Reviewerly/exHarmony huggingface dataset:
A Novel Two-stage Separable Deep Learning Framework for Practical Blind Watermarking

As a vital copyright protection technology, blind watermarking based on deep learning with an end-to-end encoder-decoder architecture has been recently proposed. Although the one-stage end-to-end training (OET) facilitates the joint learning of encoder and decoder, the noise attack must be simulated in a differentiable way, which is not always applicable in practice. In addition, OET often encounters the problems of converging slowly and tends to degrade the quality of watermarked images under noise attack. In order to address the above problems and improve the practicability and robustness of algorithms, this paper proposes a novel two-stage separable deep learning (TSDL) framework for practical blind watermarking. Precisely, the TSDL framework is composed of noise-free end-to-end adversary training (FEAT) and noise-aware decoder-only training (ADOT). A redundant multi-layer feature encoding network is developed in FEAT to obtain the encoder, while ADOT is used to get the decoder which is robust and practical enough to accept any type of noise. Extensive experiments demonstrate that the proposed framework not only exhibits better stability, greater performance and faster convergence speed compared with current state-of-the-art OET methods, but is also able to resist high-intensity noises that have not been tested in previous works.
Influence Function based Data Poisoning Attacks to Top-N Recommender Systems

Recommender system is an essential component of web services to engage users. Popular recommender systems model user preferences and item properties using a large amount of crowdsourced user-item interaction data, e.g., rating scores; then top-N items that match the best with a user’s preference are recommended to the user. In this work, we show that an attacker can launch a data poisoning attack to a recommender system to make recommendations as the attacker desires via injecting fake users with carefully crafted user-item interaction data. Specifically, an attacker can trick a recommender system to recommend a target item to as many normal users as possible. We focus on matrix factorization based recommender systems because they have been widely deployed in industry. Given the number of fake users the attacker can inject, we formulate the crafting of rating scores for the fake users as an optimization problem. However, this optimization problem is challenging to solve as it is a non-convex integer programming problem. To address the challenge, we develop several techniques to approximately solve the optimization problem. For instance, we leverage influence function to select a subset of normal users who are influential to the recommendations and solve our formulated optimization problem based on these influential users. Our results show that our attacks are effective and outperform existing methods.
Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders

A major obstacle in Word Sense Disambiguation (WSD) is that word senses are not uniformly distributed, causing existing models to generally perform poorly on senses that are either rare or unseen during training.We propose a bi-encoder model that independently embeds (1) the target word with its surrounding context and (2) the dictionary definition, or gloss, of each sense.The encoders are jointly optimized in the same representation space, so that sense disambiguation can be performed by finding the nearest sense embedding for each target word embedding.Our system outperforms previous state-of-the-art models on English all-words WSD; these gains predominantly come from improved performance on rare senses, leading to a 31.1% error reduction on less frequent senses over prior work.This demonstrates that rare senses can be more effectively disambiguated by modeling their definitions.
Re-thinking Co-Salient Object Detection

Existing CoSOD datasets often have a serious data bias, assuming that each group of images contains salient objects of similar visual appearances. This bias can lead to the ideal settings and effectiveness of models trained on existing datasets, being impaired in real-life situations, where similarities are usually semantic or conceptual. To tackle this issue, we first introduce a new benchmark, called CoSOD3k in the wild, which requires a large amount of semantic context, making it more challenging than existing CoSOD datasets. Our CoSOD3k consists of 3,316 high-quality, elaborately selected images divided into 160 groups with hierarchical annotations. The images span a wide range of categories, shapes, object sizes, and backgrounds. Second, we integrate the existing SOD techniques to build a unified, trainable CoSOD framework, which is long overdue in this field. Specifically, we propose a novel CoEG-Net that augments our prior model EGNet with a co-attention projection strategy to enable fast common information learning. CoEG-Net fully leverages previous large-scale SOD datasets and significantly improves the model scalability and stability. Third, we comprehensively summarize 34 cutting-edge algorithms, benchmarking 16 of them over three challenging CoSOD datasets, and reporting group-level performance analysis. Finally, we discuss the challenges and future works of CoSOD.
Make It a Chorus

