Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Agricultural Pest Target Detection Method based on YOLOv7

The detection of agricultural pests is a crucial part of smart agriculture. In the realm of small agricultural pest detection within complex environments, this paper presents our novel, tailored pest detection approach. Our methodology is rooted in an optimized adaptation of the YOLOv7 model. The enhanced model we propose guarantees real-time detection capabilities, while simultaneously delivering improved accuracy. The key feature augmentations we incorporate are as follows: We developed a precisely engineered detection layer to tackle the complexities posed by small target detection. Optimization of preliminary box generation was realized through the deployment of a clustering algorithm. Additionally, we improved the non-maximum suppression approach. To manage extraneous information in the convoluted agricultural environment, we fused the ACmix attention mechanism module within the model to concentrate on agricultural pests. Our practical results substantiate the efficacy of our enhanced model, named YOLOv7-AgirPest, which achieves an astounding detection speed of 60.17 FPS in identifying agricultural pests. Additionally, we achieved significant progress with an mAP value reaching 67.23%, marking a 6.1% relative enhancement over the baseline YOLOv7 model. In summary, our refined model not only fulfills real-time detection constraints but also delivers superior agricultural pest detection in complex field conditions.
Uncanny valley: A preliminary study on the acceptance of Malaysian urban and rural population toward different types of robotic faces

The proliferation of robotic technologies in recent years brings robots closer to humanities. There are many researches on going at various stages of development to bring robots into our homes, schools, nurseries, elderly care centres, offices, hospitals and factories. With recently developed robots having tendency to have appearance which increasingly displaying similarities to household animals and humans, there is a need to study the existence of uncanny valley phenomenon. Generally, the acceptance of people toward robots increases as the robots acquire increasing similarities to human features until a stage where people feel very uncomfortable, eerie, fear and disgust when the robot appearance become almost human like but not yet human. This phenomenon called uncanny valley was first reported by Masahiro Mori. There are numerous researches conducted to measure the existence of uncanny valley in Japan and European countries. However, there is limited research reported on uncanny valley phenomenon in Malaysia so far. In view of the different cultural background and exposure of Malaysian population to robotics technology compared to European or East Asian populations, it is worth to study this phenomenon in Malaysian context. The main aim of this work is to conduct a preliminary study to determine the existence of uncanny valley phenomenon in Malaysian urban and rural populations. It is interesting to find if there are any differences in the acceptance of the two set of populations despite of their differences. Among others the urban and rural populations differ in term of the rate of urbanization and exposure to latest technologies. A set of four interactive robotic faces and an ideal human model representing the fifth robot are used in this study. The robots have features resembling a cute animal, cartoon character, typical robot and human-like. Questionnaire surveys are conducted on respondents from urban and rural populations. Survey data collected are analysed to determine the preferred features in a humanoid robot, the acceptance of respondents toward the robotic faces and the existence of uncanny valley phenomenon. Based on the limited study, it is found that the uncanny valley phenomenon existed in both the Malaysian urban and rural population.
PINet: Towards Effective and Efficient Industrial Control Protocol Identification

Accurate identification of industrial control protocols plays a pivotal role in ensuring the secure and reliable operation of Industrial Internet of Things (IIoT) systems. In this paper, we propose, PINet, a novel model that employs a combination of a Gated Recurrent Unit (GRU) network and a multi-head attention mechanism for industrial control protocol identification. PINet effectively captures the underlying patterns within the input sequence by utilizing the hidden state representations obtained from the GRU network. Furthermore, the multi-head attention mechanism facilitates the extraction of crucial information from these hidden states. Ultimately, a classifier linear layer is employed to map the extracted feature vectors onto the probability distribution of protocol categories. Experimental evaluations demonstrate that PINet achieves outstanding performance and accuracy in classifying industrial control protocols, showcasing its effectiveness and reliability in practical applications.
IPSO-CNN: Malicious Code Classification with Improved PSO Optimized CNN

Convolutional Neural Networks (CNN) are a powerful tool for detecting malicious code through deep learning. However, to achieve optimal performance, the hyperparameters of CNNs must be carefully chosen. Unfortunately, finding the best hyperparameters can be a time-consuming and resourceintensive process, requiring repeated experimentation or previous experience. To address this challenge, the optimization of CNN hyperparameters with improved particle swarm optimization (IPSO-CNN) is proposed. Firstly, to improve the defects of the original PSO that falls into the local optimum and converges prematurely, a new speed update strategy of particles is proposed; secondly, the improved PSO is used to optimize the CNN hyperparameters and find the optimal combination of hyperparameters. Finally, the effectiveness of IPSO-CNN is verified on the well-known Kaggle dataset, and the classification accuracy reaches 98.54%. And compared with the other four classification methods using deep learning techniques, IPSOCNN ranked first in classification performance.
Fast Incremental Data Recognition Method Based on TCN Network

