Reviewerly/exHarmony huggingface dataset:
ICT Development, Innovation Diffusion and Sustainable Growth in Sub-Saharan Africa

This study aims to explore the impacts of ICT and innovation on sustainable economic growth and the direction of causal relationships among them in a trivariate framework. The study employed the DOLS and Panel VECM for causality to study the relationships for 33 Sub-Saharan Africa countries categorized based on income between 2000 and 2020. The study uses annual time-series data that were obtained from the World Development Indicators (WDI) database for the empirical analysis. Results from the DOLS show that both ICT development and innovation contribute positively to sustainable growth in all the categories of countries. However, the marginal effects of innovation on sustainable growth are very small compared to ICT development, especially for low-income countries. The VECM result confirms significant causal relationships among the studied variables in the short and long run. Policies should be geared toward channeling resources to enhance ICT skills, access, and usage in the continent. This can be achieved if organizations engaged in the SSA agenda for prosperity, provide the support needed to complement different governments’ efforts in advancing ICT penetration and innovation diffusion in the continent. Also, it is important for income groups to be considered when establishing and implementing such policies.
Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex data

Abstract In this paper we introduce a new class of software tools engaged in delivering successful explanations of complex processes on top of basic Explainable AI (XAI) software systems. These tools, that we call cumulatively Explanatory AI (YAI) systems, enhance the quality of the basic output of a XAI by adopting a user-centred approach to explanation that can cater to the individual needs of the explainees with measurable improvements in usability. Our approach is based on Achinstein’s theory of explanations, where explaining is an illocutionary (i.e., broad yet pertinent and deliberate) act of pragmatically answering a question. Accordingly, user-centrality enters in the equation by considering that the overall amount of information generated by answering all questions can rapidly become overwhelming and that individual users may perceive the need to explore just a few of them. In this paper, we give the theoretical foundations of YAI, formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space , generated by it. To this end, we frame the explanatory space as an hypergraph of knowledge and we identify a set of heuristics and properties that can help approximating a decomposition of it into a tree-like representation for efficient and user-centred explanation retrieval. Finally, we provide some old and new empirical results to support our theory, showing that explanations are more than textual or visual presentations of the sole information provided by a XAI.
A Tree-Based Structure-Aware Transformer Decoder for Image-To-Markup Generation

Image-to-markup generation aims at translating an image into markup (structured language) that represents both the contents and the structural semantics corresponding to the image. Recent encoder-decoder based approaches typically employ string decoders to model the string representation of the target markup, which cannot effectively capture the rich embedded structural information. In this paper, we propose TSDNet, a novel Tree-based Structure-aware Transformer Decoder NETwork to directly generate the tree representation of the target markup in a structure-aware manner. Specifically, our model learns to sequentially predict the node attributes, edge attributes, and node connectivities by multi-task learning. Meanwhile, we introduce a novel tree-structured attention to our decoder such that it can directly operate on the partial tree generated in each step to fully exploit the structural information. TSDNet doesn't rely on any prior assumptions on the target tree structure, and can be jointly optimized with encoders in an end-to-end fashion. We evaluate the performance of our model on public image-to-markup generation datasets, and demonstrate its ability to learn the complicated correlation from the structural information in the target markup with significant improvement over state-of-the-art methods by up to 5.6% in mathematical expression recognition and up to 35.34% in chemical formula recognition.
Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering

Incomplete multi-view clustering is a challenging task which aims to partition the unlabeled incomplete multi-view data into several clusters. The existing incomplete multi-view clustering methods neglect to utilize the diversified correlations inherent in data and handle the noise contained in different views. To address these issues, we propose a Robust Diversified Graph Contrastive Network (RDGC) for incomplete multi-view clustering, which integrates multi-view representation learning and diversified graph contrastive regularization into a unified framework. Multi-view unified and specific encoding network is developed to fuse different views into a unified representation, which can flexibly estimate the importance of views for incomplete multi-view data. Robust diversified graph contrastive regularization is proposed which captures the diversified data correlations to improve the discriminating power of the learned representation and reduce the information loss caused by the view missing problem. Moreover, our method can effectively resist the influence of noise and unreliable views by leveraging the robust contrastive learning loss. Extensive experiments conducted on four multi-view clustering datasets demonstrate the superiority of our method over the state-of-the-art methods.
Structure-Inferred Bi-level Model for Underwater Image Enhancement

