Reviewerly/exHarmony huggingface dataset:
Sensor-based Human Activity Recognition Using Graph LSTM and Multi-task Classification Model

This paper explores human activities recognition from sensor-based multi-dimensional streams. Recently, deep learning-based methods such as LSTM and CNN have achieved important progress in practical application scenarios. However, in most previous deep learning-based methods exist potential challenges such as class imbalance and multi-modal heterogeneity with time and sensor signals. To handle those problems, we propose a graph LSTM and Metric Learning model (GLML) with multiple construction graph fusion by modeling the sensor-aspect signals and the graph-aspect activities. GLML is a semi-supervised co-training architecture, which can be seen as several iteratively pseudo-labels sampling processing in the unlabeled data. Specifically, we construct three graphs to capture the different relations in each timestamp. Meanwhile, the graph attention model and attention mechanism are proposed to integrate multiple graph interactions for different sensor signals. Furthermore, to obtain a fixed representation of hidden state units and their neighboring nodes, we introduce the Graph LSTM to learn the graph-aspect relations from graph-structured constructed graphs. Notably, we propose a multi-task classification model combining loss function for classification distribution with deep metric learning to enhance the representation ability of the multi-modal sensor data. Experimental results on three public datasets demonstrate that our proposed GLML model has at least 2.44% improved in average against the state-of-the-art methods.
Semi-supervised Medical Image Segmentation Using Cross-Model Pseudo-Supervision with Shape Awareness and Local Context Constraints

In semi-supervised medical image segmentation, the limited amount of labeled data available for training is often insufficient to learn the variability and complexity of target regions. To overcome these challenges, we propose a novel framework based on cross-model pseudo-supervision that generates anatomically plausible predictions using shape awareness and local context constraints. Our framework consists of two parallel networks, a shape-aware network and a shape-agnostic network, which provide pseudo-labels to each other for using unlabeled data effectively. The shape-aware network implicitly captures information on the shape of target regions by adding the prediction of the other network as input. On the other hand, the shape-agnostic network leverages Monte-Carlo dropout uncertainty estimation to generate reliable pseudo-labels to the other network. The proposed framework also comprises a new loss function that enables the network to learn the local context of the segmentation, thus improving the overall segmentation accuracy. Experiments on two publicly-available datasets show that our method outperforms state-of-the-art approaches for semi-supervised segmentation and better preserves anatomical morphology compared to these approaches. Code is available at https://github.com/igip-liu/SLC-Net .
Warm Start Active Learning with Proxy Labels and Selection via Semi-supervised Fine-Tuning

Which volume to annotate next is a challenging problem in building medical imaging datasets for deep learning. One of the promising methods to approach this question is active learning (AL). However, AL has been a hard nut to crack in terms of which AL algorithm and acquisition functions are most useful for which datasets. Also, the problem is exacerbated with which volumes to label first when there is zero labeled data to start with. This is known as the cold start problem in AL. We propose two novel strategies for AL specifically for 3D image segmentation. First, we tackle the cold start problem by proposing a proxy task and then utilizing uncertainty generated from the proxy task to rank the unlabeled data to be annotated. Second, we craft a two-stage learning framework for each active iteration where the unlabeled data is also used in the second stage as a semi-supervised fine-tuning strategy. We show the promise of our approach on two well-known large public datasets from medical segmentation decathlon. The results indicate that the initial selection of data and semi-supervised framework both showed significant improvement for several AL strategies.
Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI

Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate this shortcoming and identify the under-performing classes, we propose maintaining a confidence array that records class-wise performance during training. A fuzzy fusion of these confidence scores is proposed to adaptively prioritize individual confidence metrics in every sample rather than traditional ensemble approaches, where a set of predefined fixed weights are assigned for all the test cases. Further, we introduce a robust class-wise sampling method and dynamic stabilization for a better training strategy. Our proposed method considers all the under-performing classes with dynamic weighting and tries to remove most of the noises during training. Upon evaluation on two cardiac MRI datasets, ACDC and MMWHS, our proposed method shows effectiveness and generalizability and outperforms several state-of-the-art methods found in the literature.
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging

