Reviewerly/exHarmony huggingface dataset:
Deep Non-Parallel Hyperplane Support Vector Machine for Classification

In the last few decades, deep learning based on neural networks has become popular for the classification tasks, which combines feature extraction with the classification tasks and always achieves the satisfactory performance.Non-parallel hyperplane support vector machine (NPHSVM) aims at constructing two non-parallel hyperplanes to classify data and extracted features are always used to be input data for NPHSVM.As for NPHSVM, extracted features will greatly influence the performance of the model to some extent.Therefore, in this paper, we propose a novel DNHSVM for classification, which combines deep feature extraction with the generation of hyperplanes seamlessly.Each hyperplane is close to its own class and as far as possible to other classes, and deep features are friendly for classification and samples are easy to be classified.Experiments on UCI datasets show the effectiveness of our proposed method, which outperforms other compared state-of-the-art algorithms.
A Comprehensive Study of Digital Image Steganographic Techniques

Steganography surpasses other mechanisms of securing data from potential threats. The modern digital arena calls for robust information hiding techniques and, thus, it has always been a flash point for researchers and academicians. Nowadays, transmission is susceptible to numerous hacks while sharing secret information through typical correspondence channel. Accordingly, everybody needs the classification, respectability, and realness of his or her privileged information. Particularly, different techniques are used to take on these security issues like advanced declaration, computerized mark, and cryptography. Nevertheless, these strategies alone cannot be negotiated. Steganography is a revolution where current information compression, data hypothesis, spread range, and cryptography advancements are integrated to meet the requirements for protection of data over the Internet. This study investigates and critically analyses various existing cover steganography techniques and identifies the valuable region that everyone can be benefited. Moreover, we present a comprehensive overview of the fundamental concepts with in the domain of the steganographic methods and steganalysis. These systems have been depicted in numerous areas of the steganography such as spatial space, transform domain, and adaptive space. Moreover, each space has its particular traits. A few regularly involved techniques for improving the steganographic security and upgrading steganalysis capacity are elaborated, summed up; and conceivable examination patterns are talked about. We also systematically separate different methodologies in our review and show their pros and cons, qualities, challenges and significance.
An Improved Spectrum Trading Design Based on Dynamic Credit Aggregate-Signature Blockchain

At present, the number of incoming devices and mobile data traffic has seen explosive growth, and spectrum resources are difficult to meet the increasing demand. The traditional centralized spectrum management model suffers from low spectrum utilization, high maintenance costs, and data security issues. The decentralized and secure nature of blockchain technology is a natural advantage in solving the problems faced by the current spectrum management model. However, when introducing traditional blockchain technology into spectrum management, it faces problems such as high latency and high resource consumption. In this letter, we design a new blockchain-enabled spectrum trading framework consisting of the access layer, distribution layer, and core layer, and then propose a user benefit-maximum spectrum trading matching scheme. To solve the problem of latency and energy consumption of consensus algorithms in blockchains, the dynamic credit aggregate-signature Byzantine fault tolerance (DCABFT) algorithm based on the Boneh-Lynn-Shacham (BLS) aggregated signature is proposed. Simulation results demonstrate that the proposed scheme can greatly improve the transaction latency and users’ quality of service.
Visual User-Generated Content Verification in Journalism: An Overview

Over the past few years, social media has become an indispensable part of the news generation and dissemination cycle on the global stage. These digital channels along with the easy-to-use editing tools have unfortunately created a medium for spreading mis-/disinformation containing visual content. Media practitioners and fact-checkers continue to struggle with scrutinising and debunking visual user-generated content (UGC) quickly and thoroughly as verification of visual content requires a high level of expertise and could be exceedingly complex amid the existing computational tools employed in newsrooms. The aim of this study is to present a forward-looking perspective on how visual UGC verification in journalism can be transformed by multimedia forensics research. We elaborate on a comprehensive overview of the five elements of the UGC verification and propose multimedia forensics as the sixth element. In addition, different types of visual content forgeries and detection approaches proposed by the computer science research community are explained. Finally, a mapping of the available verification tools media practitioners rely on is created along with their limitations and future research directions to gain the confidence of media professionals in using multimedia forensics tools in their day-to-day routine.
Agent-Based Image Contrast Enhancement Algorithm

