Reviewerly/exHarmony huggingface dataset:
Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

Many scholarly journals have established their own data-related policies, which specify their enforcement of data sharing, the types of data to be submitted, and their procedures for making data available. However, except for the journal impact factor and the subject area, the factors associated with the overall strength of the data sharing policies of scholarly journals remain unknown. This study examines how factors, including impact factor, subject area, type of journal publisher, and geographical location of the publisher are related to the strength of the data sharing policy.From each of the 178 categories of the Web of Science's 2017 edition of Journal Citation Reports, the top journals in each quartile (Q1, Q2, Q3, and Q4) were selected in December 2018. Of the resulting 709 journals (5%), 700 in the fields of life, health, and physical sciences were selected for analysis. Four of the authors independently reviewed the results of the journal website searches, categorized the journals' data sharing policies, and extracted the characteristics of individual journals. Univariable multinomial logistic regression analyses were initially conducted to determine whether there was a relationship between each factor and the strength of the data sharing policy. Based on the univariable analyses, a multivariable model was performed to further investigate the factors related to the presence and/or strength of the policy.Of the 700 journals, 308 (44.0%) had no data sharing policy, 125 (17.9%) had a weak policy, and 267 (38.1%) had a strong policy (expecting or mandating data sharing). The impact factor quartile was positively associated with the strength of the data sharing policies. Physical science journals were less likely to have a strong policy relative to a weak policy than Life science journals (relative risk ratio [RRR], 0.36; 95% CI [0.17-0.78]). Life science journals had a greater probability of having a weak policy relative to no policy than health science journals (RRR, 2.73; 95% CI [1.05-7.14]). Commercial publishers were more likely to have a weak policy relative to no policy than non-commercial publishers (RRR, 7.87; 95% CI, [3.98-15.57]). Journals by publishers in Europe, including the majority of those located in the United Kingdom and the Netherlands, were more likely to have a strong data sharing policy than a weak policy (RRR, 2.99; 95% CI [1.85-4.81]).These findings may account for the increase in commercial publishers' engagement in data sharing and indicate that European national initiatives that encourage and mandate data sharing may influence the presence of a strong policy in the associated journals. Future research needs to explore the factors associated with varied degrees in the strength of a data sharing policy as well as more diverse characteristics of journals related to the policy strength.
Exploring Font-independent Features for Scene Text Recognition

Scene text recognition (STR) has been extensively studied in last few years. Many recently-proposed methods are specially designed to accommodate the arbitrary shape, layout and orientation of scene texts, but ignoring that various font (or writing) styles also pose severe challenges to STR. These methods, where font features and content features of characters are tangled, perform poorly in text recognition on scene images with texts in novel font styles. To address this problem, we explore font-independent features of scene texts via attentional generation of glyphs in a large number of font styles. Specifically, we introduce trainable font embeddings to shape the font styles of generated glyphs, with the image feature of scene text only representing its essential patterns. The generation process is directed by the spatial attention mechanism, which effectively copes with irregular texts and generates higher-quality glyphs than existing image-to-image translation methods. Experiments conducted on several STR benchmarks demonstrate the superiority of our method compared to the state of the art.
Improving Intra- and Inter-Modality Visual Relation for Image Captioning

It is widely shared that capturing relationships among multi-modality features would be helpful for representing and ultimately describing an image. In this paper, we present a novel Intra- and Inter-modality visual Relation Transformer to improve connections among visual features, termed I2RT. Firstly, we propose Relation Enhanced Transformer Block (RETB) for image feature learning, which strengthens intra-modality visual relations among objects. Moreover, to bridge the gap between inter-modality feature representations, we align them explicitly via Visual Guided Alignment (VGA) module. Finally, an end-to-end formulation is adopted to train the whole model jointly. Experiments on the MS-COCO dataset show the effectiveness of our model, leading to improvements on all commonly used metrics on the "Karpathy" test split. Extensive ablation experiments are conducted for the comprehensive analysis of the proposed method.
Texture Semantically Aligned with Visibility-aware for Partial Person Re-identification

