Reviewerly/exHarmony huggingface dataset:
Disease-Image-Specific Learning for Diagnosis-Oriented Neuroimage Synthesis With Incomplete Multi-Modality Data

Incomplete data problem is commonly existing in classification tasks with multi-source data, particularly the disease diagnosis with multi-modality neuroimages, to track which, some methods have been proposed to utilize all available subjects by imputing missing neuroimages. However, these methods usually treat image synthesis and disease diagnosis as two standalone tasks, thus ignoring the specificity conveyed in different modalities, i.e., different modalities may highlight different disease-relevant regions in the brain. To this end, we propose a disease-image-specific deep learning (DSDL) framework for joint neuroimage synthesis and disease diagnosis using incomplete multi-modality neuroimages. Specifically, with each whole-brain scan as input, we first design a Disease-image-Specific Network (DSNet) with a spatial cosine module to implicitly model the disease-image specificity. We then develop a Feature-consistency Generative Adversarial Network (FGAN) to impute missing neuroimages, where feature maps (generated by DSNet) of a synthetic image and its respective real image are encouraged to be consistent while preserving the disease-image-specific information. Since our FGAN is correlated with DSNet, missing neuroimages can be synthesized in a diagnosis-oriented manner. Experimental results on three datasets suggest that our method can not only generate reasonable neuroimages, but also achieve state-of-the-art performance in both tasks of Alzheimer's disease identification and mild cognitive impairment conversion prediction.
Review of vision-based occupant information sensing systems for occupant-centric control

Vision-based (camera-based) systems, which can effectively sense occupant information, have garnered attention as a core technology in the Fourth Industrial Revolution. A detailed understanding of vision-based sensing systems is required to detect occupant information based on vision and use it for occupant-centric control. Therefore, in this study, we performed a comprehensive and structural literature review of vision-based occupant information systems. The contributions of this review can be summarized in the following six points: (1) a five-tier taxonomy of vision-based occupant information is proposed, (2) a systematic summary of vision-based occupant information is presented, (3) the quantitative and qualitative performance of sensing systems is reviewed, (4) an analysis of the applicability of deep-learning-based computer vision techniques is presented, (5) a summary of privacy-preserving techniques is included, and (6) a summary of vision-based control strategies and energy saving potential analysis is provided. The analysis in this review is an important contribution toward addressing the challenges in the field of research.
PML: Progressive Margin Loss for Long-tailed Age Classification

In this paper, we propose a progressive margin loss (PML) approach for unconstrained facial age classification. Conventional methods make strong assumption on that each class owns adequate instances to outline its data distribution, likely leading to bias prediction where the training samples are sparse across age classes. Instead, our PML aims to adaptively refine the age label pattern by enforcing a couple of margins, which fully takes in the in-between discrepancy of the intra-class variance, inter-class variance and class center. Our PML typically incorporates with the ordinal margin and the variational margin, simultaneously plugging in the globally-tuned deep neural network paradigm. More specifically, the ordinal margin learns to exploit the correlated relationship of the real-world age labels. Accordingly, the variational margin is leveraged to minimize the influence of head classes that misleads the prediction of tailed samples. Moreover, our optimization carefully seeks a series of indicator curricula to achieve robust and efficient model training. Extensive experimental results on three face aging datasets demonstrate that our PML achieves compelling performance compared to state of the art. Code will be made publicly.
Survey of similarity functions on neighborhood-based collaborative filtering

Today, recommender systems play a vital role in the acceleration of searches by internet users to find what they are interested in. Among the strategies proposed for recommender systems, collaborative filtering has received due attention regarding its simplicity and efficiency. The key factor for the success of this strategy returns to the similarity calculation methods that affect the accuracy of its recommendations. Regarding the large volume of articles published in the field of collaborative filtering for the development of recommender systems, it is necessary to provide a comprehensive review of the similarity functions and their efficiency presented in the field. Of course, several surveys have already been published to investigate the similarity functions proposed for collaborative filtering, but these articles either have looked briefly at these functions or reviewed a few numbers of them. In this study, the effort was to provide a comprehensive study on the similarity functions proposed for collaborative filtering with a special focus on the rating-based and neighbor-based approaches. After a brief explanation of each similarity function, some popular evaluation metrics were used for their evaluation using the MovieLens datasets. The comparative evaluation results presented in this article provide a highly useful reference for researchers in this field to choose their appropriate similarity function.
Cluster-driven Graph Federated Learning over Multiple Domains

Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). The central model has no direct access to the data, but only to the updates of the parameters computed locally by each client. This raises a problem, known as statistical heterogeneity, because the clients may have different data distributions (i.e. domains). This is only partly alleviated by clustering the clients. Clustering may reduce heterogeneity by identifying the domains, but it deprives each cluster model of the data and supervision of others.Here we propose a novel Cluster-driven Graph Federated Learning (FedCG). In FedCG, clustering serves to address statistical heterogeneity, while Graph Convolutional Networks (GCNs) enable sharing knowledge across them. FedCG: i. identifies the domains via an FL-compliant clustering and instantiates domain-specific modules (residual branches) for each domain; ii. connects the domain-specific modules through a GCN at training to learn the interactions among domains and share knowledge; and iii. learns to cluster unsupervised via teacher-student classifier-training iterations and to address novel unseen test domains via their domain soft-assignment scores. Thanks to the unique interplay of GCN over clusters, FedCG achieves the state-of-theart on multiple FL benchmarks.
Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation

Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated one which is usually caused during data acquisition and conversion. In this paper, we propose a novel spatial-spectral total variation (SSTV) regularized nonconvex local low-rank (LR) tensor approximation method to remove mixed noise in HSIs. From one aspect, the clean HSI data have its underlying local LR tensor property, even though the real HSI data is not globally low-rank due to the non-independent and non-identically distributed noise and out-liers. According to this fact, we propose a novel tensor Lγ-norm to formulate the local LR prior. From another aspect, HSIs are assumed to be piecewisely smooth in the global spatial and spectral domains. Instead of traditional bandwise total variation, we use the SSTV regularization to simultaneously consider global spatial and spectral smoothness. Results on simulated and real HSI datasets indicate that the use of local LR tensor penalty and global SSTV can boost the preserving of local details and overall structural information in HSIs.
SMD-Nets: Stereo Mixture Density Networks

Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging. In this paper, we propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures which ameliorates both issues. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities while explicitly modeling the aleatoric uncertainty inherent in the observations. Moreover, we formulate disparity estimation as a continuous problem in the image domain, allowing our model to query disparities at arbitrary spatial precision. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets. Our experiments demonstrate increased depth accuracy near object boundaries and prediction of ultra high-resolution disparity maps on standard GPUs. We demonstrate the flexibility of our technique by improving the performance of a variety of stereo backbones.
Convolutional Hough Matching Networks

Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM). The method distributes similarities of candidate matches over a geometric transformation space and evaluate them in a convolutional manner. We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. To validate the effect, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.
Explore Image Deblurring via Encoded Blur Kernel Space

This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Unlike recent deep-learning-based methods, our system can handle unseen blur kernel, while avoiding using complicated handcrafted priors on the blur operator often found in classical methods. Due to the method’s design, the encoded kernel space is fully differentiable, thus can be easily adopted in deep neural network models. Moreover, our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain. Finally, we provide experimental results to confirm the effectiveness of the proposed method. The code is available at https://github.com/VinAIResearch/blur-kernelspace-exploring.
A Deep Learning Based Light-Weight Face Mask Detector With Residual Context Attention and Gaussian Heatmap to Fight Against COVID-19