Data centers of various subsidiaries within large industrial companies store massive production data. Every day, it is necessary to extract the updated incremental data from this vast dataset and deliver it on time to the central data center to support subsequent analytics and decision-making. The rapid and accurate identification of incremental data is a critical factor that determines whether the daily data aggregation at the company’s headquarters can be completed on time. This paper proposes a fast incremental data recognition method based on TCN (Temporal Convolutional Network). The method incorporates self-attention layers and causal convolutions of various scales into the TCN network model, enabling the rapid parsing of unstructured log files. An optimization strategy for database DML statements is employed to remove redundant information from the incremental data, resulting in precise and fast incremental data recognition. Experimental results demonstrate that the proposed method in this paper significantly improves the accuracy and speed of incremental data recognition.
Maintanence of Voltage Level in Domewtic Based System Which Uses Sustainalble Grid System

Simplifying the integration of energy from renewable sources (RESs) into the current power system is the main goal of the development of microgrids, or MGs. Energy-storage units (RESs) usually link to the grid using power electronic inverters. As more and more different types of Renewable Energy Sources (RESs) become essential components of the power grid, inverter-based generators (IBGs) will be more common in future power systems than synchronous machines. This change presents significant differences between the properties of IBGs and synchronous generators (SGs), especially with regard to inertia and reactive power supply. As a result, IBGs have different effects on system dynamics than do SGs, which calls for new methods of system stability analysis. Studies investigating the durability of power systems using IBGs are now underway. The specialized issue – Power Stability and Microgrids in Energies is introduced by this thorough review paper. It conducts a thorough analysis of the body of research on the stability of the voltage of power networks in which a significant portion of the generating mix is made up of IBGs. Given how quickly this field of study is developing, a lot more work is likely to be done on this subject by the time this article is published and for some time to come. Other important aspects of microgrid stable voltage will surely be clarified by other papers in this special issue.
Integrating Heterogeneous Modalities for Comprehensive Facial Analysis: The Heteroface Database

Heterogeneous Face Recognition (HFR) is gradually gaining importance in various domains including biometrics and cognitive studies. Classical methods are being replaced by deep learning techniques as a means to handle varied face modalities. The paper highlights the importance of HFR and introduces a face database that incorporates diversities in spectra, illumination, and formats (viz. photograph-sketch, longitudinal, 2D-3D). The role of deep learning methods has been discussed, and its advantages over the limitations of traditional methods have been emphasized. A comprehensive real-world HFR database, such as the one presented in the paper, will likely aid algorithm development, enriched by a neural network-based curation technique that enhances diversity by excluding similar instances. The paper underscores the role of deep learning techniques in tackling the challenges in the field of HFR and presents the database as a significant contribution to advances in HFR research and application.
Random Orthogonalization for Federated Learning in Massive MIMO Systems

We propose a novel communication design, termed random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The key novelty of random orthogonalization comes from the tight coupling of FL and two unique characteristics of massive MIMO – channel hardening and favorable propagation. As a result, random orthogonalization can achieve natural over-the-air model aggregation without requiring transmitter side channel state information (CSI) for the uplink phase of FL, while significantly reducing the channel estimation overhead at the receiver. We extend this principle to the downlink communication phase and develop a simple but highly effective model broadcast method for FL. We also relax the massive MIMO assumption by proposing an enhanced random orthogonalization design for both uplink and downlink FL communications, that does not rely on channel hardening or favorable propagation. Theoretical analyses with respect to both communication and machine learning performance are carried out. In particular, an explicit relationship among the convergence rate, the number of clients, and the number of antennas is established. Experimental results validate the effectiveness and efficiency of random orthogonalization for FL in massive MIMO.
Safe Communication System Implementation In In Smart Grids With The Hybrid Systems

By using communication technologies to maximize energy management, the intelligent grid encourages customer participation by lowering loads during peak hours via the use of a dynamic energy pricing model. The goal of the newly developed Home Energy Gateway (HEG) concept is to enable active customer involvement in load control. The HEG idea is still in its infancy, and there aren't many thorough answers to problems with data representation, seamless discovery, interoperability, security, and privacy in the literature that is currently available. In order to tackle these problems, a communication architecture is introduced in this study, with a particular emphasis on problems with device integration and interoperability across various manufacturers. By providing smooth auto-discovery and zero-configuration-based networking across various devices at consumer locations, the suggested communication architecture lessens these issues. The framework prioritizes security by integrating an elliptic-curve-based security mechanism that safeguards user privacy and offers strong protection against a range of assaults. The usefulness of the framework is validated by real-world tests carried out in a networking context. The findings show that it is sufficiently flexible, low-bandwidth required, lightweight, safe, and portable to be used in dynamic energy management inside the smart grid.
Three‐dimensional numerical simulations of smooth, asymmetrically roughened, and baffled culverts for upstream passage of small‐bodied fish

