Reviewerly/exHarmony huggingface dataset:
SBAC: Substitution cipher access control based on blockchain for protecting personal data in metaverse

The evolution of the metaverse necessitates inventive solutions that can uphold data security. In this paper, we present a novel approach that seamlessly integrates blockchain and substitution cipher techniques to enhance the security landscape of the metaverse. The proposed approach encompasses the generation of the rule table for substitution encryption and decryption, as well as a methodology for data encryption and decryption utilizing the rule table. Furthermore, we conducted a comprehensive evaluation of the proposed approach's security robustness, especially focusing on its resistance against potential security attacks, including the brute-force attack. Moreover, a series of performance experiments were meticulously conducted to comprehensively gauge the effectiveness and efficiency of the proposed technique. The findings revealed that the proposed approach outperforms asymmetric key algorithms in terms of encryption elapsed time. Additionally, the decryption process of the proposed approach is notably faster than both symmetric and asymmetric key algorithms. The results of security analysis and performance evaluations underscore the viability and efficiency of the proposed approach, positioning it as a promising solution for secure and swift data management within the metaverse environment.
Deep learning aided prostate cancer detection for early diagnosis &amp; treatment using MR with TRUS images

Although difficult, robust and reliable synchronization of multimodal medical pictures has several practical uses. For instance, in MR-TRUS fusing guided prostate treatments, picture registration between the two modalities is essential. However, due to the significant variety in image appearance and correlation, MR-TRUS picture registration remains a challenging issue. In this research, we suggest employing deep convolutional neural networks (CNN) i.e. three dimensional CNN U-NET (3D-Conv-Net) to develop a resemblance measure for MR-TRUS registration. Finally, for the second-order optimal of the taught measure, we apply a composite optimisation method that searches the solution space for an appropriate starting point. We also use a multi-stage process to improve the optimisation metric.
Blockchain assisted node validation approach for security provisioning in WSN environment

Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption &amp; decryption time, delay, energy consumption, delivery ratio and throughput.
GAN-Based Siamese Neuron Network for Modulation Classification Against White-Box Adversarial Attacks

With the extensive application of modulation classification in the field of wireless communication, various methods of modulation classification have been proposed to improve the classification performance, where the deep learning (DL)-based modulation classification technology is gradually popular. However, recent studies have discovered that the DL-based modulation classification methods are vulnerable to white-box adversarial attacks, which can significantly degrade the classification performance by injecting imperceptible perturbations into clean signals. In this paper, to defend against white-box adversarial attack, we propose a generative adversarial network (GAN)-based Siamese neuron network (GSNN) for accomplishing modulation classification, where a generator is trained to generate perturbations that can easily fool the discriminator and the discriminator tries to classify correctly both original and adversarial samples generated by the generator. The discriminator acted by a Siamese network is designed to measure the similarity of sample pair based on Euclidean distance, transferring the classification problem into pairwise comparison problem. After the training of GSNN is completed, we adopt the distance-based NCM classifier to accomplish the classification task. We verify the defense performance of our proposed GSNN on both the public dataset and simulated dataset. Simulation results illustrate that our proposed method is superior to the common SoftMax classifier.
Underwater Visibility Enhancement IoT System in Extreme Environment

Imagery captured in extreme underwater environments often presents unique challenges, including blurred details, color distortion, and reduced contrast. These discrepancies largely emanate from the intricate interplay of light absorption and scattering within the aquatic medium. Predominant restoration techniques, rather simplistically, apply a static attenuation coefficient, neglecting the dynamic nuances of underwater conditions, leading to an inconsistent restoration outcome. To counter these impediments, we introduce an avant-garde Underwater Internet of Things (Underwater IoT) system, underpinned by a scene-depth fusion paradigm. Our methodology astutely accounts for the spectral decay of light underwater to infer a more refined attenuation coefficient tailored to the specific scene. This system, employing a quadtree decomposition for precise localization coupled with depth mapping, facilitates an astute estimation of prevailing luminescence. This depth map, once synthesized and refined, aids in gauging the precise attenuation dynamics of the aqueous milieu, culminating in a more precise transmission map derivation. Segueing from this, we employ an inverse model to refurbish the original image. Experimental results highlight our system’s prowess in counteracting issues like muddied details and chromatic anomalies while concurrently amplifying contrast. In juxtaposition with a spectrum of existing methodologies, our innovation outshines in terms of finesse and accuracy, underscoring its unparalleled efficacy in the challenging underwater conditions.
Binary Sequences With a Low Correlation via Cyclotomic Function Fields of Odd Characteristic