Collaboration among multiple data-owning entities (e.g., hospitals) can accelerate the training process and yield better machine learning models due to the availability and diversity of data. However, privacy concerns make it challenging to exchange data while preserving confidentiality. Federated Learning (FL) is a promising solution that enables collaborative training through exchange of model parameters instead of raw data. However, most existing FL solutions work under the assumption that participating clients are honest and thus can fail against poisoning attacks from malicious parties, whose goal is to deteriorate the global model performance. In this work, we propose a robust aggregation rule called Distance-based Outlier Suppression (DOS) that is resilient to byzantine failures. The proposed method computes the distance between local parameter updates of different clients and obtains an outlier score for each client using Copula-based Outlier Detection (COPOD). The resulting outlier scores are converted into normalized weights using a softmax function, and a weighted average of the local parameters is used for updating the global model. DOS aggregation can effectively suppress parameter updates from malicious clients without the need for any hyperparameter selection, even when the data distributions are heterogeneous. Evaluation on two medical imaging datasets (CheXpert and HAM10000) demonstrates the higher robustness of DOS method against a variety of poisoning attacks in comparison to other state-of-the-art methods. The code can be found at https://github.com/Naiftt/SPAFD .
Consistency-Preserving Visual Question Answering in Medical Imaging

Visual Question Answering (VQA) models take an image and a natural-language question as input and infer the answer to the question. Recently, VQA systems in medical imaging have gained popularity thanks to potential advantages such as patient engagement and second opinions for clinicians. While most research efforts have been focused on improving architectures and overcoming data-related limitations, answer consistency has been overlooked even though it plays a critical role in establishing trustworthy models. In this work, we propose a novel loss function and corresponding training procedure that allows the inclusion of relations between questions into the training process. Specifically, we consider the case where implications between perception and reasoning questions are known a-priori. To show the benefits of our approach, we evaluate it on the clinically relevant task of Diabetic Macular Edema (DME) staging from fundus imaging. Our experiments show that our method outperforms state-of-the-art baselines, not only by improving model consistency, but also in terms of overall model accuracy. Our code and data are available at https://github.com/sergiotasconmorales/consistency_vqa .
Robust optical displacement measurement of bridge structures in complex environments

A robust optical displacement measurement method is proposed herein for long-span bridges by considering complex environmental interference, such as illumination changes, system disturbances, and remote calibrations. First, to reduce the centroid shifts caused by lighting changes from the ambient or target itself, a center detection algorithm with an area-consistency constraint was applied. Optimized algorithm can reduce the noise variance by about half in the static laboratory experiment, which shows better illumination robustness than the adaptive threshold-based method. Second, to overcome the lower accuracy of 6-D camera pose tracking in remote measurements and limited correction effects of the widely used relative displacement method, a novel correction method with better implementability as well as accuracy was proposed for typical environmental vibrations. Third, an integrated real-time standing optical displacement sensor was developed for outdoor tests. In particular, a system combining a kilometer-level rangefinder and high-power laser designator was designed to achieve fast and fine object distance calibrations. Finally, the proposed method was applied to a short-span concrete bridge and a long-span steel bridge. The extracted displacement time histories at multiple points and deflection shapes at certain moments are expected to provide rich data for further performance evaluations.
Blockchain-Based Credential Management for Anonymous Authentication in SAGVN

In this paper, we propose a blockchain-based collaborative credential management scheme for anonymous authentication in space-air-ground integrated vehicular networks (SAGVN), named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAG-BC</i> . First, we build a consortium blockchain among service providers and design a distributed system setup (DSS) scheme to securely generate public parameters for issuing credentials. Second, we design a collaborative credential issuance (CCI) scheme to generate a succinct and easy-to-manage subscription credential. The credential can be used by users to access different access points in SAGVN efficiently without revealing true identities from the authentication messages. With co-designs of zero-knowledge proofs and succinct on-chain commitments, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAG-BC</i> provides efficient verifiability and incentives for credential management operations in SAGVN. By doing so, expensive on-chain storage and computational overheads are reduced in the DSS and CCI. Finally, we conduct a thorough security analysis to demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAG-BC</i> achieves security and verifiability for credential management in SAGVN. We set up a real-world blockchain network and conduct extensive experiments to show the feasibility and efficiency of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAG-BC</i> .
Deep learning-based super-resolution and de-noising for XMM-newton images

