Reviewerly/exHarmony huggingface dataset:
Digital Evidence Management System for Cybercrime Investigation using Proxy Re-Encryption and Blockchain

One of the most beneficial uses of the Internet of Things in cloud is data sharing. As tempting since this technology is, data security is still one of the issues it faces, as inappropriate data usage may lead to a range of issues. We offer a proxy re-encryption strategy to securely exchange data in cloud contexts in this paper. Data owners can utilize identity-based encryption to send encrypted data to the cloud, and legitimate users can access the data via proxy re-encryption. Because IoT devices have limited resources, an edge device functions as a proxy server to do complex calculations. We also make effective use of information-centric networking capabilities to supply cached data in the proxy, resulting in better service quality and more network capacity. Our system also uses block chain, a revolutionary technology that allows for decentralized data sharing. It improves centralized system efficiency and enables fine-grained data access control. The security analysis and assessment of our system show that it has the potential to offer privacy protection, authenticity, and dependability.
Visual Data Security Incorporating Fibonacci Sequence, S-Box, and Chaos Theory

Continuous innovations in the field of multimedia and its processing, combined with unprecedented transmission speeds over wireless channels and the Internet have resulted in a rather urgent need for information security. This work proposes a novel image encryption algorithm that is composed of 3 phases. In the first phase, the Fibonacci sequence is utilized. The second phase makes use of a well-tested S-box. Finally, the third phases employs a chaotic function that is based on the Tan and Bessel functions. Performance evaluation of the proposed algorithm is gauged through common image encryption metrics found in the literature. The results exhibit a superior encryption performance, including an MSE value of 8919, a PSNR value of 8.63 dB and an entropy value of 7.999, for the Lena image. Furthermore, the proposed algorithm is tested against the NIST suite and is shown to pass all the tests. Computed values of the various metrics are also compared against counterpart algorithms from the literature and the proposed algorithm is either exhibiting a superior or a comparable performance to them.
Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.
An Improved Model Predictive Control-Based Trajectory Planning Method for Automated Driving Vehicles Under Uncertainty Environments

For automated driving vehicles, trajectory planning is responsible for obtaining feasible trajectories with velocity profiles according to driving environments. From the perspective of trajectory planning, multiple uncertainties of environments and tracking deviations are two significant factors affecting driving safety. The former disturbs the judgment of trajectory planning on the environments, and the latter reduces the tracking accuracy of planned trajectories. To solve these problems, an improved model predictive control (MPC) trajectory planning method is proposed in this paper. Firstly, a Kalman filter fusion method is carried out to predict obstacle trajectory and their uncertainty, which combines model-based and data-based prediction methods. Based on the prediction results, a tube-based MPC trajectory planning method is applied to plan a reference trajectory with a small tracking deviation. The tube-based MPC is composed of two parts. One is the MPC with tightened constraints that is used to plan a feasible trajectory according to a nominal vehicle system and driving environment. The other is a state feedback control that is proposed to adjust the above planned trajectory to reduce the tracking deviations. To our knowledge, this paper proposes Kalman filter fusion and tube-based MPC planning method for the first time to consider the uncertainties of trajectory prediction and tracking control meanwhile in the planning. The planning method is verified by simulations and experiments in multiple scenes. Results show that the method is suitable for both static and dynamic scenes. Compared with applying the basic prediction method, the lateral deviation of the proposed method from the ideal trajectory is decreased by 46.5%. Compared with the nominal MPC method, the lateral tracking deviations of the proposed method are decreased by 77.42%.
KCPNet: Knowledge-Driven Context Perception Networks for Ship Detection in Infrared Imagery

Ship detection plays a crucial role in a variety of military and civilian marine inspection applications. Infrared images are irreplaceable data sources for ship detection due to their strong adaptability and excellent all-weather reconnaissance ability. However, previous researches mainly focus on visible light or synthetic aperture radar (SAR) ship detection, while infrared ship detection is left in a huge blind spot. The main obstacles to this dilemma lie in the absence of public datasets, small scale, and poor semantic information of infrared ships, and severe clutter in complex ocean environments. To address the above challenges, we propose a knowledge-driven context perception network (KCPNet) and construct a public dataset called infrared ship detection dataset (ISDD). In KCPNet, aiming at the small scale of infrared ships, a balanced feature fusion network (BFF-Net) is proposed to balance information from all backbone layers and generate nonlocal features with balanced receptive fields. Moreover, considering the key role of contextual information, a contextual attention network (CA-Net) is designed to improve robustness in complex scenes by enhancing target and contextual information and suppressing clutter. Inspired by prior knowledge of human cognitive processes, we construct a novel knowledge-driven prediction head to autonomously learn visual features and back-propagate the knowledge throughout the whole network, which can efficiently reduce false alarms. Extensive experiments demonstrate that the proposed KCPNet achieves state-of-the-art performance on ISDD. Source codes and ISDD are accessible at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yaqihan-9898</uri> .
Graph Attention Transformer Network for Multi-label Image Classification