Sequences with a low correlation have very important applications in communications, cryptography, and compressed sensing. In the literature, many efforts have been made to construct good sequences with various lengths, where binary sequences attract great attention. As a result, various constructions of good binary sequences have been proposed. However, most of the known constructions made use of the multiplicative cyclic group structure of finite field F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>p<sup>n</sup></i></sub> for a prime <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> and a positive integer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> . In fact, all <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p<sup>n</sup></i> + 1 rational places including the place at infinity of the rational function field over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>p<sup>n</sup></i></sub> can form a cyclic structure under an automorphism of order <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p<sup>n</sup></i> + 1. In this paper, we make use of this cyclic structure to provide an explicit construction of binary sequences with a low correlation of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p<sup>n</sup></i> + 1 via cyclotomic function fields over Fpn for any odd prime <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> . Each family of binary sequences has size <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p<sup>n</sup></i> -2 and its correlation is upper bounded by 4+⎣2 · <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p<sup>n/2</sup></i> ⎦. To the best of our knowledge, this is the first construction of binary sequences with a low correlation of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p<sup>n</sup></i> +1 for odd prime <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> . Moreover, our sequences can be constructed explicitly and have competitive parameters.
Event-based diffractive neural network chip for dynamic action recognition

Dynamic action recognition has promising applications in human–computer interaction, information encryption, and high-speed image processing. However, it is challenging for a high-dynamic scene due to the huge amount of data and the computation costs. Here, we have proposed an event-based diffractive neural network (EBDNN) chip to recognize human dynamic actions. An event camera with high dynamic range and high temporal resolution is used for dynamic action capture. Based on the approach of compressing dimensions, a series of static images can be obtained by superimposing the event frames of the intervals, which serve as the train dataset of a diffractive neural network with photon wave properties. Compared to a frame camera with the same temporal resolution, the required data volume is only 0.0048% of that, which greatly shortens the training time of the diffractive neural network on the computer and saves computing resources. Notably, the diffractive neural network was trained to perform image classification tasks at the speed of light with almost no energy consumption, and the diffractive devices, which includes 400 × 400 neurons, is printed based on the two-photon polymerization technology. In addition, the experimental accuracy was as high as 99.17%. Our work opens up a new direction for photoelectrically integrated intelligent device, paving the way for the development of real-time monitoring systems and other machine vision tasks, such as autonomous driving and video surveillance.
3D Skeleton-Based Human Motion Prediction Using Dynamic Multi-Scale Spatiotemporal Graph Recurrent Neural Networks

A dynamic multi-scale spatiotemporal graph recurrent neural network (DMST-GRNN) model has been introduced, which is leveraged to use human motion prediction on a 3D skeleton-based human activity dataset. It offers a multi-scale approach to spatial & temporal graphs using multi-scale graph convolution units (MGCUs) to describe the human body's semantic interconnection. The proposed DMST-GRNN is an encoder-decoder framework where a series of MGCUs are used as encoders to learn spatiotemporal features and a novel graph-gated recurrent unit lite (GGRU-L) for the decoder to predict human pose. Extensive experiments have been carried out with two datasets, Human3.6M and CMU Mocap, where both short and long videos were considered to validate the performance of the proposed model. The DMST-GRNN model outperforms the existing baseline on the Human3.6M datasets by 11.95% and 7.74% of average mean angle errors (avg MAE) for short and long-term motion prediction, respectively. Similarly, CMU Mocap datasets, the DMST-GRNN model predicts future posture more accurately than the previous best approaches by 2.77% and 5.51% of average mean angle errors (avg MAE) for short and long-term motion prediction, respectively. A comparison analysis was also presented with other measures like mean angle error, prediction loss and standard deviation. A separate discussion has been included to analyze the effect of different multiscale on spatial and temporal graphs, along with the impact of MGCU unit counts.
Shift-Sum Decoding of Non-Binary Cyclic Codes

