Reviewerly/exHarmony huggingface dataset:
A Survey on Industrial Control System Digital Forensics: Challenges, Advances and Future Directions

Operational Technology (OT) systems have become increasingly interconnected and automated, consequently resulting in them becoming targets of cyber attacks, with the threat towards a range of critical national infrastructure (CNI) sectors becoming heightened. This is particularly the case for Industrial Control Systems (ICS), which control and operate the physical processes in CNI sectors such as water treatment, electrical generation and manufacturing. Unlike information technology (IT) systems, ICS have unique cyber-physical characteristics and related safety requirements, making them an attractive target for attacks given the physical consequences that can occur. As a result, the requirement to respond and learn from previous and new attacks is also increasing, with digital forensics playing a significant role in this process. The aim of this paper is to discuss the main issues and existing limitations related to ICS digital forensic. The field of ICS digital forensics is relatively under-developed and does not have the same levels of maturity as IT digital forensics. Although the amount of research on cyber security for ICS is increasing, many unique challenges still exist that pose as barriers to the development and deployment of ICS forensic capabilities. We provide an extensive discussion on these challenges, categorising them into technical, socio-technical, and operational and legal themes. Furthermore, the relationship between these challenge themes as well as the inter-challenge dependencies are also examined. Furthermore, this work discusses ICS forensic advances in relation to the digital forensics life chain, specifically forensic readiness and investigations. The areas of digital forensic training and processes models for ICS are given particular focus. Moreover, we assess the technologies and tools that have been either applied to or developed for ICS components and networks, giving special attention to forensic acquisition and analysis methods. An examination into the specific ICS digital forensic data sources and artefacts is also presented, highlighting that until recently, this was limited to descriptions of generic data formats. In addition, this paper provides an overview of several key ICS attacks, summarising the specific techniques used, data artefacts of interest, and proposing lessons learnt. Finally, this paper presents open discussions on future ICS digital forensics research directions and on-going issues, covering both short and long-term areas that can be addressed to improve the ICS digital forensics capability.
Progressive Keypoint Detection with Dense Siamese Network for SAR Image Registration

SAR image registration plays a key role in the applications such as change detection, image mosaic, which is one of the most challenging tasks in recent years due to speckle noise, geometric distortion and nonlinear radiation differences between SAR images. In the feature-based SAR image registration methods, the repeatability of keypoints and the effectiveness of feature descriptors could directly affect the registration accuracy. In this paper, we propose a stable feature intersection-based (FI) keypoint detector, which contains three progressive detectors, i.e., phase coherency (PC) detector, horizontal/vertical and major/minor diagonal oriented gradient detectors, and local coefficient of variation (LCoV) detector. Our proposed keypoint detector can not only effectively extract keypoints with high repeatability, but also greatly reduce the number of false keypoints, thus reducing the computational cost of feature description and matching. Then we propose the Cross Stage Partial Siamese Network (CSPSNet) to rapidly extract feature descriptors containing both deep and shallow features, which can be used to obtain more correct matching point pairs than traditional man-made shallow descriptors. In addition, we design a Hard Example Mining Loss (HEMLoss) to minimize the matching distance between the matching descriptor and adaptive selected non-matching descriptors. Experimental results on different pairs of SAR images demonstrate that our proposed method achieves better performance than other state-of-the-art methods.
Evolution of Health Information Sharing Between Health Care Organizations: Potential of Nonfungible Tokens

This study attempts to explain the development and progress of the technology used for sharing health information across health care organizations (such as hospitals and physicians' offices). First, we describe the strengths and weaknesses of traditional sharing models, health information exchange (HIE), and blockchain-based HIE. Second, the potential use of nonfungible token (NFT) protocols in HIE models is proposed as the next possible move for information-sharing initiatives in health care. In addition to some potential opportunities and distinguishing features (eg, ownability, verifiability, and incentivization), we identify the uncertainty and risks associated with the application of NFTs, such as the lack of a dedicated regulatory framework for legal ownership of digital patient data. This paper is among the first to discuss the potential of NFTs in health care. The use of NFTs in HIE networks could generate a new stream of research for future studies. This study provides practical insights into how the technological foundations of information-sharing efforts in health care have developed and diversified from earlier forms.
DR-CapsNet with CAEMRA: Looking deep inside instance for boosting object detection effect