The field of artificial intelligence based image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. In this work we present the first application of Machine Learning based super-resolution (SR) and de-noising (DN) to enhance X-ray images from the European Space Agency's XMM-Newton telescope. Using XMM-Newton images in band [0.5, 2] keV from the European Photon Imaging Camera pn detector (EPIC-pn), we develop XMM-SuperRes and XMM-DeNoise deep learning-based models that can generate enhanced SR and DN images from real observations. The models are trained on realistic XMM-Newton simulations such that XMM-SuperRes will output images with two times smaller point-spread function and with improved noise characteristics. The XMM-DeNoise model is trained to produce images with 2.5x the input exposure time from 20 to 50 ks. When tested on real images, DN improves the image quality by 8.2%, as quantified by the global peak-signal-to-noise ratio. These enhanced images allow identification of features that are otherwise hard or impossible to perceive in the original or in filtered/smoothed images with traditional methods. We demonstrate the feasibility of using our deep learning models to enhance XMM-Newton X-ray images to increase their scientific value in a way that could benefit the legacy of the XMM-Newton archive.
Efficient Cloud Orchestration Services in Computing

In the buzz of technology, the IT Industry growing at a rapid speed with Cloud Orchestration. In the developing trends, cloud orchestration is becoming impressive and uncertain of dealing a large amount of information. The Cloud orchestration defines the proper arrangement and the coordination of various automated assignments for the proper flow of a process. These assignments fulfill the goal of Cloud Orchestration system. Cloud orchestration formulates the parameters like data storage capacity and capability, computing applications. The major areas where cloud orchestration is performing its goals are service workload, resource management, service level agreements, security, etc. The research is now trending with fuzzy and rough sets in cloud orchestration. In the innovation of cloud computing the important components are datacenters, cloud services, cloud parameters and users. There is a need of methodology by which the orchestration is being done. At last, the parameters used in cloud orchestration are being simulated using cloud simulators.
XRRF: An eXplainable Reasonably Randomised Forest algorithm for classification and regression problems

Tree-based ensemble algorithms (TEAs) have had a transformative impact in various fields. However, when they are applied to real-time critical problems such as medical analysis, existing TEAs fall short of two-fold issues. The first is the trade-off among model interpretability, accuracy, and explainability. Second, traditional TEAs perform dramatically worse when the number of contributing features to model accuracy is small in comparison to the total number of features. We address the aforementioned issues in this paper and propose a novel "eXplainable Reasonably Randomised Forest" (XRRF) algorithm. The XRRF consists of four conclusive steps: learning performance, intrinsic interpretability, model accuracy, and explainability. The proposed forest algorithm is evaluated on three real-world problems (medical analysis, business analysis, and employee churn), a hybrid artificial dataset, and twenty multidisciplinary benchmark problems with varying characteristics. The experimental results demonstrate that the XRRF outperforms the six mainstream and two cutting-edge black-box and white-box algorithms, respectively.
Learning compact yet accurate Generative Adversarial Networks for recommender systems

Recently, Generative Adversarial Networks (GANs) have received much attention in recommender systems because they can capture complex data distributions. They rely on two sub-networks, generator and discriminator models, to generate ‘fake’ yet reliable data, so better recommendation accuracy can be obtained. However, most variants of existing GANs achieve accuracy improvements by greatly increasing the model complexity, especially the amount of parameters, leading to difficulty in the deployment of those methods. Therefore, we aim to resolve this issue by learning compact yet accurate GANs that can make up valuable data with less generator parameters. To this end, we integrate knowledge distillation (KD, in the form of a teacher–student architecture) into GANs to reduce the model complexity while improving the accuracy of GANs. To the best of our knowledge, this work is the first to learn compact GANs for recommender systems by applying a KD framework named StuGAN. Specifically, we use a student discriminator to refine the teacher’s knowledge, and then both the generator and discriminator are enhanced by leveraging the refined knowledge via adversarial learning, which constrains the generator to produce fake data approximating both the ground truth and teacher’s predicted preferences, and enables the discriminator to distinguish between the preferences of the generator and more confusing preferences of the teacher from the ground truth. Finally, we conduct extensive experiments on two real-world datasets (i.e., Ciao and LastFM), and the results show that our approach can reduce the volume of model parameters by as much as half while maintaining comparable recommendation accuracy.
Deep online cross-modal hashing by a co-training mechanism

Batch-based cross-modal hashing retrieval methods have made great progress. However, they could not be applied in scenarios where new data continuously arrives in a stream. To this end, a few online cross-modal hashing retrieval methods have been proposed. However, they are based on shallow models, which may result in a suboptimal retrieval performance. Therefore, we propose Deep Online Cross-modal Hashing by a Co-training Mechanism (DOCHCM), which introduces deep learning to online cross-modal hashing by cooperatively training two subnetworks with two stages. DOCHCM addresses two problematics aspects. First, in each round, the image sub-network incrementally learns the hash codes of the current chunk of images by preserving the semantic similarity between their output features and the hash codes of the whole texts. The text sub-network incrementally learns the hash codes of the current chunk of texts by preserving the semantic similarity between their output features and the hash codes of the whole images. Second, knowledge distillation is leveraged to the image and text sub-networks to avoid catastrophic forgetting, which enables the two sub-networks not only to learn new knowledge but also to prevent the forgetting of the old knowledge. Extensive experiments on three benchmark datasets demonstrate that DOCHCM outperforms the state-of-the-art cross-modal hashing retrieval methods.
A Low-Cost Cross-Border Payment System Based on Auditable Cryptocurrency With Consortium Blockchain: Joint Digital Currency