This paper proposes a novel shift-sum decoding method for non-binary cyclic codes, which only requires finite field operations but yields advanced decoding performance. Using the cyclically different minimum-weight dual codewords (MWDCs) and their proper shifts, a frequency matrix can be obtained as a reliability metric for identifying the error positions and magnitudes. By analyzing the statistical distributions of the matrix entries, the rationale for the shift-sum decoding’s advanced error-correction capability is revealed. Based on this decoding method, a hard-decision iterative shift-sum (HISS) decoding algorithm is first proposed. It can correct errors beyond half of the code’s minimum Hamming distance. By further utilizing the reliability information obtained from the channel, a soft-decision iterative shift-sum (SISS) decoding algorithm is then proposed to improve the decoding performance. Both the HISS and the SISS algorithms are realized only with polynomial multiplications and numerical comparisons, which are hardware-friendly. To further improve the error-correction performance, the HISS and SISS algorithms can be integrated in a Chase decoding mechanism for handling the test-vectors. Simulation results on Reed-Solomon (RS) and non-binary BCH (NB-BCH) codes show that the proposed algorithms yield a competent decoding and complexity performances in comparison with the existing decoding algorithms.
Mutual Information Guided Financial Report Generation With Domain Adaption

The conditional long text generation is an important yet challenging task especially for some domain-specific applications such as financial report generation. Generally, this emerging financial report generation is to generate long and informative text provided by a piece of short news, where the domain knowledge obviously plays an important role. Apparently, such domain knowledge dynamically varies but could be learned from reports written by human specialists to address multiple viewpoints contained in the original news. To address this issue, this article proposes the mutual information guided financial report generation approach with domain adaption. Particularly, we first extract the underlying domain spaces from the input news as well as their corresponding reports, respectively. Then, these domains are aligned with each other in a mutual manner via the proposed domain adaption component. To further enhance the model performance, the mutual information loss is designed to guarantee the semantic meaning of the generated report is close to that of the input news. Extensive experiments are conducted on three public datasets and the proposed approach achieves the state-of-the-art model performance.
Quantum protection scheme for privacy data based on trusted center

At present, the protection of data privacy has become one of the research hotspots. However, the classic data privacy protection scheme is based on the classic cryptosystem. The classical cryptosystem is based on the computational complexity of mathematical intractable problems. With the improvement of computer computing power, the security of the classical cryptosystem is seriously threatened. Based on the physical properties of quantum mechanics, using quantum key distribution and quantum teleportation technology, a quantum protection scheme for private data based on trusted center is designed. In theory, this scheme has the unconditional security of quantum cryptography and is independent of the computing power of the attacker.
Semi-supervised class-conditional image synthesis with Semantics-guided Adaptive Feature Transforms

Generative Adversarial Networks (GANs) have become the mainstream models for class-conditional synthesis of high-fidelity images. To reduce the demand for labeled data, we propose a class-conditional GAN with Semantic-guided Adaptive Feature Transforms, which is referred to as SAFT-GAN for semi-supervised image synthesis. Instead of simply incorporating a classifier to infer the class labels of unlabeled data, the key idea behind SAFT-GAN is to incorporate class-semantic guidance in real-fake discrimination. More specifically, we adopt a two-head architecture for a discriminator: A label-embedded head identifies real and fake instances, conditioned on class label. To focus more on class-related regions, we exploit class-aware attention information to regularize this head via regional feature transforms. On the other hand, to make better use of unlabeled data, we design a label-free head, on which channel-adaptive feature transforms are imposed to fuse the discriminator and classifier features, such that the class semantics of synthesized images can be improved. Extensive experiments are performed to demonstrate how class-conditional image synthesis can benefit from the proposed feature transforms, and also demonstrate the superiority of SAFT-GAN.
Adversarial Deep Learning based Dampster–Shafer data fusion model for intelligent transportation system