One crucial step in several image processing and computer vision applications is Image Contrast Enhancement (ICE), whose main objective is to improve the quality of the information contained in the processed images. Most of the proposed schemes attack the problem by redistributing the pixel intensities in a histogram, leading to undesirable effects such as noise amplification, over-saturation, and lousy human perception. On the other hand, Agent-Based Models (ABM) are computational models that allow describing the behavior and interactions of autonomous agents when they operate cooperatively. These agents follow behavioral rules rather than mathematical formulations. This mechanism allows the implementation of complex behavioral patterns in agents through their interactions. This paper proposes a two-step method where pixels in the processed image are considered agents whose behavioral rules permit to enhance significatively the contrast. In our approach, the interactions among the agents are characterized by the differences in intensity values among the pixels or agents. In the first step, pixels or agents that present enough high differences in their intensity are modified to increase even more their differences. In the second step, pixels or agents that maintain a very small difference are altered to assume a homogeneous intensity value. The proposed approach has been tested considering different public datasets commonly used in the literature. Its results are also compared with those produced by other well-known ICE techniques. Evaluation of the experimental results demonstrates that the proposed approach highlights the important details of the image taking a lower computational execution time.
Remote Sensing Object Detection Based on Strong Feature Extraction and Prescreening Network

Remote sensing object detection has been an important and challenging research hot spot in computer vision that is widely used in military and civilian fields. Recently, the combined detection model of convolutional neural network (CNN) and transformer has achieved good results, but the problem of poor detection performance of small objects still needs to be solved urgently. This letter proposes a deformable end-to-end object detection with transformers (DETR)-based framework for object detection in remote sensing images. First, multiscale split attention (MSSA) is designed to extract more detailed feature information by grouping. Next, we propose multiscale deformable prescreening attention (MSDPA) mechanism in decoding layer, which achieves the purpose of prescreening, so that the encoder–decoder structure can obtain attention map more efficiently. Finally, the A–D loss function is applied to the prediction layer, increasing the attention of small objects and optimizing the intersection over union (IOU) function. We conduct extensive experiments on the DOTA v1.5 dataset and the HRRSD dataset, which show that the reconstructed detection model is more suitable for remote sensing objects, especially for small objects. The average detection accuracy in DOTA dataset has improved by 4.4% (up to 75.6%), especially the accuracy of small objects has raised by 5%.
Meta-Probability Weighting for Improving Reliability of DNNs to Label Noise

Training noise-robust deep neural networks (DNNs) in label noise scenario is a crucial task. In this paper, we first demonstrates that the DNNs learning with label noise exhibits over-fitting issue on noisy labels because of the DNNs is too confidence in its learning capacity. More significantly, however, it also potentially suffers from under-learning on samples with clean labels. DNNs essentially should pay more attention on the clean samples rather than the noisy samples. Inspired by the sample-weighting strategy, we propose a meta-probability weighting (MPW) algorithm which weights the output probability of DNNs to prevent DNNs from over-fitting to label noise and alleviate the under-learning issue on the clean sample. MPW conducts an approximation optimization to adaptively learn the probability weights from data under the supervision of a small clean dataset, and achieves iterative optimization between probability weights and network parameters via meta-learning paradigm. The ablation studies substantiate the effectiveness of MPW to prevent the deep neural networks from overfitting to label noise and improve the learning capacity on clean samples. Furthermore, MPW achieves competitive performance with other state-of-the-art methods on both synthetic and real-world noises.
Augmented Multi-Scale Spatiotemporal Inconsistency Magnifier for Generalized DeepFake Detection

Recently, realistic DeepFake videos have raised severe security concerns in society. Existing video-based detection methods observe local spatial regions with the coarse temporal view, thus it is difficult to obtain subtle spatiotemporal information, resulting in limited generalization ability. In this paper, we propose a novel Augmented Multi-scale Spatiotemporal Inconsistency Magnifier (AMSIM) with a Global Inconsistency View (GIV) and a more meticulous Multi-timescale Local Inconsistency View (MLIV), focusing on mining comprehensive and more subtle spatiotemporal cues. Firstly, the GIV that includs the global spatial and long-term temporal views is established to ensure comprehensive spatiotemporal clues are captured. Then, the MLIV with the critical local spatial and multi-timescale local temporal views is designed for magnifying the indetectable spatiotemporal abnormality. Subsequently, GIV is utilized to guide MLIV to dynamically find local spatiotemporal anomalies that are highly relevant to the overall video. Finally, to further obtain a generalized framework, the adversarial data augmentation is specially designed to expand source domains and simulate unseen forgery domains. Extensive experiments on six large-scale datasets show that our AMSIM outperforms state-of-the-art detection methods and remains effective when applied to unseen forgery techniques and datasets.
A Spatial Hierarchical Reasoning Network for Remote Sensing Visual Question Answering