Capsule Network (CapsNet) has shown better representability especially in the parsing of part-whole correlation which is indispensable for object detection. However, since low-level capsules vote in favor of every high-level capsule irrespective of their interrelationship, such blindly fully-connected routing manner is at the risk of misassignments. In fact, the higher the relevancy of capsules across two consecutive layers, the higher the likelihood of being routed together. Inspired by this, we propose to steer capsule assignment by employing such correlations to constrain the bottom-up voting scope, hoping the ”fragile” votes are eliminated. We formula such pipeline as a Dual-Restricted Capsule Network (DR-CapsNet) with Correlation-Aware Expectation–Maximum​ Routing-by-Agreement (CAEMRA) for boosting object detection effect. Four constraints, dubbed Intra-Object Cohesiveness Quantification (IOCQ), Part Backtracking (PB), Vote Screening (VS), and Feature Correlation Reevaluation (FCR), are customized and embedded into CAEMRA to restrain the voting scope. They stipulate that only these primary capsules (representing components) meeting the criteria of both internal consistency and external association are permissible to update entity capsules (representing the whole/composites). As a result, the capsule assignment is achieved by routing highly correlated capsules during bottom-up ”part backtracking” procedure, whilst the part-object relationships among captured entities are refined for object detection. CAEMRA enables high-level capsules to optionally aggregate projection from non-spatially-fixed sets of low-level capsules. Quantitative and ablation verifications on VOC2007, VOC2012, OICOD18, ILSVRC17, and COCO18 reveal the superiority of DR-CapsNet over the state-of-the-art models.
A model adaptive updating kernel correlation filter tracker with deep CNN features

The tracker based on correlation filter shows excellent performance in tracking accuracy and running speed. However, the models of correlated filter trackers are always updated with fixed weights, which can degrade the tracking performance when the target in a variety of challenging scenarios. In this paper, we present a model adaptive updating method based on a fuzzy system, which can set different updating weights on each frame to effectively deal with the challenging scenarios in the tracking process. Attractively, this method can be perfectly used in all trackers based on correlation filtering. In addition, we combine deep CNN features that can describe target semantics with HOG features that have spatial descriptions. Using their complementarity to target descriptions, we establish HOG-based filter model and CNN-based filter model. To two response maps of the models, we propose a different fusion strategy based on quality measurement of tracking results, which can balance the accuracy and robustness of the tracker. Experiments on OTB-2013, OTB-2015, benchmark videos, and VOT2018 dataset show that our tracker (called MACF) is effective and exhibits competitive results compared with the recent state-of-the-art (SOTA) trackers.
Displacement extraction of background-oriented schlieren images using Swin Transformer

Displacement extraction of background-oriented schlieren (BOS) is an essential step in BOS reconstruction, which directly determines the accuracy of the results. Typically, the displacement is calculated from the background images with and without inhomogeneous flow using the cross-correlation (CC) or optical flow (OF) method. This paper discusses the disadvantages of the CC and OF methods, and an end-to-end deep neural network was designed to estimate the BOS displacement. The proposed network is based on a Swin Transformer, which can build long-range correlations. A synthetic dataset used for training was generated using the simulated flow field by computational fluid dynamics. After training, the displacement can be obtained using the BOS image pair without additional parameters. Finally, the effectiveness of the proposed network was verified through experiments. The experiments illustrate that the proposed method performs stably on synthetic and real experimental images and outperforms conventional CC or OF methods and classic convolutional neural networks for OF tasks.
ADTR: Anomaly Detection Transformer with Feature Reconstruction

Anomaly detection with only prior knowledge from normal samples attracts more attention because of the lack of anomaly samples. Existing CNN-based pixel reconstruction approaches suffer from two concerns. First, the reconstruction source and target are raw pixel values that contain indistinguishable semantic information. Second, CNN tends to reconstruct both normal samples and anomalies well, making them still hard to distinguish. In this paper, we propose Anomaly Detection TRansformer (ADTR) to apply a transformer to reconstruct pre-trained features. The pre-trained features contain distinguishable semantic information. Also, the adoption of transformer limits to reconstruct anomalies well such that anomalies could be detected easily once the reconstruction fails. Moreover, we propose novel loss functions to make our approach compatible with the normal-sample-only case and the anomaly-available case with both image-level and pixel-level labeled anomalies. The performance could be further improved by adding simple synthetic or external irrelevant anomalies. Extensive experiments are conducted on anomaly detection datasets including MVTec-AD and CIFAR-10. Our method achieves superior performance compared with all baselines.
Distributed Intelligence in Wireless Networks