Traditional box culvert designs lead to development of high velocity zones in the culvert barrel that often impede upstream migration of fish. Herein, three‐dimensional Reynolds‐averaged Navier‐Stokes (RANS)‐ and Large eddy simulation (LES)‐based computational fluid dynamics (CFDs) simulations were performed to compare the effectiveness of smooth, asymmetrically roughened, and corner‐baffled barrels, in creating low‐velocity zones (LVZs) and providing opportunity for upstream passage of small‐bodied fish. The results revealed distinctive benefits provided by the asymmetrically roughened and corner‐baffled barrels relative to the smooth barrel. Cross‐sectional asymmetry, corners, and obstructions are important factors that contribute to the generation of LVZs conducive to fish passage, albeit contiguity of LVZs is required, particularly for weak swimmers. The study demonstrates the adequacy and effectiveness of CFD models to complement traditional laboratory studies in understanding basic mechanisms beneficial to fish passage and to provide insights into future designs.
Lung Cancer Detection and Classification using Transfer Learning with Pre-trained VGG19 Convolutional Neural Networks

It is possible to diagnose and identify a variety of illnesses with the use of high-quality figures obtained from medical equipment. However, obtaining and storing these kinds of photos may be quite costly, and diagnosing a patient might take a long time. Artificial intelligence (AI) solutions for automatic diagnosis may play a major role in addressing the challenges of time and cost. The categorization of medical images may be effectively solved by pre-trained deep learning models. Lung cancer continues to be a widespread and serious type of cancer on a global scale. Timely and precise identification is crucial for successful treatment and enhanced patient results. This study presents a novel approach to the identification and categorization of lung cancer by using transfer learning via a VGG19 Convolutional Neural Network (CNN) that has been trained beforehand. Our work harnesses the power of transfer learning by leveraging the VGG 19 architecture, provides the basis for lung cancer diagnosis, having been pre-trained on a large dataset. Our carefully curated dataset encompasses over 14600 diverse lung images, covering benign, malignant, and normal cases, providing the model with a rich source of information. The model demonstrated outstanding performance metrics, achieving a training accuracy of 99.68%, 98.11% for validation accuracy and 97.93% for test accuracy. Such high accuracy holds great promise for expediting and improving the diagnostic capabilities of healthcare professionals. By leveraging Transfer Learning from VGG19, our model demonstrates adaptability to diverse medical imaging tasks, making it a valuable tool for real-world clinical applications.
Combined Review on Battery and Energy Management Systems in Hybrid Electric Vehicle

Battery and Energy Management System plays a crucial role in Hybrid Electric Vehicles as it optimizes the use of available energy and ensures that the battery is charged and discharged efficiently, thus increasing vehicle range and improving overall performance. By monitoring the state of charge and health of the battery, controlling the power flow between the battery and other components like electric motor and engine, and utilizing thermal management systems to regulate temperature, BEMS helps in extending the lifespan of the battery and maintaining its safety. Furthermore, BEMS also enables the vehicle to adjust its energy usage according to driving conditions and driver behavior, resulting in better fuel economy and reduced emissions. Overall, BEMS in HEV is essential for achieving greater efficiency, performance, and sustainability in modern transportation systems.
Hierarchical Mixup: Improving Representations By Binary Masks And Interpolating Hidden States

Mixed Sample Data Augmentation (MSDA) methods are powerful Data Augmentation that interpolates between two or more examples and their corresponding target labels in the input or feature space. Recently, the Mixup method overlays or cuts and pastes two or more objects into one image, but this requires attention to the selection region. To address these issues, we propose the Hierarchical Mixup method, a simple MSDA method that enables a neural network to make predictions about the interpolation of hidden representations. It utilizes the mixed, augmented features as additional training samples to obtain a neural network that performs better on multiple hidden representations. Using this method allows the neural network to learn classes with less data. In addition, Hierarchical Mixup uses random binary masks obtained by applying thresholds to low-frequency images sampled from the Fourier space. It uses the generated binary masks as weights for MSDA. These random masks can present various shapes and be generated for two-dimensional data. Hierarchical Mixup is a lightweight module that can be applied to existing classification models. In this study, we used PreAct ResNet-18 as the base model, and our method achieved accuracies of 97.35% and 81.14% on the CIFAR-10/100 datasets, respectively. The experiments demonstrate that Hierarchical Mixup enhances the generalization ability of Deep Neural Networks (DNNs) and shows significant improvements in different datasets.
Research on panoramic anomaly detection method for super-scale heterogeneous cloud resources