Traditional recommender systems mainly aim to model inherent and long-term user preference, while dynamic user demands are also of great importance. Typically, a historical consumption will have impacts on the user demands for its relational items. For instance, users tend to buy complementary items together (iPhone and Airpods) but not substitutive items (Powerbeats and Airpods), although substitutes of the bought one still cater to his/her preference. To better model the effects of history sequence, previous studies introduce the semantics of item relations to capture user demands for recommendation. However, we argue that the temporal evolution of the effects caused by different relations cannot be neglected. In the example above, user demands for headphones can be promoted after a long period when a new one is needed.
Entanglement in a 20-Qubit Superconducting Quantum Computer

Abstract The ability to prepare sizeable multi-qubit entangled states with full qubit control is a critical milestone for physical platforms upon which quantum computers are built. We investigate the extent to which entanglement is found within a prepared graph state on the 20-qubit superconducting quantum computer IBM Q Poughkeepsie . We prepared a graph state along a path consisting of all twenty qubits within the device and performed full quantum state tomography on all groups of four connected qubits along this path. We determined that each pair of connected qubits was inseparable and hence the prepared state was entangled. Additionally, a genuine multipartite entanglement witness was measured on all qubit subpaths of the graph state and we found genuine multipartite entanglement on chains of up to three qubits. These results represent a demonstration of entanglement in one of the largest solid-state qubit arrays to date and indicate the positive direction of progress towards the goal of implementing complex quantum algorithms relying on such effects.
One-Class Learning Towards Synthetic Voice Spoofing Detection

Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of ASV systems against spoofing attacks. However, most methods encounter difficulties in detecting unknown attacks in practical use, which often have different statistical distributions from known attacks. Especially, the fast development of synthetic voice spoofing algorithms is generating increasingly powerful attacks, putting the ASV systems at risk of unseen attacks. In this work, we propose an anti-spoofing system to detect unknown synthetic voice spoofing attacks (i.e., text-to-speech or voice conversion) using one-class learning. The key idea is to compact the bona fide speech representation and inject an angular margin to separate the spoofing attacks in the embedding space. Without resorting to any data augmentation methods, our proposed system achieves an equal error rate (EER) of 2.19% on the evaluation set of ASVspoof 2019 Challenge logical access scenario, outperforming all existing single systems ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , those without model ensemble).
Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network

Transformer-based architectures have shown great success in image captioning, where object regions are encoded and then attended into the vectorial representations to guide the caption decoding. However, such vectorial representations only contain region-level information without considering the global information reflecting the entire image, which fails to expand the capability of complex multi-modal reasoning in image captioning. In this paper, we introduce a Global Enhanced Transformer (termed GET) to enable the extraction of a more comprehensive global representation, and then adaptively guide the decoder to generate high-quality captions. In GET, a Global Enhanced Encoder is designed for the embedding of the global feature, and a Global Adaptive Decoder are designed for the guidance of the caption generation. The former models intra- and inter-layer global representation by taking advantage of the proposed Global Enhanced Attention and a layer-wise fusion module. The latter contains a Global Adaptive Controller that can adaptively fuse the global information into the decoder to guide the caption generation. Extensive experiments on MS COCO dataset demonstrate the superiority of our GET over many state-of-the-arts.
Automatic ICD-9 coding via deep transfer learning

ICD-9 codes have been widely used to describe a patient's diagnosis. Accurate automatic ICD-9 coding is important because manual coding is expensive, time-consuming. Inspired by the recent successes of deep transfer learning, in this study, we propose a deep transfer learning framework for automatic ICD-9 coding. Our proposed method makes use of transferring MeSH domain knowledge to improve automatic ICD-9 coding. We demonstrate its effectiveness by achieving state-of-the-art performance with a value of 0.420 for Micro-average F-measure on MIMIC-III dataset, which indicates that our method outperforms hierarchy-based SVM and flat-SVM. Furthermore, we analyze the deep neural network structure to discover the vital elements in the success of our proposed method. Our experimental results indicate that transfer learning is the key component to improve the performance of automatic ICD-9 coding and deep learning approach is the foundation in the success of our proposed model. In addition, to explore the best network architecture, we also compare the performance of multi-scale and sequential network architectures and find that using multi-scale network is better. Finally, we investigate the effects of transferring different percentage of samples on transfer learning and the results show that the best performance of target domain task can be obtained when 100% number samples are transferred.
Adoption of augmented reality technology by university students