The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, and security and privacy concerns caused by billions of connected wireless devices and typically zillions of bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at the point of data generation as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid the transmission of the large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Besides, the deployment of AI techniques to redesign end-to-end communication is attracting attention to improve communication performance. Therefore, the interaction of AI and wireless communications generates a new concept, named native AI wireless networks. In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native AI wireless networks, with a focus on the design of distributed learning architectures for heterogeneous networks, on AI-enabled edge computing, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications. We highlight the advantages of hybrid distributed learning architectures compared to state-of-the-art distributed learning techniques. We summarize the challenges of existing research contributions in distributed intelligence in wireless networks and identify potential future opportunities.
A Lightweight Transformer-Based Approach of Specific Emitter Identification for the Automatic Identification System

The automatic identification system (AIS) is the automatic tracking system for automatic traffic control and collision avoidance services, which plays an important role in maritime traffic safety. However, it faces a possible security threat when the maritime mobile service identity (MMSI) that specifies the vessels’ identity in AIS is illegally counterfeited. To guarantee the communication security of AIS for preventing fraudulent devices, we design a novel lightweight Transformer-based network GLFormer for specific emitter identification (SEI) to provide an extra security layer for AIS terminal emitters. Concretely, the gated local attention unit (GLAU) and the gated sliding local attention unit (GSLAU) modules that combine a simplified gated attention unit (GAU) and a sliding local self-attention (SLA) are developed in GLFormer to extract the radio frequency fingerprint (RFF) features automatically from the raw in-phase signals. Especially, the simplified GAU focuses on more critical RFF features and filters out the irrelevant information from the raw signal to improve performance, which is also a single-head self-attention module with fewer parameters for lightweight. Meanwhile, the SLA limits self-attention operation to a window, introducing the inductive bias of local information to enhance performance further and reducing the quadratic computational complexity to linearity for efficiency. Experimental results demonstrate that the GLFormer achieves 96.31% and 89.38% identification accuracy in the constructed AIS transient and AIS steady-state datasets with 50 vessels, respectively. The 99.90% identification accuracy is achieved in the universal software radio peripheral (USRP) dataset with ten devices. It is not only better than the existing methods but requires much fewer parameters and lower computational complexity; besides, it is also suitable for working with long signal sequences.
Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems

Supported by some of the major revolutionary technologies, such as Internet of Vehicles (IoVs), Edge Computing, and Machine Learning (ML), the traditional Vehicular Networks (VNs) are changing drastically and converging rapidly into one of the most complex, highly intelligent, and advanced networking systems, mostly known as Intelligent Transportation System (ITS). Recently, distributed ML techniques, such as Federated Learning (FL) have gained huge popularity mainly for their advantages in terms of intelligence sharing and privacy concerns. VNs are a natural contender for exploiting FL for solving challenging problems; however, their limited resources, dynamic nature, high speed, and reduced latency requirements often become the bottleneck. V2X communication technologies allow vehicular terminals (VTs) to share their valuable local environment parameters and become aware of their surroundings. Such information can be utilized to build a more sustainable and affordable FL platform for serving VTs. Gaining from recently introduced 3D architectures, integrating terrestrial and aerial edge computing layers, we present here a distributed FL platform able to distribute the FL process on a 3D fashion while reducing the overall communication cost for providing vehicular services. The framework is defined as a constrained optimization problem for reducing the overall FL process cost through a proper network selection between various nodes. We have modeled the FL network selection problem as a sequential decision-making process through a Markov Decision Process (MDP) with time-dependent state transition probabilities. A computation-efficient value iteration algorithm is adapted for solving the MDP. Comparison with various benchmark methods shows the overall improvement in terms of latency, energy, and FL performance.
Restricted Black-Box Adversarial Attack Against DeepFake Face Swapping

DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person. In order to prevent this fraud, some researchers have begun to study the adversarial methods against DeepFake or face manipulation. However, existing works mainly focus on the white-box setting or the black-box setting driven by abundant queries, which severely limits the practical application of these methods. To tackle this problem, we introduce a practical adversarial attack that does not require any queries to the facial image forgery model. Our method is built on a substitute model based on face reconstruction and then transfers adversarial examples from the substitute model directly to inaccessible black-box DeepFake models. Specially, we propose the Transferable Cycle Adversary Generative Adversarial Network (TCA-GAN) to construct the adversarial perturbation for disrupting unknown DeepFake systems. We also present a novel post-regularization module for enhancing the transferability of generated adversarial examples. To comprehensively measure the effectiveness of our approaches, we construct a challenging baseline of DeepFake adversarial attacks for future development. Extensive experiments impressively show that the proposed adversarial attack method makes the visual quality of DeepFake face images plummet so that they are easier to be detected by humans and algorithms. Moreover, we demonstrate that the proposed algorithm can be generalized to offer face image protection against various face translation methods.
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation

Transformers, born to remedy the inadequate receptive fields of CNNs, have drawn explosive attention recently. However, the daunting computational complexity of global representation learning, together with rigid window partitioning, hinders their deployment in medical image segmentation. This work aims to address the above two issues in transformers for better medical image segmentation.We propose a boundary-aware lightweight transformer (BATFormer) that can build cross-scale global interaction with lower computational complexity and generate windows flexibly under the guidance of entropy. Specifically, to fully explore the benefits of transformers in long-range dependency establishment, a cross-scale global transformer (CGT) module is introduced to jointly utilize multiple small-scale feature maps for richer global features with lower computational complexity. Given the importance of shape modeling in medical image segmentation, a boundary-aware local transformer (BLT) module is constructed. Different from rigid window partitioning in vanilla transformers which would produce boundary distortion, BLT adopts an adaptive window partitioning scheme under the guidance of entropy for both computational complexity reduction and shape preservation.BATFormer achieves the best performance in Dice of 92.84 %, 91.97 %, 90.26 %, and 96.30 % for the average, right ventricle, myocardium, and left ventricle respectively on the ACDC dataset and the best performance in Dice, IoU, and ACC of 90.76 %, 84.64 %, and 96.76 % respectively on the ISIC 2018 dataset. More importantly, BATFormer requires the least amount of model parameters and the lowest computational complexity compared to the state-of-the-art approaches.Our results demonstrate the necessity of developing customized transformers for efficient and better medical image segmentation. We believe the design of BATFormer is inspiring and extendable to other applications/frameworks.
A unified pruning framework for vision transformers

In this study, we proposed a novel method called UP-ViTs to prune ViTs in a unified manner. Our framework can prune all components in a ViT and its variants, maintain the models' structure, and generalize well into downstream tasks. UP-ViTs achieve state-of-the-art results when pruning various ViT backbones. Moreover, we studied the transferring ability of the compressed model and found that our UP-ViTs also outperform original ViTs. We also extended our method into NLP tasks and obtained more efficient transformer models. Please refer to the appendix for more details.
Generalization of vision pre-trained models for histopathology

Out-of-distribution (OOD) generalization, especially for medical setups, is a key challenge in modern machine learning which has only recently received much attention. We investigate how different convolutional pre-trained models perform on OOD test data-that is data from domains that have not been seen during training-on histopathology repositories attributed to different trial sites. Different trial site repositories, pre-trained models, and image transformations are examined as specific aspects of pre-trained models. A comparison is also performed among models trained entirely from scratch (i.e., without pre-training) and models already pre-trained. The OOD performance of pre-trained models on natural images, i.e., (1) vanilla pre-trained ImageNet, (2) semi-supervised learning (SSL), and (3) semi-weakly-supervised learning (SWSL) models pre-trained on IG-1B-Targeted are examined in this study. In addition, the performance of a histopathology model (i.e., KimiaNet) trained on the most comprehensive histopathology dataset, i.e., TCGA, has also been studied. Although the performance of SSL and SWSL pre-trained models are conducive to better OOD performance in comparison to the vanilla ImageNet pre-trained model, the histopathology pre-trained model is still the best in overall. In terms of top-1 accuracy, we demonstrate that diversifying the images in the training using reasonable image transformations is effective to avoid learning shortcuts when the distribution shift is significant. In addition, XAI techniques-which aim to achieve high-quality human-understandable explanations of AI decisions-are leveraged for further investigations.
Survey on leveraging pre-trained generative adversarial networks for image editing and restoration

Generative adversarial networks (GANs) have drawn enormous attention due to their simple yet effective training mechanism and superior image generation quality. With the ability to generate photorealistic high-resolution (e.g., 1024 × 1024) images, recent GAN models have greatly narrowed the gaps between the generated images and the real ones. Therefore, many recent studies show emerging interest to take advantage of pre-trained GAN models by exploiting the well-disentangled latent space and the learned GAN priors. In this study, we briefly review recent progress on leveraging pre-trained large-scale GAN models from three aspects, i.e., (1) the training of large-scale generative adversarial networks, (2) exploring and understanding the pre-trained GAN models, and (3) leveraging these models for subsequent tasks like image restoration and editing.