Reviewerly/exHarmony huggingface dataset:
Research on plant disease identification based on CNN

Traditional digital image processing methods extract disease features manually, which have low efficiency and low recognition accuracy. To solve this problem, In this paper, we propose a convolutional neural network architecture FL-EfficientNet (Focal loss EfficientNet), which is used for multi-category identification of plant disease images. Firstly, through the Neural Architecture Search technology, the network width, network depth, and image resolution are adaptively adjusted according to a group of composite coefficients, to improve the balance of network dimension and model stability; Secondly, the valuable features in the disease image are extracted by introducing the moving flip bottleneck convolution and attention mechanism; Finally, the Focal loss function is used to replace the traditional Cross-Entropy loss function, to improve the ability of the network model to focus on the samples that are not easy to identify. The experiment uses the public data set new plant diseases dataset (NPDD) and compares it with ResNet50, DenseNet169, and EfficientNet. The experimental results show that the accuracy of FL-EfficientNet in identifying 10 diseases of 5 kinds of crops is 99.72%, which is better than the above comparison network. At the same time, FL-EfficientNet has the fastest convergence speed, and the training time of 15 epochs is 4.7 h.
Probabilistic photovoltaic power forecasting using a calibrated ensemble of model chains

Physical model chain is a step-by-step modeling framework for the conversion of irradiance to photovoltaic (PV) power. When a model chain is fed with irradiance forecasts, it provides the corresponding PV power forecasts. Despite its advantages, forecasting with model chains has yet to receive the attention that it deserves. In several recent works, however, the idea of model-chain-based solar forecasting has been formally modernized, though the framework was restricted to deterministic forecasting. In this work, the model-chain-based forecasting framework is extended to the probability space, in that, a calibrated ensemble of model chains is used to generate probabilistic PV power forecasts. Using two-year data from eight PV plants in Hungary, alongside professional weather forecasts issued by the Hungarian Meteorological Services, it is empirically shown that the raw model-chain ensemble forecasts tend to be underdispered, but adequate post-processing is able to improve calibration and reduce the continuous ranked probability score of raw ensembles by 20%. Given the fact that uncertainty quantification has a cardinal importance to grid integration, this probabilistic extension of the model-chain-based solar forecasting framework is thought beneficial.
Optical Ciphering Scheme for Cancellable Speaker Identification System

Most current security and authentication systems are based on personal biometrics.The security problem is a major issue in the field of biometric systems.This is due to the use in databases of the original biometrics.Then biometrics will forever be lost if these databases are attacked.Protecting privacy is the most important goal of cancelable biometrics.In order to protect privacy, therefore, cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases.One methodology to achieve non-invertibility is the employment of non-invertible transforms.This work suggests an encryption process for cancellable speaker identification using a hybrid encryption system.This system includes the 3D Jigsaw transforms and Fractional Fourier Transform (FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding (DRPE) encryption process.The evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure, authoritative, and feasible.The encryption and cancelability effects are good and reveal good performance.Also, it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.
Attention-based generative adversarial network in medical imaging: A narrative review

As a popular probabilistic generative model, generative adversarial network (GAN) has been successfully used not only in natural image processing, but also in medical image analysis and computer-aided diagnosis. Despite the various advantages, the applications of GAN in medical image analysis face new challenges. The introduction of attention mechanisms, which resemble the human visual system that focuses on the task-related local image area for certain information extraction, has drawn increasing interest. Recently proposed transformer-based architectures that leverage self-attention mechanism encode long-range dependencies and learn representations that are highly expressive. This motivates us to summarize the applications of using transformer-based GAN for medical image analysis. We reviewed recent advances in techniques combining various attention modules with different adversarial training schemes, and their applications in medical segmentation, synthesis and detection. Several recent studies have shown that attention modules can be effectively incorporated into a GAN model in detecting lesion areas and extracting diagnosis-related feature information precisely, thus providing a useful tool for medical image processing and diagnosis. This review indicates that research on the medical imaging analysis of GAN and attention mechanisms is still at an early stage despite the great potential. We highlight the attention-based generative adversarial network is an efficient and promising computational model advancing future research and applications in medical image analysis.
Enhancing Cryptography of Grayscale Images via Resilience Randomization Flexibility