Coronavirus disease 2019 has seriously affected the world.One major protective measure for individuals is to wear masks in public areas.Several regions applied a compulsory mask-wearing rule in public areas to prevent transmission of the virus.Few research studies have examined automatic face mask detection based on image analysis.In this paper, we propose a deep learning based single-shot light-weight face mask detector to meet the low computational requirements for embedded systems, as well as achieve high performance.To cope with the low feature extraction capability caused by the light-weight model, we propose two novel methods to enhance the model's feature extraction process.First, to extract rich context information and focus on crucial face mask related regions, we propose a novel residual context attention module.Second, to learn more discriminating features for faces with and without masks, we introduce a novel auxiliary task using synthesized Gaussian heat map regression.Ablation studies show that these methods can considerably boost the feature extraction ability and thus increase the final detection performance.Comparison with other models shows that the proposed model achieves state-of-the-art results on two public datasets, the AIZOO and Moxa3K face mask datasets.In particular, compared with another light-weight you only look once version 3 tiny model, the mean average precision of our model is 1.7% higher on the AIZOO dataset, and 10.47% higher on the Moxa3K dataset.Therefore, the proposed model has a high potential to contribute to public health care and fight against the coronavirus disease 2019 pandemic.
Nursing students’ views of using virtual reality in healthcare: A qualitative study

This study explored nursing students' views of using virtual reality in healthcare.The popularity and use of virtual reality in healthcare delivery and education is on the rise. Yet, the views of future nurses regarding this technology remain underexplored.This is a qualitative descriptive study guided by a naturalistic inquiry and reported using the Standards for Reporting Qualitative Research checklist.Nursing students (n = 26) were recruited using convenience and snowball sampling. They were first exposed to a virtual reality intervention aimed to enhance men's awareness of testicular diseases. This was attempted to familiarise participants with the technology and initiate conversations around its use in healthcare. Participants were then interviewed face-to-face, either individually or within focus groups. Data were analysed using thematic analysis.Four themes were identified: (i) positive experiences of virtual reality; (ii) challenges to using virtual reality; (iii) settings where virtual reality can be implemented; and (iv) blue-sky and future applications of virtual reality. Participants described this technology as novel, enjoyable, immersive, memorable and inclusive. They questioned, however, the suitability of virtual reality for older adults, reported minor technical difficulties and stressed the importance of prior preparation in the use of the technology. Virtual reality was recommended for use in outpatient healthcare settings, schools and the community. Participants suggested using virtual reality in health promotion, disease prevention and management, and to promote nurses' empathy towards patients.Findings highlight the potential role of virtual reality in assisting nurses in promoting health and managing disease. Future research is needed to establish the long-term effect of virtual reality interventions among more diverse participants.Virtual reality can be implemented in outpatient, educational and community settings in order to promote health, prevent disease, enhance disease and self-management, and increase nurses' empathy towards patients.
Oblivious Key-Value Stores and Amplification for Private Set Intersection

Many recent private set intersection (PSI) protocols encode input sets as polynomials. We consider the more general notion of an oblivious key-value store (OKVS), which is a data structure that compactly represents a desired mapping $$k_i \mapsto v_i$$ . When the $$v_i$$ values are random, the OKVS data structure hides the $$k_i$$ values that were used to generate it. The simplest (and size-optimal) OKVS is a polynomial p that is chosen using interpolation such that $$p(k_i)=v_i$$ . We initiate the formal study of oblivious key-value stores, and show new constructions resulting in the fastest OKVS to date. Similarly to cuckoo hashing, current analysis techniques are insufficient for finding concrete parameters to guarantee a small failure probability for our OKVS constructions. Moreover, it would cost too much to run experiments to validate a small upperbound on the failure probability. We therefore show novel techniques to amplify an OKVS construction which has a failure probability p, to an OKVS with a similar overhead and failure probability $$p^c$$ . Setting p to be moderately small enables to validate it by running a relatively small number of O(1/p) experiments. This validates a $$p^c$$ failure probability for the amplified OKVS. Finally, we describe how OKVS can significantly improve the state of the art of essentially all variants of PSI. This leads to the fastest two-party PSI protocols to date, for both the semi-honest and the malicious settings. Specifically, in networks with moderate bandwidth (e.g., 30–300 Mbps) our malicious two-party PSI protocol has 40% less communication and is 20–40% faster than the previous state of the art protocol, even though the latter only has heuristic confidence.
Blockchain for business management: Applications, challenges and potentials

