Reviewerly/exHarmony huggingface dataset:
From disruption to ransomware: Lessons From hackers

Since 2020, SARS-COV-2 (COVID-19) has forced organizations to pivot towards digital transformation. Yet, the same period has seen a dramatic rise in hacking and ransomware attacks, especially from internationally malicious actors attempting to simultaneously disrupt and profit. Although a knee-jerk reaction has been the adoption of new-fangled technologies as the only way to prevent, detect, and mitigate attacks, this case study highlights how imperative it is for organizations to proactively review and re-engineer their process inefficiencies and human errors as a systematic counter-strategy. Using fictitious characters reflecting real-world hacking processes and outfits, this case projects the need for a new cybersecurity order, where cybersecure process reengineering and human training deserve greater attention than simply investing in newer cybersecurity technologies.
Achieving fine-grained and flexible access control on blockchain-based data sharing for the Internet of Things

The traditional centralized data sharing systems have potential risks such as single point of failures and excessive working load on the central node. As a distributed and collaborative alternative, approaches based upon blockchain have been explored recently for Internet of Things (IoTs). However, the access from a legitimate user may be denied without the pre-defined policy and data update on the blockchain could be costly to the owners. In this paper, we first address these issues by incorporating the Accountable Subgroup Multi-Signature (ASM) algorithm into the Attribute-based Access Control (ABAC) method with Policy Smart Contract, to provide a finegrained and flexible solution. Next, we propose a policy-based Chameleon Hash algorithm that allows the data to be updated in a reliable and convenient way by the authorized users. Finally, we evaluate our work by comparing its performance with the benchmarks. The results demonstrate significant improvement on the effectiveness and efficiency.
Deep Motion Network for Freehand 3D Ultrasound Reconstruction

Freehand 3D ultrasound (US) has important clinical value due to its low cost and unrestricted field of view. Recently deep learning algorithms have removed its dependence on bulky and expensive external positioning devices. However, improving reconstruction accuracy is still hampered by difficult elevational displacement estimation and large cumulative drift. In this context, we propose a novel deep motion network (MoNet) that integrates images and a lightweight sensor known as the inertial measurement unit (IMU) from a velocity perspective to alleviate the obstacles mentioned above. Our contribution is two-fold. First, we introduce IMU acceleration for the first time to estimate elevational displacements outside the plane. We propose a temporal and multi-branch structure to mine the valuable information of low signal-to-noise ratio (SNR) acceleration. Second, we propose a multi-modal online self-supervised strategy that leverages IMU information as weak labels for adaptive optimization to reduce drift errors and further ameliorate the impacts of acceleration noise. Experiments show that our proposed method achieves the superior reconstruction performance, exceeding state-of-the-art methods across the board.
Regularizing Graph Neural Networks via Consistency-Diversity Graph Augmentations

Despite the remarkable performance of graph neural networks (GNNs) in semi-supervised learning, it is criticized for not making full use of unlabeled data and suffering from over-fitting. Recently, graph data augmentation, used to improve both accuracy and generalization of GNNs, has received considerable attentions. However, one fundamental question is how to evaluate the quality of graph augmentations in principle? In this paper, we propose two metrics, Consistency and Diversity, from the aspects of augmentation correctness and generalization. Moreover, we discover that existing augmentations fall into a dilemma between these two metrics. Can we find a graph augmentation satisfying both consistency and diversity? A well-informed answer can help us understand the mechanism behind graph augmentation and improve the performance of GNNs. To tackle this challenge, we analyze two representative semi-supervised learning algorithms: label propagation (LP) and consistency regularization (CR). We find that LP utilizes the prior knowledge of graphs to improve consistency and CR adopts variable augmentations to promote diversity. Based on this discovery, we treat neighbors as augmentations to capture the prior knowledge embodying homophily assumption, which promises a high consistency of augmentations. To further promote diversity, we randomly replace the immediate neighbors of each node with its remote neighbors. After that, a neighbor-constrained regularization is proposed to enforce the predictions of the augmented neighbors to be consistent with each other. Extensive experiments on five real-world graphs validate the superiority of our method in improving the accuracy and generalization of GNNs.
A Spatial and Geometry Feature-Based Quality Assessment Model for the Light Field Images