The current conventional panoramic anomaly detection methods for heterogeneous clouds mainly achieve anomaly detection by collecting node metrics and comparing them with conventional metric values, which leads to poor detection results due to the lack of effective processing of monitoring sequences. In this regard, a panoramic anomaly detection method for ultra-large-scale heterogeneous cloud resources is proposed. By collecting heterogeneous cloud index parameter samples, constructing dynamic monitoring sequences, and decomposing heterogeneous cloud resource data by combining wavelet transform method to extract heterogeneous cloud index data features, based on which the panoramic anomaly detection method is conceived. In the experiment, the designed panoramic anomaly detection method is tested for its detection effect. The final results can prove that the algorithm has a high AUC value and a more desirable panoramic detection effect when the proposed method is used to detect heterogeneous cloud resources.
BdrtEE: A Bert-based Decoder Refinement Tagging Method for Overlapping Event Extraction

The main task of event extraction is to extract event information from unstructured text, which is the basis for building event knowledge graphs and creating the foundation for downstream tasks. However, the existence of events is not always a simple single event. In some domains, event trigger words and arguments play multiple roles in different events. This paper mainly focuses on the issue of event overlaps, which includes trigger overlap and argument overlap. This paper presents a Bert-based decoder refinement tagging method for overlapping event extraction, BdrtEE. Specifically, BdrtEE performs type detection trigger extraction and argument extraction in turn, where the overlapping targets are extracted individually based on particular prior condition predictions. In the model, the trigger decoder and the argument decoder use the same structure, multi-head attention and MLP. The model separately adds a pooler layer to the trigger word extraction process to obtain global semantic information to assist trigger word extraction. All subtasks colearn jointly the loss function to capture the dependencies between subtasks. Compared with previous research methods, BdrtEE provides a significant improvement in overlapping event extraction over previous research approaches on FewFC dataset. BdrtEE improves the F1 values of TC, AI, AC by 4.6%, 4.2%, and 3.9% for the extraction task against overlapping events when compared with the current mainstream methods.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

Through analysis of various fault voltage characteristics of modular multilevel converter high voltage direct current (MMC-HVDC) transmission system, it shows that the positive and negative voltage relationships of direct current transmission lines with single pole-to-ground faults are obviously different from other faults. Hence, Pearson correlation coefficient is used to describe the degree of difference in this work. In the case of single pole-to-ground fault, correlation coefficient is positive and close to 1, while as for other failures, correlation coefficient is negative and close to -1. This paper analyzes the mechanism of correlation coefficient to withstand transition resistance and verifies the effectiveness of the proposed method under high resistance grounding fault. A novel scheme is proposed by combining the quantity sum of voltage variation characteristics, which can effectively identify fault pole. The proposed scheme needs smaller time window and owns high efficiency, as well as great robustness to transition resistance. INDEX TERMSMMC-HVDC transmission, High resistance ground fault, Pearson correlation coefficient, fault detection.
Learning Optimal Kernels for Gaussian Process Regression

Black box optimization focuses on optimizing unknown functions in high-dimensional spaces. In certain applications, evaluating these functions is expensive, hence the optimization must be performed under tight sample budgets. Past works have approached black-box optimization using Gaussian Process Regression (GPR) and sample efficiency has been improved by utilizing information about the shape/structure of the function. We propose to discover the structure of the function by learning a GPR kernel. Learning the kernel is achieved via an auxiliary optimization in (latent) kernel space, designed inside a variation autoencoder. The optimal kernel is expected to best “explain” the unknown function, helping lower the sample budget. Results show that our method, Kernel Optimized Blackbox Optimization ( KOBO ), effectively minimizes the black-box function at a substantively lower sample budget. Results hold not only in synthetic black box functions but also in real applications, e.g., where a hearing aid needs to be personalized with limited user queries.
Inter-individual Variations in Swarm Robotics with the Case Study of Kilobots

—Inter-individual differences are studied in natural systems, such as fish, bees, and humans, as they contribute to the complexity of both individual and collective behaviors. However, individuality in artificial systems, specifically in robotic swarms, is undervalued or even overlooked. Agent-specific deviations from the norm in swarm robotics are usually understood as mere noise that can be minimized, for example, by calibration, or regulated by feedback control. We observe that robots have consistent deviations and argue that awareness and knowledge of these can be exploited to serve a task. We use Kilobots as our case study.