Reviewerly/exHarmony huggingface dataset:
RPD-GAN: Learning to Draw Realistic Paintings With Generative Adversarial Network

Painting style transfer is an attractive and challenging computer vision problem that aims to transfer painting styles onto natural images. Existing advanced methods tackle this problem from the perspective of Neural Style Transfer (NST) or unsupervised cross-domain image translation. For both two types of methods, attention has been focused on reproducing artistic painting styles of representative artists (e.g., Vincent Van Gogh). In this paper, instead of transferring styles of artistic paintings, we focus on automatic generation of realistic paintings, for example, making the machine draw a gouache before a still life, paint a sketch of a landscape, or draw a pen-and-ink portrait of a person, etc. Besides capturing the precise target styles, synthesis of realistic paintings is more demanding in preserving original content features and image structures, for which existing advanced methods are not sufficient to generate satisfactory results. Aimed at this problem, we propose RPD-GAN (Realistic Painting Drawing Generative Adversarial Network), an unsupervised cross-domain image translation framework for realistic painting style transfer. At the heart of our model is the decomposition of the image stylization mapping into four stages: feature encoding, feature de-stylization, feature re-stylization, and feature decoding, where the functionalities of these stages are implemented by additionally embedding a content-consistency constraint and a style-alignment constraint at feature space to the classic CycleGAN architecture. By enforcing these constraints, both the content-preserving and style-capturing capabilities of the model are enhanced, leading to higher-quality stylization results. Extensive experiments demonstrate the effectiveness and superiority of our RPD-GAN in drawing realistic paintings.
Underwater Image Restoration Using Geodesic Color Distance and Complete Image Formation Model

Underwater images suffer from different types of quality degradation, including haze, blur, low contrast, and color distortion, owing to light scattering and absorption.This article proposes a novel underwater image restoration algorithm based on the complete underwater image formation model (UIFM).Although the majority of the existing methods consider the direct transmission and backward scattering components only, this study, in addition, includes forward scattering in the UIFM.We estimate the transmission map based on the observation that the scene distance is inversely proportional to the geodesic color distance from the background light.We also approximate the point spread function in the forward scattering term to estimate the scene radiance more faithfully.Moreover, we obtain the optimal parameters of the UIFM required for transmission estimation and scene radiance restoration by minimizing a cost function composed of the sharpness, information loss, and dark background prior.The experimental results confirm that the proposed algorithm considerably improves the quality of the estimated transmission maps and restores scene radiance compared with the existing state-of-the-art methods.
SpeakerGAN: Speaker identification with conditional generative adversarial network

Current methods based on the traditional i-vectors and deep neural network (DNN) have shown effectiveness on the speaker identification task, especially with the corpus of large scale. However, when the size of the training dataset is small, the overfitting problem may happen and lead to performance degradation. Besides, the robust identification still remains a challenging problem even under the less strict requirements. This paper proposes a novel approach, SpeakerGAN, for speaker identification with the conditional generative adversarial network (CGAN). It allows the adversarial networks for distinguishing real/fake samples and predicting class labels simultaneously. We configure the generator and the discriminator in SpeakerGAN with the gated convolutional neural network (CNN) and the modified residual network (ResNet) to obtain generated samples of high diversity as well as increase the network capacity. The multiple loss functions are combined and optimized to encourage the correct mapping and accelerate the convergence. Experimental results show that SpeakerGAN reduces the classification error rate by 87% and 16% compared with the traditional i-vector system and the state-of-the-art DNN based method. Under the scenario of limited training data, SpeakerGAN obtains significant improvement over the baselines. In the case of taking 1.6 s of each speaker for testing, SpeakerGAN achieves the identification accuracy of 98.20%, which suggests the promise for short-utterance speaker identification.
Orthogonality Loss: Learning Discriminative Representations for Face Recognition