Due to the involvement of a large number of intermediaries across different time zones in the correspondent banking process, existing interbank payment systems cannot provide cost-effective cross-border transactions. They also suffer from lack of transparency and long transaction delays. These issues can be solved by designing a cryptocurrency in an auditable manner using a permissioned blockchain where a group of authorities can govern the network. In this paper, we propose a low-cost, seamless cross-border payment system based on an auditable cryptocurrency that enables unspent transaction output-based transactions in a consortium blockchain network. To manage the blockchain, participating countries execute the energy-efficient proof of authority consensus algorithm with equal rights. Unlike conventional cryptocurrencies, dynamic decentralized identifiers (DIDs) are used as transacting addresses so that self-manageable authentication can be performed on-chain without any interaction with a trusted third party. The identity of transacting parties is known to respective DID issuers only. This approach enables peer-to-peer transactions while protecting user privacy and ensuring transparency without jeopardizing auditability. For user convenience, multi-party computation and multi-signature protocols are also provided. The system was implemented in Python, and the transaction mechanism was tested. This paper can help with ongoing research on blockchain-based cross-border payment solutions worldwide.
Syntactically Enhanced Dependency-POS Weighted Graph Convolutional Network for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis that presents great benefits to real-word applications. Recently, the methods utilizing graph neural networks over dependency trees are popular, but most of them merely considered if there exist dependencies between words, ignoring the types of these dependencies, which carry important information, as dependencies with different types have different effects. In addition, they neglected the correlations between dependency types and part-of-speech (POS) labels, which are helpful for utilizing dependency imformation. To address such limitations and the deficiency of insufficient syntactic and semantic feature mining, we propose a novel model containing three modules, which aims to leverage dependency trees more reasonably by distinguishing different dependencies and extracting beneficial syntactic and semantic features to further enhance model performance. To enrich word embeddings, we design a syntactic feature encoder (SynFE). In particular, we design Dependency-POS Weighted Graph Convolutional Network (DPGCN) to weight different dependencies by a graph attention mechanism we proposed. Additionally, to capture aspect-oriented semantic information, we design a semantic feature extractor (SemFE). Extensive experiments on five popular benchmark databases validate that our model can better employ dependency information and effectively extract favorable syntactic and semantic features to achieve new state-of-the-art performance.
Three learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines

Restricted Boltzmann Machines (RBMs) offer a versatile architecture for unsupervised machine learning that can in principle approximate any target probability distribution with arbitrary accuracy. However, the RBM model is usually not directly accessible due to its computational complexity, and Markov-chain sampling is invoked to analyze the learned probability distribution. For training and eventual applications, it is thus desirable to have a sampler that is both accurate and efficient. We highlight that these two goals generally compete with each other and cannot be achieved simultaneously. More specifically, we identify and quantitatively characterize three regimes of RBM learning: independent learning, where the accuracy improves without losing efficiency; correlation learning, where higher accuracy entails lower efficiency; and degradation, where both accuracy and efficiency no longer improve or even deteriorate. These findings are based on numerical experiments and heuristic arguments.
Single image super-resolution using Wasserstein generative adversarial network with gradient penalty

Due to its strong sample generating ability, Generative Adversarial Network (GAN) has been used to solve single image super-resolution (SISR) problem and obtains high perceptual quality super-resolution (SR) images. However, GAN suffers from the disadvantage of training instability, even fails to converge. In this paper, a new SISR method is proposed based on Wasserstein GAN, which is a training more stable GAN with Wasserstein metric. To further increase the SR performance and make the training process more easier and stable, two modifications are made on the original WGAN. First, a gradient penalty (GP) is adopted to replace weight clipping. Second, a new residual block with “pre-activation” of the weight layer is constructed in the generators of WGAN. Extensive experiments show that the proposed method yields superior SR performance than original GAN based SR methods and many other methods in accuracy and perceptual quality of ×4 magnification factor on four diverse testing datasets.