Very recently, with the development of underwater robots, underwater image enhancement arising growing interests in the computer vision community. However, owing to light being scattered and absorbed while it traveling in water, underwater captured images often suffer from color cast and low visibility. Existing methods depend on specific prior knowledge and training data to enhance underwater images in the absence of structure information, which results in poor and unnatural performance. To this end, we propose a Structural-Inferred Bi-level Model (SIBM) that incorporates different modalities of knowledge (i.e., semantic domain, gradient-domain, and pixel domain) hierarchically enhancing underwater images. In particular, by introducing a semantic mask, we individually optimize the forehand branch that avoids unnecessary interference arising from the background region. We design a gradient-based high-frequency branch to exploit gradient-space guidance for preserving texture structures. Moreover, we construct a pixel-based branch by feeding semantic and gradient information to enhance underwater images. To exploit different modalities, we introduce a hyper-parameter optimization scheme to fuse the above domain information. Experimental results illustrate that the developed method not only outperforms the previous methods in quantitative scores but also generalizes well on real-world underwater datasets. Source code is available at \hrefhttps://github.com/IntegralCoCo/SIBM https://github.com/IntegralCoCo/SIBM.
Real-time Semantic Segmentation with Parallel Multiple Views Feature Augmentation

Real-time semantic segmentation is essential for many practical applications, which utilizes attention-based feature aggregation into lightweight structures to improve accuracy and efficiency. However, existing attention-based methods ignore 1) high-level and low-level feature augmentation guided by spatial information, and 2) low-level feature augmentation guided by semantic context, so that feature gaps between multi-level features and noise of low-level spatial details still exist. To address these problems, a new real-time semantic segmentation network, called MvFSeg, is proposed. In MvFSeg, parallel convolution with multiple depths is designed as a context head to generate and integrate multi-view features with larger receptive fields. Moreover, MvFSeg designs multiple views feature augmentation strategies that exploit spatial and semantic guidance for shallow and deep feature augmentation in an inter-layer and intra-layer manner. These strategies eliminate feature gaps between multi-level features, filter out the noise of spatial details, and provide spatial and semantic guidance for multi-level features. By combining multi-view features and augmented features from the lightweight networks with progressive dense aggregation structures, MvFSeg effectively captures invariance at various scales and generates high-quality segmentation results. Experiments conducted on Cityscapes and CamVid benchmark show that MvFSeg outperforms existing state-of-the-art methods.
Face Forgery Detection via Symmetric Transformer

The deep learning-based face forgery detection is a novel yet challenging task. Despite impressive results have been achieved, there are still some limitations in the existing methods. For example, the previous methods are hard to maintain consistent predictions for consecutive frames, even if all of those frames are actually forged. We propose a symmetric transformer for channel and spatial feature extraction, which is because the channel and spatial features of a robust forgery detector should be consistent in the temporal domain. The symmetric transformer adopt the newly-designed attention-based strategies for channel variance and spatial gradients as the vital features, which greatly improves the robustness of deepfake video detection. Moreover, this symmetric structure acts on temporal and spatial features respectively, which ensures the robustness of detection from two different aspects. Our symmetric transformer is an end-to-end optimized network. Experiments are conducted on various settings, the proposed methods achieve significantly improvement on prediction robustness and perform better than state-of-the-art methods on different datasets.
Physician selection based on user-generated content considering interactive criteria and risk preferences of patients

Online medical platform is a platform for patients to post their medical experience, collect medical information, and link doctors and patients for related medical activities. As the number of patients and doctors registered on the platform increases, there is an urgent need to consider how patients can identify useful information from the vast amount of information to help them make medical choices, and how the platform can provide distinctive medical choices based on the risk preferences of patients. In this paper, we propose a decision-making model that integrates a machine-learning method and a multi-criteria decision-making method to help patients to select physicians based on user-generated content considering interactive criteria and risk preferences of patients. Firstly, by data mining methods, various criteria included in user-generated content that influence patients' selection behavior are retrieved to construct a physician evaluation system. Secondly, a sentiment analysis method based on a medical review corpus is developed to mine satisfaction information from text reviews. Finally, a multi-criteria decision-making method is proposed considering patients' risk-averse preferences for medical services and the interactions among criteria. The validity of the proposed model is demonstrated by a case study of ranking psychologists from haodf.com. The comparisons with other methods and sensitivity analysis results provide suggestions to patients to choose psychologists and the website to rank psychologists.
RadioBERT: A deep learning-based system for medical report generation from chest X-ray images using contextual embeddings

