Reviewerly/exHarmony huggingface dataset:
End-to-end residual attention mechanism for cataractous retinal image dehazing

Cataract is one of the most common causes of vision loss. Light scattering due to clouding of the lens in cataract patients makes it extremely difficult to image the retina of cataract patients with fundus cameras, resulting in a serious decrease in the quality of the retinal images taken. Furthermore, the age of cataract patients is generally too old, in addition to cataracts, the patients often have other retinal diseases, which brings great challenges to experts in the clinical diagnosis of cataract patients using retinal imaging.In this paper, we present the End-to-End Residual Attention Mechanism (ERAN) for Cataractous Retinal Image Dehazing, which it includes four modules: encoding module, multi-scale feature extraction module, feature fusion module, and decoding module. The encoding module encodes the input cataract haze image into an image, facilitating subsequent feature extraction and reducing memory usage. The multi-scale feature extraction module includes a hole convolution module, a residual block, and an adaptive skip connection, which can expand the receptive field and extract features of different scales through weighted screening for fusion. The feature fusion module uses adaptive skip connections to enhance the network's ability to extract haze density images to make haze removal more thorough. Furthermore, the decoding module performs non-linear mapping on the fused features to obtain the haze density image, and then restores the haze-free image.The experimental results show that the proposed method has achieved better objective and subjective evaluation results, and has a better dehazing effect.We proposed ERAN method not only provides visually better images, but also helps experts better diagnose other retinal diseases in cataract patients, leading to better care and treatment.
Application of VR Technology in Brand IP Image Design

Virtual reality (VR) refers to the computer-generated simulation of a three-dimensional image environment that can be interacted within a seemingly physical way by a person using special electronic equipment and making people interactive and immersive. VR technology is applied to communication activities, producing VR communication. VR technology is applied to news reports, giving birth to the emergence of VR news. The great significance of VR technology for news communication lies in the comprehensive intake and recording and transmission of news site materials so that news events can “reproduce” and “restore.” The most prominent feature of VR transmission is immersion. Immersivity is mainly reflected in the perceptual system and the behavioral system of the human body. The immersion of perception system includes visual immersion, auditory immersion, touch immersion, and other sensory immersion; behavioral system immersion includes the immersion of language system, direction system immersion, and expression system immersion. Immersive communication has experienced three development stages: interactive communication, invasive communication, infiltration, and audience experience. There are three hidden dangers in VR technology in the communication. One is the hidden danger of the technical operation level: the lack of security and stability of the production process may lead to the disappearance of the traditional reporter, the second is the ethical hidden danger: the degradation of “audience” to “mass,” and third is “body” weakening the authority of “addiction”: the emergence of “brain in the cylinder,” the emergence of snooping feast, and the formation of exposed carnival. There are three aspects to prevent VR hidden dangers, layout and top-level design, improve security, and communication efficiency, promote industrial application, enhance user stickiness, strengthen brand building, and create ace IP and quality content.
A Novel 3D Image Encryption Based on the Chaotic System and RNA Crossover and Mutation

In this paper, a novel 3D image encryption based on the memristive chaotic system and RNA crossover and mutation is proposed. Firstly, the dynamic characteristics of the nonlinear system with two memristors are analyzed, including phase diagrams, Lyapunov exponential spectrums, and bifurcation diagrams. According to the merged image of three 3D images, the initial values of the memristive chaotic system are generated by SHA-256. Then the vertex coordinates are scrambled and diffused by 3D Arnold matrix and chaotic sequences. Finally, according to the dynamical encoding and decoding rules, crossover and RNA mutation are designed to confuse and diffuse the vertex coordinates. Throughout the encryption process, the Arnold matrix, RNA encoding and decoding rules, and crossover and mutation algorithms are determined by the memristive chaotic system. The experimental results verify that the proposed cryptosystem could encrypt three 3D images at the same time and resist various attacks effectively, and has good security performance.
Deep Uncoupled Discrete Hashing via Similarity Matrix Decomposition