This paper proposes a new full-reference image quality assessment (IQA) model for performing perceptual quality evaluation on light field (LF) images, called the spatial and geometry feature-based model (SGFM). Considering that the LF image describe both spatial and geometry information of the scene, the spatial features are extracted over the sub-aperture images (SAIs) by using contourlet transform and then exploited to reflect the spatial quality degradation of the LF images, while the geometry features are extracted across the adjacent SAIs based on 3D-Gabor filter and then explored to describe the viewing consistency loss of the LF images. These schemes are motivated and designed based on the fact that the human eyes are more interested in the scale, direction, contour from the spatial perspective and viewing angle variations from the geometry perspective. These operations are applied to the reference and distorted LF images independently. The degree of similarity can be computed based on the above-measured quantities for jointly arriving at the final IQA score of the distorted LF image. Experimental results on three commonly-used LF IQA datasets show that the proposed SGFM is more in line with the quality assessment of the LF images perceived by the human visual system (HVS), compared with multiple classical and state-of-the-art IQA models.
Fast LiDAR R-CNN: Residual Relation-Aware Region Proposal Networks for Multiclass 3-D Object Detection

Three-dimensional (3-D) object detection from Light Detection and Ranging (LiDAR) point clouds is the most challenging problem in practical 3-D scene understanding. This paper presents a fast two-stage 3-D object detection framework that jointly integrates voxel and point feature representations. Specifically, the first stage takes the voxel features from raw point clouds as inputs and then outputs bird eye’s view (BEV) feature maps and structured voxel center points. The BEV feature map and objects’ empirical sizes are used for generating 3-D proposals. The second stage extracts region pointwise features for the final object prediction using the 3-D proposals generated in the first stage. The proposed framework runs at 30 frames per second (FPS) with high performance. To improve the performance of the pedestrian class, we propose a dual-path feature module (DFM) to learn and pass features from BEV feature maps. Moreover, we propose a lightweight relation-aware module (LRAM) for sparse point clouds to enhance the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attention</i> ability of region proposal networks by exploring the relationships between pixels and between channels. On the KITTI benchmark suite, performed experiments show that the proposed LiDAR-based method achieves a new state-of-the-art on the three classes in 3-D performance (Easy, Moderate, Hard): car (92.53%, 84.70%, 82.32%), pedestrian (68.30%, 61.20%, 55.17%), cyclist (91.73%, 72.61%, 68.24%).
Sign language identification and recognition: A comparative study

Abstract Sign Language (SL) is the main language for handicapped and disabled people. Each country has its own SL that is different from other countries. Each sign in a language is represented with variant hand gestures, body movements, and facial expressions. Researchers in this field aim to remove any obstacles that prevent the communication with deaf people by replacing all device-based techniques with vision-based techniques using Artificial Intelligence (AI) and Deep Learning. This article highlights two main SL processing tasks: Sign Language Recognition (SLR) and Sign Language Identification (SLID). The latter task is targeted to identify the signer language, while the former is aimed to translate the signer conversation into tokens (signs). The article addresses the most common datasets used in the literature for the two tasks (static and dynamic datasets that are collected from different corpora) with different contents including numerical, alphabets, words, and sentences from different SLs. It also discusses the devices required to build these datasets, as well as the different preprocessing steps applied before training and testing. The article compares the different approaches and techniques applied on these datasets. It discusses both the vision-based and the data-gloves-based approaches, aiming to analyze and focus on main methods used in vision-based approaches such as hybrid methods and deep learning algorithms. Furthermore, the article presents a graphical depiction and a tabular representation of various SLR approaches.
Semi-supervised learning in cancer diagnostics

In cancer diagnostics, a considerable amount of data is acquired during routine work-up. Recently, machine learning has been used to build classifiers that are tasked with cancer detection and aid in clinical decision-making. Most of these classifiers are based on supervised learning (SL) that needs time- and cost-intensive manual labeling of samples by medical experts for model training. Semi-supervised learning (SSL), however, works with only a fraction of labeled data by including unlabeled samples for information abstraction and thus can utilize the vast discrepancy between available labeled data and overall available data in cancer diagnostics. In this review, we provide a comprehensive overview of essential functionalities and assumptions of SSL and survey key studies with regard to cancer care differentiating between image-based and non-image-based applications. We highlight current state-of-the-art models in histopathology, radiology and radiotherapy, as well as genomics. Further, we discuss potential pitfalls in SSL study design such as discrepancies in data distributions and comparison to baseline SL models, and point out future directions for SSL in oncology. We believe well-designed SSL models to strongly contribute to computer-guided diagnostics in malignant disease by overcoming current hinderances in the form of sparse labeled and abundant unlabeled data.
Reward-Penalty Weighted Ensemble for Emotion State Classification from Multi-Modal Data Streams