Images are the most used e-files within normal personal life utilization affecting overall community safety and security. This research focuses on grayscale images security via resilience crypto-encryption nonviolent utilization. The work relies on flexibility XOR randomizations searching for most secure image crypto scheme testing different pseudo random number generators (PRNG) aiming to select best choice for security enhancement. The experimentations assume flexibly in selecting crypto operation as well as PRNG changings depending on e-image variations. The research implements several encryption schemes adopting substitution and transposition in variance attempts. All PRNGs are tested to encrypt the grayscale images revealing attractive prioritization remarks. The paper search anticipates to recognize an overview of enhancing cryptography of grayscale images via resilience randomization flexibility. The work PRNG and crypto-operation flexibility provided best interesting privacy intellectual image security as innovative basic crypto research track to build upon.
A novel hybrid feature fusion model for detecting phishing scam on Ethereum using deep neural network

The development of blockchain technology has brought prosperity to the cryptocurrency market and has made the blockchain platform a hotbed of crimes. As one of the most rampant crimes, phishing scam has caused a huge economic loss to blockchain platforms and users. In order to address the threat to the financial security of blockchain, this paper proposes a model based on hybrid deep neural network to detect phishing scam accounts, namely LBPS (LSTM-FCN and BP neural network-based Phishing Scam accounts detection model), and verifies its effectiveness on Ethereum. The LBPS model provides a novel approach to analyse transaction records by adopting the BP neural network to obtain the implicit relationship between features extracted from transaction records and the LSTM-FCN neural network to capture the temporal feature from all transaction records of a target account. The experimental results demonstrate that the features selected in this paper could identify phishing scam accounts effectively. Moreover, the LBPS model performs better than the existing methods and baseline models with an F1-score of 97.86%.
Masked Swin Transformer Unet for Industrial Anomaly Detection

The intelligent detection process for industrial anomalies employs artificial intelligence methods to classify images that deviate from a normal appearance. Traditional convolutional neural network (CNN)-based anomaly detection algorithms mainly use the network to restructure abnormal areas and detect anomalies by calculating the errors between the original image and reconstructed image. However, the traditional CNNs struggle to extract global context information, resulting in poor anomaly detection performance. Thus, a masked Swin Transformer Unet (MSTUnet) for anomaly detection is proposed. To solve the problem of insufficient abnormal samples in the training phase, an anomaly simulation and mask strategy is first applied on anomaly-free samples to generate a simulated anomaly and, then, the Swin Transformer's powerful global learning ability is used to inpaint the masked area. Finally, a convolution-based Unet network is used for end-to-end anomaly detection. Experimental results on industrial dataset MVTec AD show that MSTUnet achieves superior anomaly detection and localization performance.
Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model

To guarantee the safety of the power grid system, it is essential to proceed reliable powerline inspection. Insulators are key devices in the powerlines. Their major function is to achieve mechanical fixing and electrical insulation, they play a key role in power lines. Insulators are deployed outdoors. Therefore, ensuring the safe operation of insulators is significant in the powerline inspection. Among all the inspection method, visual inspection is the key way. However, problems such as large changes in outdoor lighting have a strong impact on the accuracy of insulator detection. To overcome the shortcomings of uneven illumination, low contrast and poor details display in outdoor images, in this paper we introduces an image enhancement method based on illumination correction and compensation. First, the input data is converted from RGB color space to the HSV space, and three components, H, S and V, are obtained. The saturation component S is enhanced adaptively, and the brightness component V is processed by multi-scale gradient domain guided filter (MGDGF). Then the illumination component of the image is extracted, and corrected by two-dimensional adaptive Gamma transformation. The new brightness component is fused by Retinex based models. It helps to enhance the dark details and overall brightness of the image. This method not only solves the uneven illumination problem of the image, but also improves the contrast and details, while maintaining the original naturalness. Further, we introduce a real-time one step detection model based on YOLOv5, to detect the defect of the insulator. We evaluate the proposed method on an open public dataset. The evaluation results demonstrate that our proposed method can get very competitive results while maintaining real-time performance.
Metaheuristics with Deep Transfer Learning Enabled Detection and classification model for industrial waste management