For visual question answering on remote sensing (RSVQA), current methods scarcely consider geospatial objects typically with large-scale differences and positional sensitive properties. Besides, modeling and reasoning the relationships between entities have rarely been explored, which leads to one-sided and inaccurate answer predictions. In this article, a novel method called spatial hierarchical reasoning network (SHRNet) is proposed, which endows a remote sensing (RS) visual question answering (VQA) system with enhanced visual–spatial reasoning capability. Specifically, a hash-based spatial multiscale visual representation module is first designed to encode multiscale visual features embedded with spatial positional information. Then, spatial hierarchical reasoning is conducted to learn the high-order inner group object relations across multiple scales under the guidance of linguistic cues. Finally, a visual-question (VQ) interaction module is employed to learn an effective image–text joint embedding for the final answer predicting. Experimental results on three public RS VQA datasets confirm the effectiveness and superiority of our model SHRNet.
DLT architectures for trust anchors in 6G

Abstract This manuscript investigates viable Distributed Ledger Technology (DLT) architecture approaches to be used as basis for the distribution of integrity verification data. We discuss what can be a Trust Anchor and how the property of trust can be enabled as a service for mobile communications infrastructures. This follows up on a preceding publication, in the course of which a service was developed that can be utilized to create trust and traceability in transactions between other services. Crucial for the integrity of such an audit trail is proof for which side was committing, in case a tampering was detected. For such verification in the aftermath, mechanisms for the distribution of meta data are necessary. Where our ultimate goal is to develop a versatile framework for Trust as a Service (TaaS), the work at hand contributes the investigation on header distribution. We put a major focus on providing Trust as a Service (TaaS) especially in the mobile communications domain since a reliable concept for trustworthiness is indispensable for the vision of organic infrastructures beyond 5G, which means that such networks are flexible regarding their composition and open for stakeholders.
Guidance-Aided Triple-Adaptive Frost Filter for Speckle Suppression in the Synthetic Aperture Radar Image

Speckle noise exists inherently in the synthetic aperture radar (SAR) image. Its multiplicative property leads to lots of difficulties in SAR image processing. A novel guidance-aided triple-adaptive Frost filter is proposed in this paper, which has potential for real-time processing platforms. Firstly, a scale-adaptive sliding window sizing method is adopted to determine the neighborhood ranges for every point in the image. All the subsequent processing is based on it. Then, an adaptive calculation for the tuning factor in the Frost filter is embedded into the proposed method. Lastly, the feature information apertured from the original image is used to provide guidance for edge recovery automatically, which guarantees the satisfactory ability for feature preservation. Thus, a novel improved Frost filter is proposed with triple adaptabilities. Both the positioning accuracy and response sensitivity of the scale-adaptive sliding window sizing method are verified first. The superiority of the adaptive tuning factor combined with the scale-adaptive sliding window is confirmed by two comparison experiments. At last, the results of speckle suppression experiments on the synthetic images and two natural airborne SAR images present a better performance than other methods.
FESNet: Frequency-Enhanced Saliency Detection Network for Grain Pest Segmentation

As insect infestation is the leading factor accounting for nutritive and economic losses in stored grains, it is important to detect the presence and number of insects for the sake of taking proper control measures. Inspired by the human visual attention mechanism, we propose a U-net-like frequency-enhanced saliency (FESNet) detection model, resulting in the pixelwise segmentation of grain pests. The frequency clues, as well as the spatial information, are leveraged to enhance the detection performance of small insects from the cluttered grain background. Firstly, we collect a dedicated dataset, GrainPest, with pixel-level annotation after analyzing the image attributes of the existing salient object detection datasets. Secondly, we design a FESNet with the discrete wavelet transformation (DWT) and the discrete cosine transformation (DCT), both involved in the traditional convolution layers. As current salient object detection models will reduce the spatial information with pooling operations in the sequence of encoding stages, a special branch of the discrete wavelet transformation (DWT) is connected to the higher stages to capture accurate spatial information for saliency detection. Then, we introduce the discrete cosine transform (DCT) into the backbone bottlenecks to enhance the channel attention with low-frequency information. Moreover, we also propose a new receptive field block (NRFB) to enlarge the receptive fields by aggregating three atrous convolution features. Finally, in the phase of decoding, we use the high-frequency information and aggregated features together to restore the saliency map. Extensive experiments and ablation studies on our dataset, GrainPest, and open dataset, Salient Objects in Clutter (SOC), demonstrate that the proposed model performs favorably against the state-of-the-art model.
Metallic lead (Pb) nanospheres discovered in Hadean and Eoarchean zircon crystals at Jack Hills