Convolutional neural networks have achieved excellent performance on face recognition (FR) by learning the high discriminative features with advanced loss functions. These improved loss functions share the similar idea for maximizing inter-class variance or minimizing intra-class variance. In this article, from a different perspective, we consider enlarging the inter-class variance by directly penalizing weight vectors of last fully connected layer, which represent the center of classes. To the end, we propose Orthogonality loss as an elegant penalty item appends to common classification loss to learn the discriminative representations. The main idea is that in order for weight vectors to be discriminative, it should be as close as possible to be orthogonal to each other in the vector space. More specifically, the optimization objective of Orthogonality loss is the first moment and second moment of cosine similarity of weight vectors. We performed the empirical studies through simulating the long-tail datasets to show the generalization ability of the proposed approach on long-tail distribution datasets. Further, extensive experiments on large-scale face recognition benchmarks including the Labeled Face in the Wild (LFW), the IARPA Janus Benchmark A (IJB-A), IJB-B, IJB-C, MegaFace Challenge 1 (MF1) and MS-Celeb-1M Low-shot Learning demonstrated that Orthogonality loss outperforms strong baselines, which showcases the extensive suitability and effectiveness of Orthogonality loss.
Zero-Shot Learning for Cross-Lingual News Sentiment Classification

In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment category not only to Slovene news, but to news in another language without any training data required. Our system is based on the multilingual BERTmodel, while we test different approaches for handling long documents and propose a novel technique for sentiment enrichment of the BERT model as an intermediate training step. With the proposed approach, we achieve state-of-the-art performance on the sentiment analysis task on Slovenian news. We evaluate the zero-shot cross-lingual capabilities of our system on a novel news sentiment test set in Croatian. The results show that the cross-lingual approach also largely outperforms the majority classifier, as well as all settings without sentiment enrichment in pre-training.
A Framework for Educational and Training Immersive Virtual Reality Experiences

The fast development and progressive price reduction of Virtual Reality (VR) technologies promotes their implementation in areas beyond gaming. This makes it increasingly applied in other areas such as the development of educational or training applications. However, the development of these applications from scratch can involve a very high time and research investment. To solve this limitation, this paper presents an immersive virtual reality framework that simplifies the development of applications and allows researchers to focus on the design of the educational or training experience, once the framework already solve the main technical issues of the VR environment's development. This Virtual Reality framework is based in Unreal Engine, therefore compatible with most virtual reality helmets on the market. The framework includes tools for all the required tasks to create virtual reality experiences: movement of the player, interactions with the scenario and objects and the creation of scene objectives among others. To validate its suitability, two cultural heritage environments have been used, in which it has been applied to create two educational experiences.
Video saliency prediction using enhanced spatiotemporal alignment network

Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP). To address this issue, we develop an effective spatiotemporal feature alignment network tailored to VSP, mainly including two key sub-networks: a multi-scale deformable convolutional alignment network (MDAN) and a bidirectional convolutional Long Short-Term Memory (Bi-ConvLSTM) network. The MDAN learns to align the features of the neighboring frames to the reference one in a coarse-to-fine manner, which can well handle various motions. Specifically, the MDAN owns a pyramidal feature hierarchy structure that first leverages deformable convolution (Dconv) to align the lower-resolution features across frames, and then aggregates the aligned features to align the higher-resolution features, progressively enhancing the features from top to bottom. The output of MDAN is then fed into the Bi-ConvLSTM for further enhancement, which captures the useful long-time temporal information along forward and backward timing directions to effectively guide attention orientation shift prediction under complex scene transformation. Finally, the enhanced features are decoded to generate the predicted saliency map. The proposed model is trained end-to-end without any intricate post processing. Extensive evaluations on four VSP benchmark datasets demonstrate that the proposed method achieves favorable performance against state-of-the-art methods. The source codes and all the results will be released at https://github.com/cj4L/ESAN-VSP.
Framing a discussion on paradigm shift(s) in the field of information

Abstract In this opinion paper, we frame a discussion on paradigm shift(s) in the field of information. We believe that in this astonishing historical moment of new directions and new opportunities both the existing paradigms and conceptual models in the field of information can benefit from re‐examination to stay current with the times. We propose a framework articulating key narratives associated with the why, what, how, and who dimensions to discuss paradigm shift(s). The purpose of this opinion paper is to initiate dialogues on ground‐breaking ideas and innovative solutions as well as support research that addresses contemporary challenges in the field of information.
Empirical software product line engineering: A systematic literature review

