, we study the performance of MuZero (Schrittwieser et al., 2019), a state-of-the-art MBRL algorithm with strong connections and overlapping components with many other MBRL algorithms. We perform a number of interventions and ablations of MuZero across a wide range of environments, including control tasks, Atari, and 9x9 Go. Our results suggest the following: (1) Planning is most useful in the learning process, both for policy updates and for providing a more useful data distribution. (2) Using shallow trees with simple Monte-Carlo rollouts is as performant as more complex methods, except in the most difficult reasoning tasks. (3) Planning alone is insufficient to drive strong generalization. These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.
Real Image Super Resolution via Heterogeneous Model Ensemble Using GP-NAS

With advancement in deep neural network (DNN), recent state-of-the-art (SOTA) image super-resolution (SR) methods have achieved impressive performance using deep residual network with dense skip connections. While these models perform well on benchmark dataset where low-resolution (LR) images are constructed from high-resolution (HR) references with known blur kernel, real image SR is more challenging when both images in the LR-HR pair are collected from real cameras. Based on existing dense residual networks, a Gaussian process based neural architecture search (GP-NAS) scheme is utilized to find candidate network architectures using a large search space by varying the number of dense residual blocks, the block size and the number of features. A suite of heterogeneous models with diverse network structure and hyperparameter are selected for model-ensemble to achieve outstanding performance in real image SR. The proposed method won the first place in all three tracks of the AIM 2020 Real Image Super-Resolution Challenge.
Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition

Semantic segmentation is an extensively studied task in computer vision, with numerous methods proposed every year. Thanks to the advent of deep learning in semantic segmentation, the performance on existing benchmarks is close to saturation. A natural question then arises: Does the superior performance on the closed (and frequently re-used) test sets transfer to the open visual world with unconstrained variations? In this paper, we take steps toward answering the question by exposing failures of existing semantic segmentation methods in the open visual world under the constraint of very limited human labeling effort. Inspired by previous research on model falsification, we start from an arbitrarily large image set, and automatically sample a small image set by maximizing the discrepancy (MAD) between two segmentation methods. The selected images have the greatest potential in falsifying either (or both) of the two methods. We also explicitly enforce several conditions to diversify the exposed failures, corresponding to different underlying root causes. A segmentation method, whose failures are more difficult to be exposed in the MAD competition, is considered better. We conduct a thorough MAD diagnosis of ten PASCAL VOC semantic segmentation algorithms. With detailed analysis of experimental results, we point out strengths and weaknesses of the competing algorithms, as well as potential research directions for further advancement in semantic segmentation. The codes are publicly available at https://github.com/QTJiebin/MAD_Segmentation .
E-Learning in COVID-19 Situation: Students’ Perception

COVID-19 pandemic era has changed human life in the world. In education side, the learning process is forced to be carried out through full online (E-learning) since this pandemic era. There are many studies conducted on students’ perceptions of using E-learning to learn the English language while this challenges era. So, the researchers also want to conduct a study about it. The objective of this study was to investigate the students’ perceptions on the use of E-learning while COVID-19 pandemic era. Data were collected using a questionnaire that collected background information of participants and a five-point Likert scale to gauge the students’ perception use of the E-learning in EFL classroom while pandemic COVID-19 era. Findings from this research indicate that the most of students showed a negative perception toward to use E-learning in the EFL process. It was proved by the results of students’ perception. There is only 1 student who strongly agrees with the current e-learning process or said that the E-learning in pandemic COVID-19 era is effective, there are 10 students who said the E-learning is less effective, 1 students say they are moderate (4%), 12 students said E-learning is not effective and 3 student said E-learning is very not effective. There are many factors influenced the students’ perception on the use of E-learning media in COVID-19 pandemic era such as, the signal and network connection; limit of quota and expensive; lack of lecturers methods; lack of students’ motivation; limitation of time; and lack of interaction.
Attention Back-End for Automatic Speaker Verification with Multiple Enrollment Utterances