Here, we report small randomly-distributed crystalline lead (Pb) nanospheres occurring in detrital zircon grains obtained from a weakly metamorphosed Archean conglomerate at Jack Hills, Western Australia, making this the third known global example of this phenomenon. They form in zircon crystals ranging from Hadean (> 4 billion years-Ga) to Eoarchean (> 3.6 Ga) in age, but are absent from Paleoarchean (~ 3.4 Ga) crystals. Unlike previous discoveries of nanospheres in zircon from Precambrian gneisses in Antarctica and India, detrital zircon from Jack Hills shows no evidence of ever undergoing ultra-high temperature (UHT) metamorphism, either before or after deposition, therefore implying that nanospheres can form at temperatures lower than ca. 900 °C. The nanospheres are composed of radiogenic Pb released by the breakdown of uranium (U) and thorium (Th) and are present in zircon irrespective of its U, Th and water contents, its oxygen isotopic composition, and the degree of discordance due to Pb loss or gain. The nanospheres pre-date annealed cracks in the crystals, showing that, once formed, they effectively 'freeze' radiogenic Pb in the zircon structure, precluding any further interaction during subsequent geological processes. Both Pb nanoclusters and nanospheres are now reported from Jack Hills, and it appears likely the former is a precursor stage in the formation of the latter. Although the precise mechanism for this transition remains unresolved, a later thermal event is required, but this likely did not reach UHT conditions at Jack Hills.
Interactivity Recognition Graph Neural Network (IR-GNN) Model for Improving Human–Object Interaction Detection

Human–object interaction (HOI) detection is important for promoting the development of many fields such as human–computer interactions, service robotics, and video security surveillance. A high percentage of human–object pairs with invalid interactions are discovered in the object detection phase of conventional human–object interaction detection algorithms, resulting in inaccurate interaction detection. To recognize invalid human–object interaction pairs, this paper proposes a model structure, the interactivity recognition graph neural network (IR-GNN) model, which can directly infer the probability of human–object interactions from a graph model architecture. The model consists of three modules: The first one is the human posture feature module, which uses key points of the human body to construct relative spatial pose features and further facilitates the discrimination of human–object interactivity through human pose information. Second, a human–object interactivity graph module is proposed. The spatial relationship of human–object distance is used as the initialization weight of edges, and the graph is updated by combining the message passing of attention mechanism so that edges with interacting node pairs obtain higher weights. Thirdly, the classification module is proposed; by finally using a fully connected neural network, the interactivity of human–object pairs is binarily classified. These three modules work in collaboration to enable the effective inference of interactive possibilities. On the datasets HICO-DET and V-COCO, comparative and ablation experiments are carried out. It has been proved that our technology can improve the detection of human–object interactions.
CNN Model with Multilayer ASPP and Two-Step Cross-Stage for Semantic Segmentation

Currently, interest in deep learning-based semantic segmentation is increasing in various fields such as the medical field, automatic operation, and object division. For example, UNet, a deep learning network with an encoder–decoder structure, is used for image segmentation in the biomedical area, and an attempt to segment various objects is made using ASPP such as Deeplab. A recent study improves the accuracy of object segmentation through structures that extend in various receptive fields. Semantic segmentation has evolved to divide objects of various sizes more accurately and in detail, and various methods have been presented for this. In this paper, we propose a model structure that reduces the overall parameters of the deep learning model in this development and improves accuracy. The proposed model is an encoder–decoder structure, and an encoder half scale provides a feature map with few encoder parameters. A decoder integrates feature maps of various scales with high area details and forward features of low areas. An integrated feature map learns a feature map of each encoder hierarchy over an area of previous data in the form of a continuous coupling structure. To verify the performance of the model, we learned and compared the KITTI-360 dataset with the Cityscapes dataset, and experimentally confirmed that the proposed method was superior to the existing model.
Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach

In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.