Intelligent Transportation Systems (ITS) have revolutionized transportation by incorporating advanced technologies for efficient and safe mobility. However, these systems face challenges ensuring security and resilience against adversarial attacks. This research addresses these challenges and introduces a novel Dampster–Shafer data fusion-based Adversarial Deep Learning (DS-ADL) Model for ITS in fog cloud environments. Our proposed model focuses on three levels of adversarial attacks: original image level, feature level, and decision level. Adversarial examples are generated at each level to evaluate the system's vulnerability comprehensively. To enhance the system's capabilities, we leverage the power of several vital components. Firstly, we employ Dempster–Shafer-based Multimodal Sensor Fusion, enabling the fusion of information from multiple sensors for improved scene understanding. This fusion approach enhances the system's perception and decision-making abilities. For feature extraction and classification, we utilize ResNet 101, a deep learning architecture known for its effectiveness in computer vision tasks. We introduced a novel Monomodal Multidimensional Gaussian Model (MMGM-DD) based Adversarial Detection approach to detect adversarial examples. This detection mechanism enhances the system's ability to identify and mitigate adversarial attacks in real-time. Additionally, we incorporate the Defensive Distillation method for adversarial training, which trains the model to be robust against attacks by exposing it to adversarial examples during the training process. To evaluate the performance of our proposed model, we utilize two datasets: Google Speech Command version 0.01 and the German Traffic Sign Recognition Benchmark (GTSRB). Evaluation metrics include latency delay and computation time (fog–cloud), accuracy, MSE, loss, and F-score for attack detection and defense. The results and discussions demonstrate the effectiveness of our Dampster–Shafer data fusion-based Adversarial Deep Learning Model in enhancing the robustness and security of ITS in fog–cloud environments. The model's ability to detect and defend against adversarial attacks while maintaining low-latency fog–cloud operations highlights its potential for real-world deployment in ITS.
Coarse-to-fine online latent representations matching for one-stage domain adaptive semantic segmentation

Domain adaptive semantic segmentation is meaningful since collecting numerous labeled samples in different domains is expensive and time-consuming. Recent domain adaptation methods yield not so efficient performance compared with supervised learning. With the hypothesis that semantic feature can be shared across domains, this paper proposes a coarse-to-fine online matching architecture (COM) for one-stage domain adaptation. We consider subsequent learning stages progressively refining the task in the latent feature space, i.e. the finer set at each component is hierarchically derived from the coarser set of the previous components, including cross-domain global prototypes, categories and instances matching and anchor-points contrastive learning, which further achieve self-supervised learning with region-level pseudo label generated only in a single training step. Beforehand, feature refinement are performed to realize edge perception and inter-feature augmentation. Then, coarse-to-fine network fuses global and local consistency matching via specific distribution alignment between the source and target domain. Finally, the adversarial structure controls the uncertainty of generator prediction through the maximization of classification results and minimization of two classifiers discrepancy. This proposed method is evaluated in two unsupervised domain adaptation tasks, i.e. GTA5 → Cityscapes and SYNTHIA → Cityscapes. Extensive experiments verify the effectiveness of our proposed COM model and demonstrate its superiority over several state-of-the-art approaches.
Parallel disentangling network for human–object interaction detection

Human–object interaction (HOI) detection aims to localize and classify triplets of human, object and interaction from a given image. Earlier two-stage methods suffer both from mutually independent training processes and the interference of redundant negative human–object pairs. Prevailing one-stage transformer-based methods are free from the above problems by tackling HOI in an end-to-end manner. However, one-stage transformer-based methods carry the unnecessary entanglements of the query for different tasks, i.e., human–object detection and interaction classification, and thus bring in poor performance. In this paper, we propose a new transformer-based approach that parallelly disentangles human–object detection and interaction classification in a triplet-wise manner. To make each query focus on one specific task clearly, we exhaustively disentangle HOI by parallelly expanding the naive query in vanilla transformer as triple explicit queries. Then, we introduce a semantic communication layer to preserve the consistent semantic association of each HOI through mixing the feature representations of each query triplet of the correspondence constraint. Extensive experiments demonstrate that our proposed framework outperforms the existing methods and achieves the state-of-the-art performance, with significant reduction in parameters and FLOPs.