Hashing has been drawing increasing attention in the task of large-scale image retrieval owing to its storage and computation efficiency, especially the recent asymmetric deep hashing methods. These approaches treat the query and database in an asymmetric way and can take full advantage of the whole training data. Though it has achieved state-of-the-art performance, asymmetric deep hashing methods still suffer from the large quantization error and efficiency problem on large-scale datasets due to the tight coupling between the query and database. In this article, we propose a novel asymmetric hashing method, called D eep U ncoupled D iscrete H ashing (DUDH), for large-scale approximate nearest neighbor search. Instead of directly preserving the similarity between the query and database, DUDH first exploits a small similarity-transfer image set to transfer the underlying semantic structures from the database to the query and implicitly keep the desired similarity. As a result, the large similarity matrix is decomposed into two relatively small ones and the query is decoupled from the database. Then both database codes and similarity-transfer codes are directly learned during optimization. The quantization error of DUDH only exists in the process of preserving similarity between the query and similarity-transfer set. By uncoupling the query from the database, the training cost of optimizing the CNN model for the query is no longer related to the size of the database. Besides, to further accelerate the training process, we propose to optimize the similarity-transfer codes with a constant-approximation solution. In doing so, the training cost of optimizing similarity-transfer codes can be almost ignored. Extensive experiments on four widely used image retrieval benchmarks demonstrate that DUDH can achieve state-of-the-art retrieval performance with remarkable training cost reduction (30× - 50× relative).
Characterization of out-of-plane tensile stress–strain behavior for GFRP composite materials at elevated temperatures

Three-dimensional stress–strain constitutive parameters in various environments are essential for understanding the mechanical behavior of thick cross-section composite load-bearing structures. This work aims to expand a short-beam bending test combined with a digital image correlation method to obtain the out-of-plane (interlaminar) stress–strain behavior for polymer–matrix composites at elevated temperatures. A simple and repeatable digital speckle pattern fabrication method was used based on the water transfer printing (WTP) technique to reduce deformation measurement uncertainty induced by the quality of the speckle pattern. The effects of DIC parameters and misalignment of the DIC stereo camera system on the uncertainty of deformation measurement have been studied. The out-of-plane properties, including elastic modulus and tensile strength for a 50-ply thick S6C10/AC318 glass/epoxy unidirectional panel, have been obtained at elevated temperatures for the first time. The experimental results showed that the out-of-plane modulus and tensile strength were 15.95 GPa and 76 MPa at room temperature. The modulus was nearly unchanged at 50 °C and decreased 18% at 80 °C. The out-of-plane tensile strength decreased 9% at 50 °C and 22% at 80 °C, respectively, indicating that the tensile strength degradation is more sensitive to temperature rising than the modulus. The study suggests that the improved short-beam bending test combined with the DIC method can guarantee reproducible results and usability at elevated temperatures.
Construction and Evaluation of a High-Quality Corpus for Legal Intelligence Using Semiautomated Approaches

A high-quality corpus is essential for building an effective legal intelligence system. The quality of a corpus includes both the quality of original data and the quality of its corresponding labeling. The major quality dimensions of a legal corpus include comprehensiveness, freshness, and correctness. However, building a comprehensive, correct, and fresh legal corpus is a grand challenge. In this article, we propose a semiautomated machine learning framework to address the challenge. We first created an initial corpus with 4937 instances that were manually labeled. Several strategies were implemented to assure its quality. The initial results showed that class imbalance and insufficiency of training data are the two major quality issues that negatively impacted the quality of the system that was built on the data. We experimented and compared three class-imbalance-handling techniques and found that the mixed-sampling method, which combines upsampling and downsampling, was the most effective way to address the issue. In order to address the insufficiency of training data, we experimented several machine learning methods for automated data augmentation including pseudolabeling, co-training, expectation-maximization, and generative adversarial network (GAN). The results showed that GAN with deep learning models achieved the best performance. Finally, ensemble learning of different classifiers was proposed and experimented with for the construction of a legal corpus, which achieves higher quality in comprehensiveness, freshness, and correctness compared to existing work. The semiautomated machine learning framework and the data quality evaluation method developed in this research can be used for data augmentation and quality evaluation of a large dataset as well as a reference for the selection of machine learning methods for data augmentation and generation. The machine learning models, the training data, and the legal corpus are published and publicly accessible at [Online]. Available: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/haihua0913/legalArgumentmining</uri> .
A Novel Ensemble Bagging Classification Method for Breast Cancer Classification Using Machine Learning Techniques