Researchers have shown the limitations of using the single-modal data stream for emotion classification. Multi-modal data streams are therefore deemed necessary to improve the accuracy and performance of online emotion classifiers. An online decision ensemble is a widely used approach to classify emotions in real-time using multi-modal data streams. There is a plethora of online ensemble approaches; these approaches use a fixed parameter ([Formula: see text]) to adjust the weights of each classifier (called penalty) in case of wrong classification and no reward for a good performing classifier. Also, the performance of the ensemble depends on the [Formula: see text], which is set using trial and error. This paper presents a new Reward-Penalty-based Weighted Ensemble (RPWE) for real-time multi-modal emotion classification using multi-modal physiological data streams. The proposed RPWE is thoroughly tested using two prevalent benchmark data sets, DEAP and AMIGOS. The first experiment confirms the impact of the base stream classifier with RPWE for emotion classification in real-time. The RPWE is compared with different popular and widely used online ensemble approaches using multi-modal data streams in the second experiment. The average balanced accuracy, F1-score results showed the usefulness and robustness of RPWE in emotion classification in real-time from the multi-modal data stream.
Using natural language processing on free-text clinical notes to identify patients with long-term COVID effects

As of May 15th, 2022, the novel coronavirus SARS-COV-2 has infected 517 million people and resulted in more than 6.2 million deaths around the world. About 40% to 87% of patients suffer from persistent symptoms weeks or months after their original infection. Despite remarkable progress in preventing and treating acute COVID-19 conditions, the clinical diagnosis of long-term COVID remains difficult. In this work, we use free-text clinical notes and natural language processing (NLP) techniques to explore long-term COVID effects. We first obtain free-text clinical notes from 719 outpatient encounters representing patients treated by physicians at Emory Clinic to detect patterns in patients with long-term COVID symptoms. We apply state-of-the-art NLP frameworks to automatically identify patients with long-term COVID effects, achieving 0.881 recall (sensitivity) score for note-level prediction. We further interpret the prediction outcomes and discuss potential phenotypes. Our work aims to provide a data-driven solution to identify patients who have developed persistent symptoms after acute COVID infection. With this work, clinicians may be able to identify patients who have long-term COVID symptoms to optimize treatment.
White blood cell classification via a discriminative region detection assisted feature aggregation network

White blood cell (WBC) classification plays an important role in human pathological diagnosis since WBCs will show different appearance when they fight with various disease pathogens. Although many previous white blood cell classification have been proposed and earned great success, their classification accuracy is still significantly affected by some practical issues such as uneven staining, boundary blur and nuclear intra-class variability. In this paper, we propose a deep neural network for WBC classification via discriminative region detection assisted feature aggregation (DRFA-Net), which can accurately locate the WBC area to boost final classification performance. Specifically, DRFA-Net uses an adaptive feature enhancement module to refine multi-level deep features in a bilateral manner for efficiently capturing both high-level semantic information and low-level details of WBC images. Considering the fact that background areas could inevitably produce interference, we design a network branch to detect the WBC area with the supervision of segmented ground truth. The bilaterally refined features obtained from two directions are finally aggregated for final classification, and the detected WBC area is utilized to highlight the features of discriminative regions by an attention mechanism. Extensive experiments on several public datasets are conducted to validate that our proposed DRFA-Net can obtain higher accuracies when compared with other state-of-the-art WBC classification methods.
An Energy-efficient And Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT

Many anomaly detection techniques have been adopted by Industrial Internet of Things (IIoT) for improving self-diagnosing efficiency and infrastructures security. However, they are usually associated with the issues of computational-hungry and “black box.” Thus, it becomes important to ensure that the detection is not only accurate but also energy-efficient and trustworthy. In this article, we propose an Energy-efficient And Trustworthy Unsupervised anomaly detection framework (EATU) for IIoT. The framework consists of two levels of feature extraction: (1) Autoencoder-based feature extraction and (2) Efficient DeepExplainer-based explainable feature selection. We propose an Efficient DeepExplainer model based on perturbation-focused sampling, which demonstrates the most computational efficiency among state-of-the-art explainable models. With the important features selected by Efficient DeepExplainer, the rationale of why an anomaly detection decision was made is given, enhancing the trustworthiness of the detection as well as improving the accuracy of anomaly detection. Three real-world IIoT datasets with high-dimensional features are used to validate the effectiveness of the proposed framework. Extensive experimental results demonstrate that in comparison with the state-of-the-art, our framework has the attributes of improved accuracy, trustworthiness (in terms of correctness and stability of the explanation), and energy-efficiency (in terms of wall-clock-time and resource usage).
Self-supervised cycle-consistent learning for scale-arbitrary real-world single image super-resolution