Increasing number of chest X-ray (CXR) examinations in radiodiagnosis departments burdens radiologists' and makes the timely generation of accurate radiological reports highly challenging. An automatic radiological report generation (ARRG) system is envisaged to generate radiographic reports with minimal human intervention, ease radiologists' burden, and smoothen the clinical workflow. The success of an ARRG system depends on two critical factors: i) quality of the features extracted by the ARRG system from the CXR images, and ii) quality of the linguistic expression generated by the ARRG system describing the normalities and abnormalities as indicated by the extracted features. Most of the existing ARRG systems miserably fail due to the latter factor and do not generate clinically acceptable reports because they ignore the contextual importance of the medical terms.The advent of contextual word embeddings, like ELMo and BERT, has revolutionized several natural language processing (NLP) tasks. A contextual embedding represents a word based on its context. The main objective of this work is to develop an ARRG system that uses contextual word embeddings to generate clinically accurate reports from CXR images.We present an end-to-end deep neural network that uses contextual word representations for generating clinically useful radiological reports from CXR images. The proposed network, termed as RadioBERT, uses DistilBERT for contextual word representation and leverages transfer learning. Additionally, due to the importance of abnormal observations over the normal ones, the network reorders the generated sentences by applying sentiment analysis to keep abnormal descriptions on the top of the generated report.The empirical study consisting of several experiments performed on the OpenI dataset indicates that CNN+Hierarchical LSTM with DistilBERT embedding improves the benchmark performance. We have been able to achieve the following performance scores: BLEU-1 = 0.772, BLEU-2 = 0.770, BLEU-3 = 0.768, BLEU-4 = 0.767, CIDEr = 0.5563, and ROUGE = 0.897.The proposed method improves the state-of-the-art performance scores by a substantial margin. It is concluded that the use of word embeddings generated by DistilBERT enhances the performance of hierarchical LSTM for producing clinical reports by significant margin.
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization

Considerable progress has been made in domain generalization (DG) which aims to learn a generalizable model from multiple well-annotated source domains to unknown target domains. However, it can be prohibitively expensive to obtain sufficient annotation for source datasets in many real scenarios. To escape from the dilemma between domain generalization and annotation costs, in this paper, we introduce a novel task named label-efficient domain generalization (LEDG) to enable model generalization with label-limited source domains. To address this challenging task, we propose a novel framework called Collaborative Exploration and Generalization (CEG) which jointly optimizes active exploration and semi-supervised generalization. Specifically, in active exploration, to explore class and domain discriminability while avoiding information divergence and redundancy, we query the labels of the samples with the highest overall ranking of class uncertainty, domain representativeness, and information diversity. In semi-supervised generalization, we design MixUp-based intra- and inter-domain knowledge augmentation to expand domain knowledge and generalize domain invariance. We unify active exploration and semi-supervised generalization in a collaborative way and promote mutual enhancement between them, boosting model generalization with limited annotation. Extensive experiments show that CEG yields superior generalization performance. In particular, CEG can even use only 5% data annotation budget to achieve competitive results compared to the previous DG methods with fully labeled data on PACS dataset.
Single Image Shadow Detection via Complementary Mechanism

In this paper, we present a novel shadow detection framework by investigating the mutual complementary mechanisms contained in this specific task. Our method is based on a key observation: in a single shadow image, shadow regions and non-shadow counterparts are complementary to each other in nature, thus a better estimation on one side leads to an improved estimation on the other, and vice versa. Motivated by this observation, we first leverage two parallel interactive branches to jointly produce shadow and non-shadow masks. The interaction between two parallel branches is to retain the deactivated intermediate features of one branch by introducing the negative activation technique, which could serve as complementary features to the other branch. Besides, we also apply identity reconstruction loss as complementary training guidance at the image level. Finally, we design two discriminative losses to satisfy the complementary requirements of shadow detection, i.e., neither missing any shadow regions nor falsely detecting non-shadow regions. By fully exploring and exploiting the complementary mechanism of shadow detection, our method can confidently predict more accurate shadow detection results. Extensive experiments on the three widely-used benchmarks demonstrate our proposed method achieves superior shadow detection performance against state-of-the-art methods with a relatively low computational cost.
CRNet: Unsupervised Color Retention Network for Blind Motion Deblurring