Breast cancer is observed as a dangerous disease type for women in the world. The clinical experts stated that early detection of cancer helps in saving lives. To detect cancer in the early stage, medical image processing is observed as an effective field. Medical Image processing with an appropriate classification mechanism improves accuracy and image resource with minimal processing time. To detect breast cancer several machine learning techniques are evolved for cancer classification. However, those machine learning techniques are subjected to increased time consumption and limitation in the accuracy of classification. This paper proposed an Ensemble Bagging Weighted Voting Classification (EBWvc) for the classification of breast cancer. Initially, to resolve to overfit in machine learning bagging is applied for collected data. The ensemble bagging classification provides effective training to machine learning for reduced computational time and improved performance characteristics. The weighted voting is adopted for the classification of cancer in the breast. The performance of proposed EBWvc is analyzed comparatively with consideration accuracy, precision, recall, and F1 -Score. The comparative analysis of results exhibited that proposed EBWvc exhibits improved performance than existing classification techniques.
Autonomous Human-Vehicle Leader-Follower Control Using Deep-Learning-Driven Gesture Recognition

Leader-follower autonomy (LFA) systems have so far only focused on vehicles following other vehicles. Though there have been several decades of research into this topic, there has not yet been any work on human-vehicle leader-follower systems in the known literature. We present a system in which an autonomous vehicle—our ACTor 1 platform—can follow a human leader who controls the vehicle through hand-and-body gestures. We successfully developed a modular pipeline that uses artificial intelligence/deep learning to recognize hand-and-body gestures from a user in view of the vehicle’s camera and translate those gestures into physical action by the vehicle. We demonstrate our work using our ACTor 1 platform, a modified Polaris Gem 2. Results show that our modular pipeline design reliably recognizes human body language and translates the body language into LFA commands in real time. This work has numerous applications such as material transport in industrial contexts.
Design and Prototyping of Intelligent Bin by Using AIoT

The problem here we address is solid waste. Pollution directly or indirectly impacts the quality of human life, animals, and the environment. It is the need of the hour that everyone cooperates with each other by reducing sorting and reusing waste as much as possible. This paper proposes to design the classification of the wastes in an intelligent bin by using Artificial Intelligent & Internet of Things (AIoT). The detection and classification are based on deep learning. The 3,000 waste image dataset is used for training divided into three groups. After that, the system controls the servo motor to open the lid of the bin which matches the waste. The accuracy rate shows 90.44%. Moreover, our prototype can detect the amount of waste in the bin to notify the authorities and prevent the problem of overflowing the bin.
Efficient and provably secure multi-receiver signcryption scheme using implicit certificate in edge computing

Edge computing is an emerging computing paradigm, which extends the functions of cloud center to the edge of the networks, and brings great convenience to solving problems of delay and bandwidth in the traditional cloud computing paradigm. In edge computing architecture, one-to-many communication is a important communication mode that supports the edge nodes to send one message to multiple terminal devices in one broadcast report. To build a secure one-to-many communication, several multi-receiver signcryption (MRSC) schemes have been brought forward to ensure the security of broadcast messages. However, the existing MRSC schemes need the heavy computation and communication cost, and is not suitable for IoT terminal devices with limited resources. Besides, most of the existing MRSC schemes are vulnerable to the security and privacy leakage, and fail in achieving the decryption fairness. To solve these problems, based on the implicit certificate (IC) cryptosystem and polynomial interpolation evaluation, this paper proposes an provably secure multi-receiver IC-based signcryption (MRICSC) scheme for one-to-many communication in edge computing. The security analysis evidences the proposed MRICSC scheme can ensure the security. With the experimental results indicating that the proposed MRICSC scheme achieves the better performance than existing schemes.
Model selection for RBF-ARX models

Radial basis function network-based autoregressive with exogenous input (RBF-ARX) models are useful in nonlinear system modelling and prediction. The identification of RBF-ARX models includes optimization of the (model lags, number of hidden nodes and state vector) and the parameters of the model. Previous works have usually ignored optimizations of the model’s architecture. In this paper, the RBF-ARX architecture, which includes the selection of lags, number of nodes of the RBF network, lag orders and state vector, is encoded into a chromosome and is evolved simultaneously by a genetic algorithm (GA). This combines the advantages of the GA and the variable projection (VP) method to automatically generate a parsimonious RBF-ARX model with a high generalization performance. The highly efficient VP algorithm is used as a local search strategy to accelerate the convergence of the optimization. The experimental results demonstrate the effectiveness of the proposed method.
Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network