Multi-label classification aims to recognize multiple objects or attributes from images. The key to solving this issue relies on effectively characterizing the inter-label correlations or dependencies, which bring the prevailing graph neural network. However, current methods often use the co-occurrence probability of labels based on the training set as the adjacency matrix to model this correlation, which is greatly limited by the dataset and affects the model’s generalization ability. This article proposes a Graph Attention Transformer Network, a general framework for multi-label image classification by mining rich and effective label correlation. First, we use the cosine similarity value of the pre-trained label word embedding as the initial correlation matrix, which can represent richer semantic information than the co-occurrence one. Subsequently, we propose the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain. Our extensive experiments have demonstrated that our proposed methods can achieve highly competitive performance on three datasets.
Multi-channel network: Constructing efficient GCN baselines for skeleton-based action recognition

Skeleton-based action sequences are widely used for human behaviour understanding due to their compact characteristics. Most existing work designed Graph Convolutional Networks and integrated multiple input channels rather than the original motion sequence to improve the final performance. However, few of them have been reported on the detailed effects of such multiple input channels. In contrast to them, we systemically study the impact of different input channels and construct a more efficient GCN framework. We have identified the complementary effect between the local frame channel and global sequence channel, which is essential to improve the action recognition accuracy. By coupling local frame and global sequence information with a classical spatial–temporal graph neural network, e.g. MS-G3D, it achieves competitive performance compared with SOTA methods on challenging benchmark datasets. Related code would be available at https://github.com/movearbitrarily/multi-stream.
A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks

Randomized-based Feedforward Neural Networks approach regression and classification (binary and multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden layer space (randomly generated in its neural network version) for models without direct links and the patterns projected in the hidden layer space along with the original input data for models with direct links. The targets are encoded for the multi-class classification problem according to the 1-of-J encoding (J the number of classes), which implies that the model parameters are estimated to project all the patterns belonging to its corresponding class to one and the remaining to zero. This approach has several drawbacks, which motivated us to propose an alternative optimization model for the framework. In the proposed optimization model, model parameters are estimated for each class so that their patterns are projected to a reference point (also optimized during the process), whereas the remaining patterns (not belonging to that class) are projected as far away as possible from the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four models are then presented: the neural network version of the algorithm and its corresponding kernel version for the neural networks models with and without direct links. In addition, the optimization model has also been implemented in randomization-based multi-layer or deep neural networks. The empirical results obtained by the proposed models were compared to those reported by state-of-the-art models in the correct classification rate and a separability index (which measures the degree of separability in projection terms per class of the patterns belonging to the class of the others). The proposed methods show very competitive performance in the separability index and prediction accuracy compared to the neural networks version of the comparison methods (with and without direct links). Remarkably, the model provides significantly superior performance in deep models with direct links compared to its deep model counterpart.
A Comprehensive Review on Speaker Recognition

Speech is the most natural mode of human communication. In addition to the exchange of thoughts and ideas, speech is considered to be useful for extracting a lot of other information like language identity, gender, age, emotion, and cognitive behavior. It is also known to contain speaker identity information besides other useful information. The task of recognizing the identity of an individual from the para-linguistic cues present in his or her speech signal is known as speaker recognition. It finds numerous applications across different fields like biometrics, forensics, and access control systems. Research in this field has been carried out over several decades, focusing on various aspects like features, modeling techniques, and scoring. The significant advancements in the field of machine learning and deep learning in recent times have developed renewed interest among researchers in this area. This chapter presents a comprehensive literature review on speaker recognition with emphasis on the text-dependent case wherein a predefined text is used for authentication purpose. It discusses the feature extraction and modeling techniques from the earliest to the newest. It also surveys the different deep learning architectures that have resulted in state-of-the-art systems, having received impetus from the availability of increased data and high computational power.
A Networked Method for Multi-Evidence-Based Information Fusion

Dempster-Shafer evidence theory is an effective way to solve multi-sensor data fusion problems. After developing many improved combination rules, Dempster-Shafer evidence theory can also yield excellent results when fusing highly conflicting evidence. However, these approaches still have deficiencies if the conflicting evidence is due to sensor malfunction. This work presents a combination method by integrating information interaction graph and Dempster-Shafer evidence theory; thus, the multiple evidence fusion process is expressed as a network. In particular, the credibility of each piece of evidence is obtained by measuring the distance between the evidence first. After that, the credibility of the evidence is evaluated, keeping the unreliable evidence out of the information interaction network. With the fusion of connected evidence, the accuracy of the fusion result is improved. Finally, application results show that the presented method is effective.
Optical asymmetric JTC cryptosystem based on multiplication-division operation and RSA algorithm