Blind image deblurring is still a challenging problem due to the inherent ill-posed properties. To improve the deblurring performance, many supervised methods have been proposed. However, obtaining labeled samples from a specific distribution (or a domain) is usually expensive, and the data-driven training-based model also cannot be generalized to the blurry images in all domains. These challenges have given birth to certain unsupervised deblurring methods. However, there is a great chromatic aberration between the latent and original images, directly degrading the performance. In this paper, we therefore propose a novel unsupervised color retention network termed CRNet to perform blind motion deblurring. In addition, new concepts of blur offset estimation and adaptive blur correction are proposed to retain the color information when deblurring. As a result, unlike the previous studies, CRNet does not learn a mapping directly from the blurry image to the restored latent image, but from the blurry image to a motion offset. An adaptive blur correction operation is then performed on the blurry image to restore the latent image, thereby retaining the color information of the original image to the greatest extent. To further effectively retain the color information and extract the blur information, we also propose a new module called pyramid global blur feature perception (PGBFP). To quantitatively prove the effectiveness of our network in color retention, we propose a novel chromatic aberration quantization metrics in line with the human perception. Extensive quantitative and visualization experiments show that CRNet can obtain the state-of-the-art performance in unsupervised deblurring tasks.
Hierarchical Graph Embedded Pose Regularity Learning via Spatio-Temporal Transformer for Abnormal Behavior Detection

Abnormal behavior detection in surveillance video is a fundamental task in modern public security. Different from typical pixel-based solutions, pose-based approaches leverage low-dimensional and strongly-structured skeleton feature, which enables the anomaly detector to be immune to complex background noise and obtain higher efficiency. However, existing pose-based methods only utilize the pose of each individual independently while ignore the important interactions between individuals. In this paper, we present a hierarchical graph embedded pose regularity learning framework via spatio-temporal transformer, which leverages the strength of graph representation in encoding strongly-structured skeleton feature. Specifically, skeleton feature is encoded as the hierarchical graph representation, which jointly models the interactions among multiple individuals and the correlations among body joints within the same individual. Furthermore, a novel task-specific spatial-temporal graph transformer is designed to encode the hierarchical spatio-temporal graph embeddings of human skeletons and learn the regular patterns within normal training videos. Experimental results indicate that our method obtains superior performance over state-of-the-art methods on several challenging datasets.
Relation-enhanced Negative Sampling for Multimodal Knowledge Graph Completion

Knowledge Graph Completion (KGC), aiming to infer the missing part of Knowledge Graphs (KGs), has long been treated as a crucial task to support downstream applications of KGs, especially for the multimodal KGs (MKGs) which suffer the incomplete relations due to the insufficient accumulation of multimodal corpus. Though a few research attentions have been paid to the completion task of MKGs, there is still a lack of specially designed negative sampling strategies tailored to MKGs. Meanwhile, though effective negative sampling strategies have been widely regarded as a crucial solution for KGC to alleviate the vanishing gradient problem, we realize that, there is a unique challenge for negative sampling in MKGs about how to model the effect of KG relations during learning the complementary semantics among multiple modalities as an extra context. In this case, traditional negative sampling techniques which only consider the structural knowledge may fail to deal with the multimodal KGC task. To that end, in this paper, we propose a MultiModal Relation-enhanced Negative Sampling (MMRNS) framework for multimodal KGC task. Especially, we design a novel knowledge-guided cross-modal attention (KCA) mechanism, which provides bi-directional attention for visual & textual features via integrating relation embedding. Then, an effective contrastive semantic sampler is devised after consolidating the KCA mechanism with contrastive learning. In this way, a more similar representation of semantic features between positive samples, as well as a more diverse representation between negative samples under different relations could be learned. Afterwards, a masked gumbel-softmax optimization mechanism is utilized for solving the non-differentiability of sampling process, which provides effective parameter optimization compared with traditional sample strategies. Extensive experiments on three multimodal KGs demonstrate that our MMRNS framework could significantly outperform the state-of-the-art baseline methods, which validates the effectiveness of relation guides in multimodal KGC task.
Learning Smooth Representation for Multi-view Subspace Clustering

Multi-view subspace clustering aims to exploit data correlation consensus among multiple views, which essentially can be treated as graph-based approach. However, existing methods usually suffer from suboptimal solution as the raw data might not be separable into subspaces. In this paper, we propose to achieve a smooth representation for each view and thus facilitate the downstream clustering task. It is based on a assumption that a graph signal is smooth if nearby nodes on the graph have similar features representations. Specifically, our mode is able to retain the graph geometric features by applying a low-pass filter to extract the smooth representations of multiple views. Besides, our method achieves the smooth representation learning as well as multi-view clustering interactively in a unified framework, hence it is an end-to-end single-stage learning problem. Substantial experiments on benchmark multi-view datasets are performed to validate the effectiveness of the proposed method, compared to the state-of-the-arts over the clustering performance.