The adoption of Software Product Line Engineering (SPLE) is usually only based on its theoretical benefits instead of empirical evidences. In fact, there is no work that synthesizes the empirical studies on SPLE. This makes it difficult for researchers to base their contributions on previous works validated with an empirical strategy. The objective of this work is to discover and summarize the studies that have used empirical evidences in SPLE limited to those ones with the intervention of humans. This will allow evaluating the quality and to know the scope of these studies over time. Doing so, research opportunities can arise A systematic literature review was conducted. The scope of the work focuses on those studies in which there is human intervention and were published between 2000 and 2018. We considered peer-reviewed papers from journals and top software engineering conferences. Out of a total of 1880 studies in the initial set, a total of 62 primary studies were selected after applying a series of inclusion and exclusion criteria. We found that, approximately 56% of the studies used the empirical case study strategy while the rest used experimental strategies. Around 86% of the case studies were performed in an industrial environment showing the penetration of SPLE in industry. The interest of empirical studies has been growing since 2008. Around 95.16% of the studies address aspects related to domain engineering while application engineering received less attention. Most of the experiments and case study evaluated showed an acceptable level of quality. The first study found dates from 2005 and since then, the interest in the empirical SPLE has increased.
A Robust Quality Enhancement Method Based on Joint Spatial-Temporal Priors for Video Coding

Quality enhancement of HEVC compressed videos has attracted a lot of attentions in recent years. In this article, we propose a robust multi-frame guided attention network (MGANet) to reconstruct high-quality frames based on HEVC compressed videos. In our network, we first use an advanced motion flow algorithm to estimate the motion information of input frames so as to guide the warping of adjacent frames. After performing the alignment, we find that large residuals still appear in the edge area of moving objects of the warped frames. Then, we design a temporal encoder based on a bi-directional convolutional long short term memory (ConvLSTM) with residual structure to further discover the variations between the current frame and its adjacent warped frames. Finally, we feed the extracted temporal information and a partitioned average image (PAI) to a multi-scale guided encoder-decoder subnet to reconstruct high-quality frames. Here, each PAI is generated according to the transform unit (TU) partitioning map that can be extracted directly from the coded bit-streams, thus enabling our network to focus on the TU boundaries while optimizing the global content. We present extensive experimental results to demonstrate the robustness of our method, especially for the high bit-rate coding case and large motion scenes. Due to the lightweight design structure, our proposed MGANet also has a very competitive inference time.
Is disruptive innovation in emerging economies different? Evidence from China

Most studies on disruptive innovations have focused on developed economies and little work examines this type of innovation in and from emerging economies. Moreover, previous studies on disruptive innovations have not investigated the processes behind. This study begins to fill these research gaps. Analysing disruptive innovations in and from China, we identify three important differences from the kinds of disruption observed in developed economies. First, rather than being based on launching products with inferior performance, disruptive innovations in China focus on offering different value propositions. Second, the rate at which Chinese disruptive innovations are improved and extended is typically faster than in developed markets. Third, Chinese disruptive innovations are often launched directly into a mass market rather than a niche. Besides identifying these differences, we also discuss how Chinese firms generate disruptive innovations. The findings expand the understanding of disruptive innovation and hence enrich the existing literature on this important phenomenon.
Learning smooth representations with generalized softmax for unsupervised domain adaptation

Domain adaptation aims at training accurate classifiers in a target domain by utilizing the data in a different but related source domain, which has made great progress in many aspects. Most of existing methods try to match the moments of data by mapping them to feature space, either first or second moment, so as to match the distribution. Despite their appeal, such models often fail to guarantee the obtained features can be well-classified. In this paper, we propose generalized softmax and smooth regularization to extract features and adapt classifiers simultaneously. Considering label matrix as special features, generalized softmax has more tolerance to the diversity of samples belonging to the same class. Smoothness regularization guarantees stronger robustness between target features and decision boundary. Finally, we evaluate our method on several standard benchmark datasets. Empirical evidence shows that the proposed method is comparable or superior to existing methods, and the same results based on two classification schemes indicate that the smoothness regularization is effective.
The impact of engineering information format on task performance: Gaze scanning pattern analysis