The joint transform correlator (JTC) cryptosystem is a simple and practical optical cryptosystem. However, the anti-attack capability of JTC cryptosystem is still needed to be improved because of its linearity, and the identical key mask in both encryption and decryption also brings inconvenience and security risks in the key distribution and management. Aiming at overcoming these drawbacks, a novel optical asymmetric JTC cryptosystem is proposed in this paper. To design optical asymmetric JTC cryptosystem, a simple but effective trapdoor one-way function based on the image multiplication and division (IMD) operation is created for the first time to author's knowledge. To improve the distribution and management of key mask in JTC cryptosystem, RSA algorithm is utilized to protect the structural parameters and geometric size of the structured spiral phase mask (SSPM), so the security of JTC cryptosystem can be enhanced simultaneously. The merits of our proposed optical asymmetric JTC cryptosystem, whose keys include a RPM public key, the RSA public key, the IMD private key and the RSA private key, are as follows: (1) the IMD operation is low in complexity and easy to be carried out with the aid of a time-varying noise image after the denoised joint power spectrum is obtained; (2) the keys in our proposal conform more closely to the basic agreement of asymmetric cryptosystem that have great advantages in the key distribution and management. Both simulation analysis and initial experimental results verified the correctness and feasibility of our proposal, which is provided with not only simplicity in configuration but also reliable security performance.
Finding the Optimal Topology of an Approximating Neural Network

A large number of researchers spend a lot of time searching for the most efficient neural network to solve a given problem. The procedure of configuration, training, testing, and comparison for expected performance is applied to each experimental neural network. The configuration parameters—training methods, transfer functions, number of hidden layers, number of neurons, number of epochs, and tolerable error—have multiple possible values. Setting guidelines for appropriate parameter values would shorten the time required to create an efficient neural network, facilitate researchers, and provide a tool to improve the performance of automated neural network search methods. The task considered in this paper is related to the determination of upper bounds for the number of hidden layers and the number of neurons in them for approximating artificial neural networks trained with algorithms using the Jacobi matrix in the error function. The derived formulas for the upper limits of the number of hidden layers and the number of neurons in them are proved theoretically, and the presented experiments confirm their validity. They show that the search for an efficient neural network can focus below certain upper bounds, and above them, it becomes pointless. The formulas provide researchers with a useful auxiliary tool in the search for efficient neural networks with optimal topology. They are applicable to neural networks trained with methods such as Levenberg–Marquardt, Gauss–Newton, Bayesian regularization, scaled conjugate gradient, BFGS quasi-Newton, etc., which use the Jacobi matrix.
Foreign capital flow, institutional quality and human capital development in sub-Saharan Africa

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Development of a distributed MR-IoT method for operations and maintenance of underground pipeline network

The underground pipeline network (UPN) is an essential infrastructure and plays an irreplaceable role in national defense and urban activities. The complexity of structural environment and management makes its operation and maintenance difficult. To solve this problem, a distributed mixed reality (MR) and internet of things (IoT) system is developed through game thinking. Firstly, digital models are created based on design drawings and real-world environments, and then an MR system for the UPN is built by the game engine and the OpenXR platform. Secondly, an IoT cloud platform is built to connect with the MR system based on the API sets and cloud services; the data communication between sensors and MR devices is linked with the Socket method, and the data filtering model is constructed by the Kalman algorithm to realize the information exchange between the field workers and the backend managers. Finally, the National Center for Materials Service Safety at the University of Science and Technology Beijing (NCMS_USTB) is used as the experimental site to test this system, and its underground sewage and rainwater pipeline network are used to simulate the key problems in the operation and maintenance. The effect of the application shows that there is potential technical complementarity between the MR and IoT, and the distributed MR-IoT approach can be used as a new technical reference for the operation and maintenance of the UPN.
Domain-agnostic single-image super-resolution via a meta-transfer neural architecture search

Fueled by the powerful learning ability of deep networks, generalized models have been proposed that use external datasets for single-image super-resolution tasks. However, a model trained only with external data may have difficulty in super-resolving images in a domain that differs from the training data. To solve this drawback, several methods have been proposed for internal learning approaches that learn the weights of the network accordance with the test image. Despite these attempts to adapt to specific images using internal learning, they suffer from poor performance due to lack of flexibility that comes from using a fixed architecture regardless of image domain. We thus propose a novel training process that includes external and internal learning. Our internal learning process finds a suitable network architecture and trains the weights for each unseen test image. The overall training process allows the network to learn obtain knowledge from external data and internal learning in a balanced manner. In blind and non-blind experiments, our proposed method outperforms state-of-the-art super-resolution algorithms in various image domains with different kernels. Our proposed approach obtains impressive results in terms of expressing detailed texture and accurate color in images from various domains.
EASOt-AP: An open-source MATLAB package to estimate the seismic moment, rupture radius, and stress-drop of earthquakes from time-dependent P-wave displacements

We developed a MATLAB package, to rapidly obtain the apparent EArthquake SOurce time function using the Average of near-source P-wave displacement records (EASOt-AP) and to estimate the source parameters of the seismic moment, the rupture radius, and the average static stress drop. The algorithm implemented in this package is based on a rapid and straightforward methodology that is recently developed by Zollo et al. (2021). To this purpose, EASOt-AP models a LPDT curve, which is the average P-wave displacement versus time in the logarithm scale. In this paper, the performance of the EASOt-AP has been shown by evaluating the strong motion data recorded in the near-source range of small magnitude events (2 = Mw ≤ 3) that occurred in the Irpinia region, southern Italy. The source parameters follow a self-similar, constant-stress-drop scaling with a relatively low average stress drop of about 01-0.2 MPa. This Tool is designed for an easy use, and all steps of data processing are performed completely within the numerical environment of MATLAB.