In recent times, Augmented Reality has gained more relevance in the field of education. This relevance has been enhanced due to its ease of use, as well as the availability of the technical devices for the students. The present study was conducted with students enrolled in the Pedagogy Degree in the Faculty of Education at the University of Seville. The objective was to understand the degree of technological acceptance of students during their interaction with the AR objects produced, the performance achieved by the students, and if their gender affected their acquisition of knowledge. For this, three data collection instruments were utilized: a multiple choice test for the analysis of the student's performance after the interaction, the Technology Acceptance Model (TAM) diagnostic instrument, created by Davis (1989), and an "ad hoc" instrument created so that the students could evaluate the class notes enriched with the AR objects created. The study has allowed us to broaden the scientific knowledge of the TAM by Davis, to understand that AR objects can be utilized in university teaching, and to know that the student's gender does not influence learning.
Understanding Undesirable Word Embedding Associations

Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes.However, methods for measuring and removing such biases remain poorly understood.We show that for any embedding model that implicitly does matrix factorization, debiasing vectors post hoc using subspace projection (Bolukbasi et al., 2016) is, under certain conditions, equivalent to training on an unbiased corpus.We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias.Given that the subspace projection method is provably effective, we use it to derive a new measure of association called the relational inner product association (RIPA).Experiments with RIPA reveal that, on average, skipgram with negative sampling (SGNS) does not make most words any more gendered than they are in the training corpus.However, for gender-stereotyped words, SGNS actually amplifies the gender association in the corpus.
Mask R-CNN With Pyramid Attention Network for Scene Text Detection

In this paper, we present a new Mask R-CNN based text detection approach which can robustly detect multi-oriented and curved text from natural scene images in a unified manner. To enhance the feature representation ability of Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention Network (PAN) as a new backbone network of Mask R-CNN. Experiments demonstrate that PAN can suppress false alarms caused by text-like backgrounds more effectively. Our proposed approach has achieved superior performance on both multi-oriented (ICDAR-2015, ICDAR-2017 MLT) and curved (SCUT-CTW1500) text detection benchmark tasks by only using single-scale and single-model testing.
Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model

In this paper, we analyze Twitter signals as a medium for user sentiment to predict the price fluctuations of a small-cap alternative cryptocurrency called \emph{ZClassic}. We extracted tweets on an hourly basis for a period of 3.5 weeks, classifying each tweet as positive, neutral, or negative. We then compiled these tweets into an hourly sentiment index, creating an unweighted and weighted index, with the latter giving larger weight to retweets. These two indices, alongside the raw summations of positive, negative, and neutral sentiment were juxtaposed to $\sim 400$ data points of hourly pricing data to train an Extreme Gradient Boosting Regression Tree Model. Price predictions produced from this model were compared to historical price data, with the resulting predictions having a 0.81 correlation with the testing data. Our model's predictive data yielded statistical significance at the $p < 0.0001$ level. Our model is the first academic proof of concept that social media platforms such as Twitter can serve as powerful social signals for predicting price movements in the highly speculative alternative cryptocurrency, or ``alt-coin'', market.
Clinical big data and deep learning: Applications, challenges, and future outlooks

The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning.In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition.This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data.Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets.Secondly, a brief review of common deep learning models and their characteristics is conducted.Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks.Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine.
Bridging the Day and Night Domain Gap for Semantic Segmentation