In real person re-identification (ReID) tasks, pedestrians are often obscured by other pedestrians or objects; moreover, changes in poses or observation perspectives also commonly exist in partial-person ReID. To the best of our knowledge, few works simultaneously focus on these two issues. In this work, we propose a novel texture semantic alignment (TSA) approach with the visibility-aware for partial person ReID task where the occlusion issue and changes in poses are simultaneously explored in an end-to-end unified framework. Specifically, we first employ a texture alignment scheme with the semantic visibility of a person's image to solve the issue of changes in poses that can enhance the alignment and generalization capability of the models. Second, we design a human pose-based partial region alignment scheme to solve the occlusion problem that makes TSA method emphasize the shared body parts. Finally, these two networks jointly learn these aspects. Extensive experimental results demonstrate that our proposed TSA method is very effective and robust for simultaneously handling occlusion and changes in pose, and it can outperform state-of-the-art approaches by a large margin and achieves an improvement of 5% and 6.4% on the rank-1 accuracy over the visibility-aware part model (VPM) method (published in CVPR 2019) on the Partial ReID and Partial-iLIDS datasets, respectively.
Bi-space Interactive Cooperative Coevolutionary algorithm for large scale black-box optimization

Large scale black-box optimization problems arise in many fields of science and engineering, and many of existing algorithms for these problems still suffer from the “curse of dimensionality”. This paper proposes a generalized framework of Bi-space Interactive Cooperative Coevolutionary Algorithm (BICCA) with evolutions in two spaces. In the pattern space, the interacting patterns of variables are continuously excavated for the evolution of the groups for cooperative coevolution. In the search space, cooperative coevolution and global search are carried out adaptively to get better fitness. By adopting evolutions and interactions within two spaces, patterns evolve to provide better groupings while individuals evolve to reach better fitness. The problem decomposition is conducted along the optimization process, and no extra fitness evaluations are needed for problem decomposition. Experiments on widely-used benchmarks show that BICCA obtains competitive performance on high-dimensional optimization problems with different levels of dimensionality up to 10000.
Bi-MARS: A Bi-clustering based Memetic Algorithm for Recommender Systems

With the expansion of e-business, availability of items on the web is profuse as compared to the previous era. The task of finding relevant items from this pool of items available online is a time-consuming task. Collaborative Filtering (CF) is the foremost productive recommendation algorithm that helps the user find relevant items and thus increase the user's engagement. However, several drawbacks of CF, especially data sparsity, scalability, and relevance of items recommended to users are open research issues that pose serious challenges to precision of the algorithm. We proposed a novel Bi-clustering based Memetic Algorithm for Recommender Systems (Bi-MARS) based on the collaborative behavior of memes. Bi-clusters are created for discovering precise and localized neighborhood of the target user. Further, a novel local search to refine similarity values associated with neighborhood users and prediction score function for unrated items are formulated. We evaluated the performance of Bi-MARS on MovieLens dataset of three different sizes. Additionally, results are compared with eight other approaches namely, traditional CF, Probabilistic Latent Semantic Analysis, Non-negative Matrix Factorization, Entropy-based CF, Jaccard Coefficient-based Bi-clustering and Fusion, Combined Bi-clustering and Entropy-based CF (CBE-CF), and evolutionary approaches: Genetic Algorithm based Recommender System and Memetic Algorithm based Recommender System (MARS). Experimental results depict that Bi-MARS outperforms all above-said approaches. The precision of MARS is improved by 66.3% on Movielens dataset of size 100K, which is the most precise algorithm after Bi-MARS among all evolutionary algorithms considered. Further, CPU Time of CBE-CF, which portrays least mean absolute error, among all clustering techniques considered, is improved by 53.8% with Bi-MARS. Bi-MARS generates recommendations by finding the closest similarity vector of the target user, which contributes to the computational accuracy of the algorithm and thus the relevance of items.
Deep Neural Network Self-Distillation Exploiting Data Representation Invariance

To harvest small networks with high accuracies, most existing methods mainly utilize compression techniques such as low-rank decomposition and pruning to compress a trained large model into a small network or transfer knowledge from a powerful large model (teacher) to a small network (student). Despite their success in generating small models of high performance, the dependence of accompanying assistive models complicates the training process and increases memory and time cost. In this article, we propose an elegant self-distillation (SD) mechanism to obtain high-accuracy models directly without going through an assistive model. Inspired by the invariant recognition in the human vision system, different distorted instances of the same input should possess similar high-level data representations. Thus, we can learn data representation invariance between different distorted versions of the same sample. Especially, in our learning algorithm based on SD, the single network utilizes the maximum mean discrepancy metric to learn the global feature consistency and the Kullback-Leibler divergence to constrain the posterior class probability consistency across the different distorted branches. Extensive experiments on MNIST, CIFAR-10/100, and ImageNet data sets demonstrate that the proposed method can effectively reduce the generalization error for various network architectures, such as AlexNet, VGGNet, ResNet, Wide ResNet, and DenseNet, and outperform existing model distillation methods with little extra training efforts.
COVID-19 and the Rise of Participatory SIGINT: An Examination of the Rise in Government Surveillance Through Mobile Applications