Neural network creates a neuron-based network similar to the human nervous system to solve classification problems efficiently. The smishing problem is a binary classification problem in which attackers target smartphone users through text messages. As smishing is a remarkable cybersecurity issue that is troubling researchers and smartphone users these days. Addressing this security issue using the most efficient algorithm is the need of the hour. This manuscript presented an algorithm for the model proposed by authors in 'Smishing Detector' model and implemented it using Neural Network. The result obtained proves that the neural network is much efficient in detecting smishing problem. Neural Network outperformed other machine learning algorithms with a difference of 1.11%. Neural Network performed with the final accuracy of 97.40%. In this paper, system extracted the most efficient features of smishing SMS (Short Message Service) using the Neural Network. This manuscript also reported the accuracy shown by the system for each feature selected and implemented. It is evident from the implementation that each feature selected is most effective in smishing detection and URL (Uniform Resource Locator) feature is the most effective feature with an accuracy of 94%.
Search in forest optimizer: a bioinspired metaheuristic algorithm for global optimization problems

The present research proposes a new particle swarm optimization-based metaheuristic algorithm entitled “search in forest optimizer (SIFO)” to solve the global optimization problems. The algorithm is designed based on the organized behavior of search teams looking for missing persons in a forest. According to SIFO optimizer, a number of teams each including several experts in the search field spread out across the forest and gradually move in the same direction by finding clues from the target until they find the missing person. This search structure was designed in a mathematical structure in the form of intragroup search operators and transferring the expert member to the top team. In addition, the efficiency of the algorithm was assessed by comparing the results to the standard representations and a problem with the genetic, grey wolf, salp swarm, and ant lion optimizers. According to the results, the proposed algorithm was efficient for solving many numerical representations, compared to the other algorithms.
Framework for Remote Sensing and Modelling of Lithium-Brine Deposit Formation

The demand for “green” metals such as lithium is increasing as the world works to reduce its reliance on fossil fuels. More than half of the world’s lithium resources are contained in lithium-brine deposits, including the salt flats, or “salars”, of the Andean region of South America, also known as the Lithium Triangle. The genesis of lithium-brine deposits is largely driven by the leaching of lithium from source rocks in watersheds, transport via groundwater systems to salars, and evaporative concentration in salars. The goal of this research is to create a consistent and seamless methodology for tracking lithium mass from its source in the watershed to its greatest concentration in the nucleus. The area of interest is in and around Bolivia’s Salar de Uyuni, the world’s largest salt flat. We explore how Li-brine deposits form, where the water and solute come from, how the brines are formed, and how abstraction affects the mass balance inside the salar. To support the entire system, open-source Earth observation (EO) data are analysed. We found that by constructing a flexible and repeatable workflow, the question of how lithium reaches the Salar de Uyuni can be addressed. The work demonstrated the importance of groundwater flow to the river network and highlighted the need for flow data for the main river supplying the salar with both water inflow and lithium mass.
Adaptive Patch Exiting for Scalable Single Image Super-Resolution

Since the future of computing is heterogeneous, scalability is a crucial problem for single image super-resolution. Recent works try to train one network, which can be deployed on platforms with different capacities. However, they rely on the pixel-wise sparse convolution, which is not hardware-friendly and achieves limited practical speedup. As image can be divided into patches, which have various restoration difficulties, we present a scalable method based on Adaptive Patch Exiting (APE) to achieve more practical speedup. Specifically, we propose to train a regressor to predict the incremental capacity of each layer for the patch. Once the incremental capacity is below the threshold, the patch can exit at the specific layer. Our method can easily adjust the trade-off between performance and efficiency by changing the threshold of incremental capacity. Furthermore, we propose a novel strategy to enable the network training of our method. We conduct extensive experiments across various backbones, datasets and scaling factors to demonstrate the advantages of our method. Code is available at https://github.com/littlepure2333/APE .
A Principled Design of Image Representation: Towards Forensic Tasks

Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society. Like other vision-related applications, forensic analysis relies heavily on the proper image representation. Despite the importance, current theoretical understanding for such representation remains limited, with varying degrees of neglect for its key role. For this gap, we attempt to investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. Our work starts from the abstraction of basic principles that the representation for forensics should satisfy, especially revealing the criticality of robustness, interpretability, and coverage. At the theoretical level, we propose a new representation framework for forensics, called dense invariant representation (DIR), which is characterized by stable description with mathematical guarantees. At the implementation level, the discrete calculation problems of DIR are discussed, and the corresponding accurate and fast solutions are designed with generic nature and constant complexity. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors. Also, at the application level, the proposed DIR is initially explored in passive and active forensics, namely copy-move forgery detection and perceptual hashing, exhibiting the benefits in fulfilling the requirements of such forensic tasks.