Perception in autonomous vehicles has progressed exponentially in the last years thanks to the advances of vision-based methods such as Convolutional Neural Networks (CNNs). Current deep networks are both efficient and reliable, at least in standard conditions, standing as a suitable solution for the perception tasks of autonomous vehicles. However, there is a large accuracy downgrade when these methods are taken to adverse conditions such as nighttime. In this paper, we study methods to alleviate this accuracy gap by using recent techniques such as Generative Adversarial Networks (GANs). We explore diverse options such as enlarging the dataset to cover these domains in unsupervised training or adapting the images on-the-fly during inference to a comfortable domain such as sunny daylight in a pre-processing step. The results show some interesting insights and demonstrate that both proposed approaches considerably reduce the domain gap, allowing IV perception systems to work reliably also at night.
Practical Deep Learning with Bayesian Principles

Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this paper, we demonstrate practical training of deep networks with natural-gradient variational inference. By applying techniques such as batch normalisation, data augmentation, and distributed training, we achieve similar performance in about the same number of epochs as the Adam optimiser, even on large datasets such as ImageNet. Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated, uncertainties on out-of-distribution data are improved, and continual-learning performance is boosted. This work enables practical deep learning while preserving benefits of Bayesian principles. A PyTorch implementation is available as a plug-and-play optimiser.
Seeing isn't Believing

Recently Adversarial Examples (AEs) that deceive deep learning models have been a topic of intense research interest. Compared with the AEs in the digital space, the physical adversarial attack is considered as a more severe threat to the applications like face recognition in authentication, objection detection in autonomous driving cars, etc. In particular, deceiving the object detectors practically, is more challenging since the relative position between the object and the detector may keep changing. Existing works attacking object detectors are still very limited in various scenarios, e.g., varying distance and angles, etc. In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors. Particularly, for Hiding Attack (HA), we proposed thefeature-interference reinforcement (FIR) method and theenhanced realistic constraints generation (ERG) to enhance robustness, and for Appearing Attack (AA), we proposed thenested-AE, which combines two AEs together to attack object detectors in both long and short distance. We also designed diverse styles of AEs to make AA more surreptitious. Evaluation results show that our AEs can attack the state-of-the-art real-time object detectors (i.e., YOLO V3 and faster-RCNN) at the success rate up to 92.4% with varying distance from 1m to 25m and angles from -60º to 60º. Our AEs are also demonstrated to be highly transferable, capable of attacking another three state-of-the-art black-box models with high success rate.
Relation Network for Person Re-Identification

Person re-identification (reID) aims at retrieving an image of the person of interest from a set of images typically captured by multiple cameras. Recent reID methods have shown that exploiting local features describing body parts, together with a global feature of a person image itself, gives robust feature representations, even in the case of missing body parts. However, using the individual part-level features directly, without considering relations between body parts, confuses differentiating identities of different persons having similar attributes in corresponding parts. To address this issue, we propose a new relation network for person reID that considers relations between individual body parts and the rest of them. Our model makes a single part-level feature incorporate partial information of other body parts as well, supporting it to be more discriminative. We also introduce a global contrastive pooling (GCP) method to obtain a global feature of a person image. We propose to use contrastive features for GCP to complement conventional max and averaging pooling techniques. We show that our model outperforms the state of the art on the Market1501, DukeMTMC-reID and CUHK03 datasets, demonstrating the effectiveness of our approach on discriminative person representations.
Multi-Stage Feature Constraints Learning for Age Estimation

The biometric information contained in a face image is affected by many factors such as living environment, racial differences, and genetic diversity, this complexity leads to the nonstationary of the age estimation. In order to reduce the overlap of face features between adjacent ages and improve the accuracy of age prediction, a multi-stage feature constraints learning method is proposed for face age estimation. The proposed method gradually refines the feature through three feature constraint stages. In each stage, the algorithm continuously updates the feature center of its corresponding age range, and minimizes the distance between each age feature and feature center of the corresponding age range through feature constraint. Feature constraint makes the feature distances between different individuals in the same age feature space smaller and decrease the overlap areas between adjacent age range feature spaces. Meanwhile, the feature distance of different age range feature space is enlarged. The proposed network efficiently merges the features of three stages and optimizes the mapping of feature maps to an ordered binary comparison space. Experiments show that the proposed method is able to effectively improve the discrimination between different age features, and hence to improve the accuracy of face age estimation. In addition, the proposed algorithm is simple enough to achieve fast face age estimation.