Probabilistic linear discriminant analysis (PLDA) or cosine similarity have been widely used in traditional speaker verification systems as back-end techniques to measure pairwise similarities. To make better use of multiple enrollment utterances, we propose a novel attention back-end model that can be used for both textindependent (TI) and text-dependent (TD) speaker verification, and we use scaled-dot self-attention and feed-forward self-attention networks as architectures that learn the intra-relationships of enrollment utterances. To verify the proposed model, we conduct a series of experiments on the CNCeleb and VoxCeleb datasets by combining it with several state-of-the-art speaker encoders including TDNN and ResNet. Experimental results obtained using multiple enrollment utterances on CNCeleb show that the proposed attention back-end model leads to lower EER and minDCF scores than its PLDA and cosine similarity counterparts for each speaker encoder, and an experiment on VoxCeleb demonstrates that our model can be used even for a single enrollment case.
Research for Face Image Super-Resolution Reconstruction Based on Wavelet Transform and SRGAN

Super-resolution face image is the basis of high detection rate in face recognition. In order to meet the requirements of super-resolution image in face recognition, aiming at the problem of texture loss of super-resolution image under high-frequency features, a face image reconstruction method based on wavelet transform and super-resolution generative adversarial network (SRGAN) is proposed to reduce the impact of low-resolution image caused by imaging hardware, network bandwidth and sampling environment on face recognition accuracy. Firstly, the wavelet transform algorithm is used to preprocess the low-resolution face image to extract the detailed texture features of the face image under different frequencies. Then, GAN is used to learn the prior knowledge of wavelet coefficients, and the identity preserving constraint is applied to the output image, and the perceptual loss function of the fusion wavelet coefficients is realized. Finally, the deep learning model based on SRGAN is used to obtain high-resolution face images. Experimental results show that the method can achieve super-resolution restoration of low-resolution face images and meet the requirements of face recognition accuracy.
Activating hidden teleportation power: Theory and experiment

Ideal quantum teleportation transfers an unknown quantum state intact from one party Alice to the other Bob via the use of a maximally entangled state and the communication of classical information. If Alice and Bob do not share entanglement, the maximal average fidelity between the state to be teleported and the state received, according to a classical measure-and-prepare scheme, is upper bounded by a function fc that is inversely proportional to the Hilbert space dimension. In fact, even if they share entanglement, the so-called teleportation fidelity may still be less than the classical threshold fc. For two-qubit entangled states, conditioned on a successful local filtering, the teleportation fidelity can always be activated, i.e., boosted beyond fc. Here, for all dimensions larger than two, we show that the teleportation power hidden in a subset of entangled two-qudit Werner states can also be activated. In addition, we show that an entire family of two-qudit rank-deficient states violates the reduction criterion of separability, and thus their teleportation power is either above the classical threshold or can be activated. Using hybrid entanglement prepared in photon pairs, we also provide the first proof-of-principle experimental demonstration of the activation of teleportation power hidden in this latter family of qubit states. The connection between the possibility of activating hidden teleportation power with the closely-related problem of entanglement distillation is discussed.6 MoreReceived 18 August 2020Accepted 26 March 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023045Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement manipulationEntanglement productionQuantum protocolsQuantum teleportationQuantum Information
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources

Sentiment analysis of user-generated reviews or comments on products and services in social networks can help enterprises to analyze the feedback from customers and take corresponding actions for improvement. To mitigate large-scale annotations on the target domain, domain adaptation (DA) provides an alternate solution by learning a transferable model from other labeled source domains. Existing multi-source domain adaptation (MDA) methods either fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources and the distribution difference among different sub-domains even in the same source, or cannot reflect the varying optimal weighting during different training stages. In this paper, we propose a novel instance-level MDA framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN), to address the above issues. Specifically, C-CycleGAN consists of three components: (1) pre-trained text encoder which encodes textual input from different domains into a continuous representation space, (2) intermediate domain generator with curriculum instance-level adaptation which bridges the gap across source and target domains, and (3) task classifier trained on the intermediate domain for final sentiment classification. C-CycleGAN transfers source samples at instance-level to an intermediate domain that is closer to the target domain with sentiment semantics preserved and without losing discriminative features. Further, our dynamic instance-level weighting mechanisms can assign the optimal weights to different source samples in each training stage. We conduct extensive experiments on three benchmark datasets and achieve substantial gains over state-of-the-art DA approaches. Our source code is released at: https://github.com/WArushrush/Curriculum-CycleGAN.
Anomaly Detection and Cause Analysis During Landing Approach Using Recurrent Neural Network