Rapid industrialization has led to the generation of a considerable amount of waste, both solid and liquid, in industrial fields like food processing, sugar, pulp, sago or starch, dairies, paper, fruit processing, poultry, distilleries, slaughterhouses, tanneries, and so forth. Despite the requirement for pollution control measures, the waste is discharged into water bodies or generally dumped on land without appropriate management, and thus becomes a significant source of environmental pollution and health hazards. The most essential step of waste management is the segregation of waste into the various elements, and normally this process is done automatically by hand-picking. A smart waste material classification technique is required to simplify the procedures. Therefore, the study presents a new Metaheuristics with Deep Transfer Learning Enabled Detection and Classification Methods for Industrial Waste Management (MDTLDC-IWM) method. The presented MDTLDC-IWM model facilitates the use of DL models for the identification and classification of waste materials in the IWM system. To accomplish this, the presented MDTLDC-IWM model follows two key phases, namely waste object recognition and waste object classification. At the initial stage, the YOLO-v5 object detector with the Harris Hawks Optimization (HHO) algorithm is used. Next, in the second stage, the stacked sparse auto encoder (SSAE) model is applied for the waste object classification method. The SSAE model is effectively optimized using the Aquila Optimization Algorithm (AOA), which helps to accomplish maximum classification of waste objects. The MDTLDC-IWM model has achieved a precision of 96.84 percent and an F score of 96.71 percent. A benchmark dataset is used to test the experimental validity of the presented MDTLDC-IWM model. Extensive comparative analysis reported the enhanced performance of the MDTLDC-IWM model over recent state-of-the-art approaches.
Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity.The user's access over the internet creates massive data processing over the internet.Big data require an intelligent feature selection model by addressing huge varieties of data.Traditional feature selection techniques are only applicable to simple data mining.Intelligent techniques are needed in big data processing and machine learning for an efficient classification.Major feature selection algorithms read the input features as they are.Then, the features are preprocessed and classified.Here, an algorithm does not consider the relatedness.During feature selection, all features are misread as outputs.Accordingly, a less optimal solution is achieved.In our proposed research, we focus on the feature selection by using supervised learning techniques called grey wolf optimization (GWO) with decomposed random differential grouping (DrnDG-GWO).First, decomposition of features into subsets based on relatedness in variables is performed.Random differential grouping is performed using a fitness value of two variables.Now, every subset is regarded as a population in GWO techniques.The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research.Once the features are optimized, we classify using advanced kNN process for accurate data classification.The result of DrnDG-GWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm.The accuracy and time complexity of the proposed algorithm are 98% and 5 s, which are better than the existing techniques.
Hierarchical Interactive Multimodal Transformer for Aspect-Based Multimodal Sentiment Analysis

Aspect-based multimodal sentiment analysis (ABMSA) aims to determine the sentiment polarities of each aspect or entity mentioned in a multimodal post or review. Previous studies to ABMSA can be summarized into two subtasks: aspect-term based multimodal sentiment classification (ATMSC) and aspect-category based multimodal sentiment classification (ACMSC). However, these existing studies have three shortcomings: (1) ignoring the object-level semantics in images; (2) primarily focusing on aspect-text and aspect-image interactions; (3) failing to consider the semantic gap between text and image representations. To tackle these issues, we propose a general Hierarchical Interactive Multimodal Transformer (HIMT) model for ABMSA. Specifically, we extract salient features with semantic concepts from images via an object detection method, and then propose a hierarchical interaction module to first model the aspect-text and aspect-image interactions, followed by capturing the text-image interactions. Moreover, an auxiliary reconstruction module is devised to largely eliminate the semantic gap between text and image representations. Experimental results show that our HIMT model significantly outperforms state-of-the-art methods on two benchmarks for ATMSC and one benchmark for ACMSC.
A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot

To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. We propose a garbage detection method based on a modified YOLOv4, allowing high-speed and high-precision object detection. Specifically, the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection accuracy, YOLOv4 is transformed into a four-scale detection method. To improve the detection speed, model pruning is applied to the new model. By virtue of the improved detection methods, the robot can collect garbage autonomously. The detection speed is up to 66.67 frames/s with a mean average precision (mAP) of 95.099%, and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.
Dynamic intent-aware iterative denoising network for session-based recommendation