Blockchain technology has emerged as one of the most vital and encouraging technology in Industry 4.0. It is said to have the potential to majorly transform the way the business world and the economic system functions; it offers numerous possibilities to flourish existing businesses and also to grow entirely new ones along with severe disruptions to the traditional businesses. The present study is an attempt to understand the applications of blockchain technology in managing business. Through systematic literature review, it intends to study the applications in different key functions of business. Furthermore, it highlights the key challenges in blockchain implementation and the potentials of blockchain in managing business. The study will be useful to the practitioners as well as to the scholars around the globe. Blockchain adoption can help in securing business transactions, reduction in errors, improving organizational functions and procedures, and prevention of fraud. The smart contract as a feature of blockchain technology can be applied to all transactions involving movement of resources namely finance, material and people. This will lead to effective tracking, visibility, security enhancement and cost reduction in the overall business process.
Improving Unsupervised Image Clustering With Robust Learning

Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC’s novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model’s flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise.
Privacy-Preserving Mechanism in Smart Home Using Blockchain

The IoT, or Internet of Things has been a major talking point amongst technology enthusiasts in recent years.The internet of thing (IoT) has been emerged and evolved rapidly, making the world's fabric around us smarter and more responsive.The smart home uses one such transformation of IoT, which seems to be the wave of the future.However, with the increasing wide adoption of IoT, data security, and privacy concerns about how our data is collected and shared with others, has also risen.To solve these challenges, an approach to data privacy and security in a smart home using blockchain technology is proposed in this paper.We propose authentication scheme that combines attribute-based access control with smart contracts and edge computing to create a secure framework for IoT devices in smart home systems.The edge server adds scalability to the system by offloading heavy processing activities and using a differential privacy method to aggregate data to the cloud securely and privately.We present several aspects of testing and implementing smart contracts, the differential private stochastic gradient descent algorithm, and system architecture and design.We demonstrate the efficacy of our proposed system by fully examining its security and privacy goals in terms of confidentiality, integrity, and availability.Our framework achieves desired security and privacy goals and is resilient against modification, DoS attacks, data mining and linkage attacks.Finally, we undertake a performance evaluation to demonstrate the proposed scheme's feasibility and efficiency.
SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping

We present SMURF, a method for unsupervised learning of optical flow that improves state of the art on all benchmarks by 36% to 40% (over the prior best method UFlow) and even outperforms several supervised approaches such as PWC-Net and FlowNet2. Our method integrates architecture improvements from supervised optical flow, i.e. the RAFT model, with new ideas for unsupervised learning that include a sequence-aware self-supervision loss, a technique for handling out-of-frame motion, and an approach for learning effectively from multi-frame video data while still only requiring two frames for inference.
Two-Branch Deep Neural Network for Underwater Image Enhancement in HSV Color Space

Due to the influence of light absorption and scattering, underwater images usually suffer from quality deteriorations such as color cast and reduced contrast. The diverse quality degradations not only dissatisfy the user expectation but also lead to a significant performance drop in many underwater vision applications. This letter proposes a novel two-branch deep neural network for underwater image enhancement (UIE), which is capable of separately removing color cast and enhancing image contrast by fully leveraging useful properties of the HSV color space in disentangling chrominance and intensity. Specifically, the input underwater image is first converted into the HSV color space and disentangled into HS and V channels to serve as the input of the two branches, respectively. Then, the color cast removal branch enhances the H and S channels with a generative adversarial network architecture while the contrast enhancement branch enhances the V channel via a traditional convolutional neural network. The enhanced channels by the two branches are merged and converted back into RGB color space to obtain the final enhanced result. Experimental results demonstrate that, compared with state-of-the-art UIE methods, our method can produce much more visually pleasing enhanced results.