Whether conventional machine learning-based or current deep neural networks-based single image super-resolution (SISR) methods, they are generally trained and validated on synthetic datasets, in which low-resolution (LR) inputs are artificially produced by degrading high-resolution (HR) images based on a hand-crafted degradation model (e.g., bicubic downsampling). One of the main reasons for this is that it is challenging to build a realistic dataset composed of real-world LR–HR image pairs. However, a domain gap exists between synthetic and real-world data because the degradations in real scenarios are more complicated, limiting the performance in practical applications of SISR models trained with synthetic data. To address these problems, we propose a Self-supervised Cycle-consistent Learning-based Scale-Arbitrary Super-Resolution framework (SCL-SASR) for real-world images. Inspired by the Maximum a Posteriori estimation, our SCL-SASR consists of a Scale-Arbitrary Super-Resolution Network (SASRN) and an inverse Scale-Arbitrary Resolution-Degradation Network (SARDN). SARDN and SASRN restrain each other with the bidirectional cycle consistency constraints as well as image priors, making SASRN adapt to the image-specific degradation well. Meanwhile, considering the lack of targeted training images and the complexity of realistic degradations, SCL-SASR is designed to be online optimized solely with the LR input prior to the SR reconstruction. Benefitting from the flexible architecture and the self-supervised learning manner, SCL-SASR can easily super-resolve new images with arbitrary integer or non-integer scaling factors. Experiments on real-world images demonstrate the high flexibility and good applicability of SCL-SASR, which achieves better reconstruction performance than state-of-the-art self-supervised learning-based SISR methods as well as several external dataset-trained SISR models.
A Survey on Blockchain and Artificial Intelligence Technologies for Enhancing Security and Privacy in Smart Environments

Smart environments consist of a collection of sensors, actuators, and numerous computing units that improve human life. With the booming of smart environments, data generation has been notably increasing in recent years, which must be managed in a smart and optimal manner. The components (i.e., workstations and cloud) used for data processing are not the best to recommend since it is risky and resource costing. For that matter, enterprises, firms and companies are deploying blockchain technologies (BT) as a more suitable alternative. In fact, blockchain is a distributed transaction ledger ensuring the reliability and transparency of data. However, BT faces some inherent security challenges such as DoS, eclipse and double spending attacks as well as Advanced Persistent Threat (APT) and malware. Thus, advanced anomaly detection and mitigation approaches, especially the ones using artificial intelligence (AI) techniques (e. g. Machine Learning, Deep Learning, Federated Learning) are required to address the aforementioned issues. In combination, AI and BT are capable of detecting anomalies within blockchain networks with high accuracy. In this paper, with a focus on cyber security issues, we explore the challenges of blockchain deployment in smart environments. Additionally, we explore the use of anomaly detection AI-based techniques as a ledger of blockchain technologies to address the security issues in smart environments. Thus, we propose a framework that emphasizes the challenges of BT, values and capabilities of BT-AI integration. We also present research trends to highlight potential research paths for improving the security of blockchain networks using artificial intelligence.
N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation

Medical image segmentation is an essential component of computer-aided diagnosis (CAD) systems. Thyroid nodule segmentation using ultrasound images is a necessary step for the early diagnosis of thyroid diseases. An encoder-decoder based deep convolutional neural network (DCNN), like U-Net architecture and its variants, has been extensively used to deal with medical image segmentation tasks. In this article, we propose a novel N-shape dense fully convolutional neural network for medical image segmentation, referred to as N-Net. The proposed framework is composed of three major components: a multi-scale input layer, an attention guidance module, and an innovative stackable dilated convolution (SDC) block. First, we apply the multi-scale input layer to construct an image pyramid, which achieves multi-level receiver field sizes and obtains rich feature representation. After that, the U-shape convolutional network is employed as the backbone structure. Moreover, we use the attention guidance module to filter the features before several skip connections, which can transfer structural information from previous feature maps to the following layers. This module can also remove noise and reduce the negative impact of the background. Finally, we propose a stackable dilated convolution (SDC) block, which is able to capture deep semantic features that may be lost in bilinear upsampling. We have evaluated the proposed N-Net framework on a thyroid nodule ultrasound image dataset (called the TNUI-2021 dataset) and the DDTI publicly available dataset. The experimental results show that our N-Net model outperforms several state-of-the-art methods in the thyroid nodule segmentation tasks.