Session-based recommendation aims to predict items that a user will interact with based on historical behaviors in anonymous sessions. It has long faced two challenges: (1) the dynamic change of user intents which makes user preferences towards items change over time; (2) the uncertainty of user behaviors which adds noise to hinder precise preference learning. They jointly preclude recommender system from capturing real intents of users. Existing methods have not properly solved these problems since they either ignore many useful factors like the temporal information when building item embeddings, or do not explicitly filter out noisy clicks in sessions. To tackle above issues, we propose a novel Dynamic Intent-aware Iterative Denoising Network (DIDN) for session-based recommendation. Specifically, to model the dynamic intents of users, we present a dynamic intent-aware module that incorporates item-aware, user-aware and temporal-aware information to learn dynamic item embeddings. A novel iterative denoising module is then devised to explicitly filter out noisy clicks within a session. In addition, we mine collaborative information to further enrich the session semantics. Extensive experimental results on three real-world datasets demonstrate the effectiveness of the proposed DIDN. Specifically, DIDN obtains improvements over the best baselines by 1.66%, 1.75%, and 7.76% in terms of [email protected] and 1.70%, 2.20%, and 10.48% in terms of [email protected] on all datasets.
A New 5-D Multistable Hyperchaotic System With Three Positive Lyapunov Exponents: Bifurcation Analysis, Circuit Design, FPGA Realization and Image Encryption

In this work, we describe the model of a new 5-D hyperchaotic system with three positive Lyapunov exponents. Since the maximum positive Lyapunov exponent of the proposed hyperchaotic system is larger than twelve, the new hyperchaotic system is highly hyperchaotic. We also show that the new 5-D hyperchaotic system exhibits multistability with coexisting attractors. Using Multisim, we design an electronic circuit for the new 5-D hyperchaotic system. The hardware implementation of the new 5-D hyperchaotic system is done by applying two numerical methods. From the experimental results of the FPGA-based implementation, we show that the attractors observed in a Lecroy oscilloscope are in good agreement with numerical simulations. To prove the reliability of the proposed system for cybersecurity purposes, we presented a new image cryptosystem using our hyperchaotic system. Experimental outcomes show the efficiency and the reliability of our cryptosystem based on the proposed hyperchaotic system.
Levelized Multiple Workflow Allocation Strategy Under Precedence Constraints With Task Merging in IaaS Cloud Environment

Cloud Service Providers are speedily becoming the target platform for scientific workflow computations due to the massive possible and flexible pay-as-you-go pricing model.Workflow allocation problem in cloud systems is considered NP-hard.A heterogeneous IaaS cloud could be fully effective if the allocation method provides an efficient mapping between virtual machines (VMs) and workflow applications demanding execution.First, we model multiple workflow allocation problem in the cloud environment.Then, we propose a levelized multiple workflow allocation strategy with task merging (LMWS-TM) to optimize turnaround time for multiple workflow applications in the Infrastructure as a Service (IaaS) cloud environment to achieve better performance.The task merging scheme is incorporated into workflows after partitioning and prior to allocation to reduce inter-task communication share and the total number of depth levels for improving the overall completion time.Moreover, it considers the inter-task communication and inter-machine distance for estimating communication cost share among tasks on the schedule generated.Furthermore, the scheme is capable enough to use simple and flexible level attributes to tackle precedence constraints.Afterward, we conducted an experimental study to evaluate LMWS-TM by comparative performance analysis with its peers, namely SLBBS, DLS, and HEFT, on quality of service (QoS) parameters, namely, turnaround time, system utilization, flow time, and response time.The study reveals the superior performance of LMWS-TM among its considered peers in almost all the cases for almost all considered parameters under investigation.Finally, we performed statistical testing to test the significance level using SPSS 20, confirming the hypothesis drawn in the experimental study.
A mathematical programming approach to SVM-based classification with label noise

In this paper we propose novel methodologies to optimally construct Support Vector Machine-based classifiers that take into account that label noise occur in the training sample. We propose different alternatives based on solving Mixed Integer Linear and Non Linear models by incorporating decisions on relabeling some of the observations in the training dataset. The first method incorporates relabeling directly in the SVM model while a second family of methods combines clustering with classification at the same time, giving rise to a model that applies simultaneously similarity measures and SVM. Extensive computational experiments are reported based on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of the proposed approaches.