The COVID-19 pandemic has triggered a significant growth in government surveillance techniques globally, primarily through the use of cell phone applications. However, although these applications can have actionable effects on public health efforts to control pandemics, the participatory or voluntary nature of these measures is obscuring the relationship between health information and traditional government surveillance techniques, potentially preventing effective oversight. Public health measures have traditionally been resistant to the integration of government-led intelligence techniques, such as signals intelligence (SIGINT), because of ethical and legal issues arising from the nature of surveillance techniques.We explore this rise of participatory SIGINT and its nature as an extension of biosurveillance through 3 drivers: the rise of surveillance capitalism, the exploitation of a public health crisis to obscure state of exception politics with a moral imperative, and the historically enduring nature of emergency-implemented surveillance measures.We conclude that although mobile applications may indeed be useful in containing pandemics, they should be subject to similar oversight and regulation as other government intelligence collection techniques.
Knowledge structures delineated by fuzzy skill maps

Skills represent the underlying cognitive abilities, which consist in methods, algorithms or tricks etc. Skills can be used to interpret individuals' knowledge states. A skill map assigns a subset of skills to each item q. The disjunctive model assumes that at least one of the assigned skills is acquired for the mastery of the item q, and the conjunctive model states that all of the assigned skills are necessary for the mastery of the item q. The latent cognitive abilities of different individuals are distinct. Different levels of proficiency in skills may be required to solve different items. It's natural to use a fuzzy skill map or a fuzzy skill multimap to represent the levels of proficiency in skills for solving items. A fuzzy skill map (Q,S,τ) assigns a fuzzy set of skills to each item q, which represents the level of proficiency in skills needed to solve the item. A fuzzy skill multimap (Q,S,μ) assigns a collection of fuzzy sets of skills to each item q, any fuzzy set C of skills in μ(q) can be viewed as an approach to solve the item q. A knowledge space and a simple closure space can be delineated by a fuzzy skill map via the disjunctive model and the conjunctive model respectively. A knowledge structure can be delineated via the competency model by a fuzzy skill multimap. Algorithms for delineating knowledge structures via the disjunctive model and the conjunctive model by a fuzzy skill map are only concerned with skills in S. Delineating the knowledge structure by a fuzzy skill multimap just depends on the competencies and the weak competencies. For any fuzzy skill map (Q,S,τ) or fuzzy skill multimap (Q,S,μ), the knowledge states delineated by some skills can be replaced by knowledge states delineated by some other skills. Having removed these skills, the relationship between items and skills can be described more concisely without changing the knowledge structure. A minimal set of skills can faithfully summarize the relationship between items and skills, which results in a substantial economy of storage in a computer memory and a high efficiency of building, testing and searching for knowledge structures.
Why pay more? A simple and efficient named entity recognition system for tweets

The current paper investigates the problem of multimodal named entity recognition from Twitter data. Named entity recognition (NER) is an important task in natural language processing and has been carefully studied in recent decades. NER from tweets is particularly challenging because of 1) tweets are limited in length, 2) contains noisy text, and 3) contains hashtags. Moreover often tweets are associated with images and hyperlinks. Existing works on tweet-NER mostly concentrate on multimodal deep learning based models neglecting the use of hand-crafted features and usage of hyperlinks. The current paper investigates the incorporation of hand-crafted features extracted from different modalities like images, hyperlinks while extracting named entities from tweet-text. A large set of hand-crafted features are extracted from different modalities (images, hyperlinks) and those are added with the features extracted by a hybrid deep-neural model, bi-directional LSTM and CNN, followed by a conditional random field to perform this task. Several variants of these models in association with different hand-crafted feature sets are designed. Extensive experimentations on a multimodal Twitter data (containing text, images and urls) illustrate that character level hand-crafted features significantly improve the performance of the systems. In a part of the paper, results of the proposed models are also shown on a standard NER dataset, CoNLL 2003 dataset.
Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild

We address the problem of unsupervised gaze correction in the wild, presenting a solution that works without the need of precise annotations of the gaze angle and the head pose. We created a new dataset called CelebAGaze consisting of two domains X, Y, where the eyes are either staring at the camera or somewhere else. Our method consists of three novel modules: the Gaze Correction module(GCM), the Gaze Animation module(GAM), and the Pretrained Autoencoder module (PAM). Specifically, GCM and GAM separately train a dual in-painting network using data from the domain X for gaze correction and data from the domain Y for gaze animation. Additionally, a Synthesis-As-Training method is proposed when training GAM to encourage the features encoded from the eye region to be correlated with the angle information, resulting in gaze animation achieved by interpolation in the latent space. To further preserve the identity information e.g., eye shape, iris color, we propose the PAM with an Autoencoder, which is based on Self-Supervised mirror learning where the bottleneck features are angle-invariant and which works as an extra input to the dual in-painting models. Extensive experiments validate the effectiveness of the proposed method for gaze correction and gaze animation in the wild and demonstrate the superiority of our approach in producing more compelling results than state-of-the-art baselines. Our code, the pretrained models and supplementary results are available at:https://github.com/zhangqianhui/GazeAnimation.
Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE)

COVID-19 is a new pulmonary disease which is driving stress to the hospitals due to the large number of cases worldwide. Imaging of lungs can play a key role in the monitoring of health status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with significant success. However, this approach cannot be massively used, mainly for both high risk and cost, also in some countries, this tool is not extensively available. Alternatively, chest X-ray, although less sensitive than CT-scan, can provide important information about the evolution of pulmonary involvement during the disease; this aspect is very important to verify the response of a patient to treatments. Here, we show how to improve the sensitivity of chest X-ray via a nonlinear post-processing tool, named PACE (Pipeline for Advanced Contrast Enhancement), combining properly Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The results show an enhancement of the image contrast as confirmed by three widely used metrics: (i) contrast improvement index, (ii) entropy, and (iii) measure of enhancement. This improvement gives rise to a detectability of more lung lesions as identified by two radiologists, who evaluated the images separately, and confirmed by CT-scans. The results show this method is a flexible and an effective approach for medical image enhancement and can be used as a post-processing tool for medical image understanding and analysis.
Optoelectronic intelligence

General intelligence involves the integration of many sources of information into a coherent, adaptive model of the world. To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and electronics for computation are complementary and interdependent. Using light for communication enables high fan-out as well as low-latency signaling across large systems with no traffic-dependent bottlenecks. For computation, the inherent nonlinearities, high speed, and low power consumption of Josephson circuits are conducive to complex neural functions. Operation at 4 K enables the use of single-photon detectors and silicon light sources, two features that lead to efficiency and economical scalability. Here, I sketch a concept for optoelectronic hardware, beginning with synaptic circuits, continuing through wafer-scale integration, and extending to systems interconnected with fiber-optic tracts, potentially at the scale of the human brain and beyond.
MSCAN: Multimodal Self-and-Collaborative Attention Network for image aesthetic prediction tasks

With the ever-expanding volume of visual images on the Internet, automatic image aesthetic prediction is becoming more and more important in computer vision field. Considering the image aesthetic assessment is a highly subjective and complex task, some researchers resort to the user comments to aid aesthetic prediction. However, these methods only achieve limited success because 1) they rely heavily on convolution to extract visual features, which is difficult to capture the spatial interaction of visual elements in image composition; 2) they treat the image features extraction and textual feature extraction as two distinct tasks and ignore the inter-relationships between these two features. We address these challenges by proposing a Multimodal Self-and-Collaborative Attention Network (MSCAN). More specifically, the self-attention module calculates the response at a position by attending to all positions in the images, thus it can effectively encode spatial interaction of the visual elements. To model the complex image-textual feature relations, a co-attention module is used to jointly perform the textual-guided visual attention and visual-guided textual attention. Then the attended multimodal features are aggregated and sent into a two-layer MLP to obtain the aesthetic values. Extensive experiments over two large benchmarks demonstrate that the proposed MSCAN outperforms the state-of-the-arts by a large margin for unified aesthetic prediction tasks.
Around the world in 60 days: an exploratory study of impact of COVID-19 on online global news sentiment

The world is going through an unprecedented crisis due to COVID-19 breakout, and people all over the world are forced to stay indoors for safety. In such a situation, the rise and fall of the number of affected cases or deaths has turned into a constant headline in most news channels. Consequently, there is a lack of positivity in the world-wide news published in different forms of media. Texts based on news articles, movie reviews, tweets, etc. are often analyzed by researchers, and mined for determining opinion or sentiment, using supervised and unsupervised methods. The proposed work takes up the challenge of mining a comprehensive set of online news texts, for determining the prevailing sentiment in the context of the ongoing pandemic, along with a statistical analysis of the relation between actual effect of COVID-19 and online news sentiment. The amount and observed delay of impact of the ground truth situation on online news is determined on a global scale, as well as at country level. The authors conclude that at a global level, the news sentiment has a good amount of dependence on the number of new cases or deaths, while the effect varies for different countries, and is also dependent on regional socio-political factors.