The stabilized approach is important to avoid aircraft accidents during landing. Although there are many possible factors that can lead to an unstable approach, actions can be taken to mitigate the risk if the reason for unstable approaches is identified properly. However, because unstable approaches are rarely observed, it is difficult to identify the relevant reasons by analyzing unstable approaches only. Instead, the author proposes to identify untypical flights, which are not necessarily categorized into unstable approaches, by using neural network. Using the proposed method, more flights are available to identify the reason of unstable approaches. The stability index is estimated by neural network considering the current flight conditions such as wind and initial deviation. Therefore, untypical flights can be detected by comparing the actual stability index and the estimated stability index. The long-term interaction between the stability index and flight conditions is modeled by using gated recurrent unit. As a result of modeling, some of flights are categorized into untypical flights, and the false localizer beam is observed for most of these flights, which could be a potential hazard for unstable approaches. The proposed approach has a big potential to identify such potential hazards based on historical flight data.
Adaptive depth estimation for pyramid multi-view stereo

In this paper, we propose a Multi-View Stereo (MVS) network which can perform efficient high-resolution depth estimation with low memory consumption. Classical learning-based MVS approaches typically construct 3D cost volumes to regress depth information, making the output resolution rather limited as the memory consumption grows cubically with the input resolution. Although recent approaches have made significant progress in scalability by introducing the coarse-to-fine fashion or sequential cost map regularization, the memory consumption still grows quadratically with input resolution and is not friendly for commodity GPU. Observing that the surfaces of most objects in real world are locally smooth, we assume that most of the depth hypotheses upsampled from a well-estimated depth map are accurate. Based on the assumption, we propose a pyramid MVS network based on the adaptive depth estimation, which gradually refines and upsamples the depth map to the desired resolution. Instead of estimating depth hypotheses for all pixels in the depth map, our method only performs prediction at adaptively selected locations, alleviating excessive computation on well-estimated positions. To estimate depth hypotheses for sparse selected locations, we propose the lightweight pixelwise depth estimation network, which can estimate depth value for each selected location independently. Experiments demonstrate that our method can generate results comparable with the state-of-the-art learning-based methods while reconstructing more geometric details and consuming less GPU memory.
CGMVAE: Coupling GMM Prior and GMM Estimator for Unsupervised Clustering and Disentanglement

Impressive progress has been recently witnessed on deep unsupervised clustering and feature disentanglement. In this paper, we propose a novel method on top of one recent architecture with a novel explanation of Gaussian mixture model (GMM) membership, accompanied by a GMM loss to enhance the clustering. The GMM loss is optimized with the explicitly computed parameters under our coupled GMM inspired framework. Specifically, our model takes the advantage of implicitly learning a GMM in latent space by neural networks (GMM prior as the first GMM), and explicitly clustering via the other GMM framework (GMM estimator as the second GMM). We further introduce a Dirichlet conjugate loss as a regularization term to prevent the GMM estimator from degenerating to few Gaussians. Eventually, we further propose an application of apparel generation based on the proposed method which requires only three selection steps. Extensive experiments on publicly available datasets demonstrate the effectiveness of our method, in terms of clustering and disentanglement performance.
Decolonizing Design Practices: Towards Pluriversality

Decolonizing discourses teach us that we need to move away from the universalizing ‘grand narratives’ of knowledge production and focus on contextualizing diverse and situated experiences, epistemologies and narratives. Yet, few contributions actively demonstrate what a shift to decolonizing design means in practice. Participatory Design (PD) approaches are particularly well-suited to contributing to contemporary debates of decolonization in design due to PD’s long-standing political traditions and values of equality and empowerment, but even here empirical methods and techniques to fully realize pluriversality in design are lacking. In line with the CHI 2021 theme of Making Waves. Combining Strengths, this interactive workshop will invigorate the debates and practices in HCI of decolonization by bringing together and demonstrating how designers and researchers in diverse global contexts are working with and adapting modes, concepts, methodologies and sensibilities into decolonizing design practices. Not only will this workshop provide new ways of thinking in HCI but also fuse theories and practices to develop truly transcultural approaches to HCI.