The emergence of new visualization technologies such as Virtual Reality (VR) and Augmented Reality (AR) had been widely implemented in the Architecture, Engineering, and Construction (AEC) industry. Although cumulative evidence pointed out a positive impact of these visualization technologies on construction task performance, there is still an obvious disagreement on the benefits or implications of these new visualization technologies, due to the lack of understanding of the mechanisms in which the visualization affects cognitive processes related to information processing. To obtain more evidence, this paper presents a human-subject experiment (n = 90) to investigate the impact of information format on the performance of an industrial pipeline maintenance task. The investigation centers around how different engineering information formats affect the attention patterns as a potential explanation for the changes in performance. A between-group experiment design was used where the participants were randomly assigned to one of the three groups (2D group, 3D group, and VR group) depending on what type of information was given to review the pipe operation instruction. After the review session, the participants were asked to perform the operation task in the virtual environment based on their memory. The results showed that the 3D and VR groups outperformed the 2D group in task performance. The analysis of eye-tracking data further indicated that the information format significantly changed the gaze scanning pattern when participants were reviewing the operational instructions. We also found that the task performance was correlated with eye-tracking features including gaze movement and pupil dilation. Our findings provided more evidence about the mechanisms in which new visualization technologies affect the attention patterns, helped resolve the current disagreement within the literature. In addition, a prediction model was proposed to use eye-tracking features to predict construction task performance.
M-M Estimation-Based Robust Cubature Kalman Filter for INS/GPS Integrated Navigation System

In the engineering applications, Kalman filter (KF) is the most commonly used for INS/GPS integration. This KF-based integration is prone to divergence because of two typical problems. One is that the predicted state and measurement are contaminated by non-Gaussian noise. The other is that the initial attitude error is too large. Until now, there arise many algorithms dealing with only one of these two problems, but the algorithms which can deal with both two problems are few. Motived by this situation, an M-M estimation-based cubature Kalman filter (MMCKF) is proposed which innovatively combines the M-M estimation and nonlinear cubature Kalman filter (CKF) to enhance the performance of INS/GPS integration system in the case that above two problems occur at the same time. Simulation and vehicle-mounted experiment results validate its accuracy and robustness.
Immunity passports, fundamental rights and public health hazards: a reply to Brown <i>et al</i>

In their recent article, Brown et al analyse several ethical aspects around immunity passports and put forward some recommendations for implementing them. Although they offer a comprehensive perspective, they overlook two essential aspects. First, while the authors consider the possibility that immunological passports may appear to discriminate against those who do not possess them, the opposite viewpoint of immune people is underdeveloped. We argue that if a person has been tested positive for and recovered from COVID-19, becoming immune to it, she cannot be considered a hazard to public health and, therefore, the curtailment of her fundamental rights (eg, the right to freedom of movement) is not legitimate. Second, they omit that vaccine distribution will create similar problems related to immunity-based licenses. Vaccine certificates will de facto generate a sort of immunity passport. In the next phases of the pandemic, different immunity statuses will be at stake, because the need to identify who can spread COVID-19 is unavoidable. If a person does not pose a threat to public health because she cannot spread the infection, then her right to freedom of movement should be respected, regardless of how she acquired that immunity.
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets

Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on task-specific imaging protocols including a task-specific set of imaging modalities. In this work, we propose a novel approach to build a joint tissue and lesion segmentation model from aggregated task-specific hetero-modal domain-shifted and partially-annotated datasets. Starting from a variational formulation of the joint problem, we show how the expected risk can be decomposed and optimised empirically. We exploit an upper bound of the risk to deal with heterogeneous imaging modalities across datasets. To deal with potential domain shift, we integrated and tested three conventional techniques based on data augmentation, adversarial learning and pseudo-healthy generation. For each individual task, our joint approach reaches comparable performance to task-specific and fully-supervised models. The proposed framework is assessed on two different types of brain lesions: White matter lesions and gliomas. In the latter case, lacking a joint ground-truth for quantitative assessment purposes, we propose and use a novel clinically-relevant qualitative assessment methodology.
FSST

Strings are prevalent in real-world data sets. They often occupy a large fraction of the data and are slow to process. In this work, we present Fast Static Symbol Table (FSST), a lightweight compression scheme for strings. On text data, FSST offers decompression and compression speed similar to or better than the best speed-optimized compression methods, such as LZ4, yet offers significantly better compression factors. Moreover, its use of a static symbol table allows random access to individual, compressed strings, enabling lazy decompression and query processing on compressed data. We believe these features will make FSST a valuable piece in the standard compression toolbox.