Miracle-dz/UniFAR-FTUs huggingface dataset:
Twice-Trained Agglomerative clustering approach using topic modeling over Generic Semantic Core Knowledge Graph

Topic Modeling (TM) can act as a bridge linking unstructured text data to a structured knowledge representation in a Knowledge Graph (KG). The Latent Dirichlet Allocation (LDA) is a commonly used distributional term clustering technique in this regard. However, existing distributional term clustering approaches have not utilized LDA as a bottom-up training strategy with prior knowledge to cluster semantically related terms as concepts across different domains for building and enhancing a GSCKG. We propose to employ a Twice-Trained Agglomerative hierarchical framework using LDA over Generic Semantic Core KG (T2AggLDA-GSCKG), outlined in five steps. We aim to align term topics with the predefined Core Concepts (CCs) of SCKGs, thereby designating modules of a GSCKG that facilitate both its building and enrichment. Our goal is to adapt the LDA term clustering process by utilizing a topic seed-based LDA model to consider these CCs. During the 1st LDA training, we endeavor to identify hypernym and related relations among noun phrase patterns to construct SCKG, and during the 2nd training, we aim to enhance it. To achieve this, we will boost the 2nd training LDA input data by benefiting from CCs’ two prior knowledge techniques for topic-seed terms incorporation namely seed key knowledge injection and entity masking. The evaluation results show that our proposal has an overwhelming term clustering performance over CCs. It outperforms other unsupervised and semi-supervised distributional baselines on two datasets related to fish hunting and ontology domains, with nearly 13 times higher precision compared to that of normal LDA training.
Exploration of Various Machine Learning Techniques for Identifying and Mitigating DDoS Attacks

This study is conducted as a result of some of the key observations documented in a recent study published by IEEE, titled "Threat Actors’ Tenacity to Disrupt: Examination of Major Cybersecurity incidents" where some of the key findings therein that study describes how DDoS attack together with other attack techniques were used by threat actors to disrupt public and private enterprises on multiple occasions. In this study, there is a focus on DDoS attacks and exploration of how various machine learning techniques may be leveraged to identify and mitigate them. Real world examples of major DDoS attacks that happened and reported or announced to the public were examined and it was found that in the last seven years, there has been an increase in the use of DDoS attacks to cause major disruptions to organizations. In a total of over 700 major cybersecurity incidents that we analyzed from 2015 to 2022, evidence of DDoS attacks were found every year. Hence, in that subset of dataset we examined, there was an increase from 8 to 31 major DDoS attacks from 2021 to 2022 which is an estimate of 288 percent increase. Due to the volume of network traffic analysis involved in the detection of DDoS events which more often may be very difficult for humans to manually or efficiently analyze, the validation of the importance of utilizing AI technologies such machine learning algorithms as effective capabilities for the detection of DDoS attacks and DDoS mitigation are assessed in this study. Identification of specific machine learning algorithms that may be used for the detection and mitigation of DDoS attacks were looked into. Given that there is no one-size-fits-all solution to the mitigation of DDoS attacks, this study echoes the leveraging of the CIS Benchmarks and also encourages organizations to adopt referencing one of the NIST frameworks that was released to the public on January 26, 2023 titled "AI Risk Management Framework" as guidelines in ensuring multiple layers of controls in handling DDoS attacks.
Human-Like Autonomous Lane Change Trajectory Planning Based on Driver Risk Perception Model

Unmanned driving technologies have great potential in improving traffic safety and reducing driver workload, but the human driving mechanism is rarely considered. Human-like unmanned driving could meet the expectations of passengers and pedestrians, so that unmanned vehicles can be more widely accepted. However, current human-like unmanned driving methods rely heavily on historical data, only imitating the driver's behavior without fundamentally explaining the motivation behind his behavior. This study develops a driver risk perception model that describes drivers' perceptions of risk for providing an in-depth explanation of human driving mechanisms to lay a theoretical foundation for human-like driving methods. It also reveals the characteristics of the driver's perceived risk index during lane changing and its relationship with the lane change trajectory. Finally, it proposes a human-like autonomous lane change trajectory planning method, which achieves autonomous planning of human-like lane change trajectory with high fidelity by using the position and speed information of the ego vehicle and obstacles.
A Lyapunov-Based Design of Lateral Dynamics Control for a Smart High-Efficiency Flight Vehicle

This paper presents a Lyapunov-based design of lateral dynamics control for a smart high-efficiency flight vehicle (HEFV). Based on the lateral dynamics of the HEFV, the path following control problem is transformed into a simple roll angle following control problem by constructing two Lyapunov functions that guarantee the convergence to the considered straight path. In addition, since a very careful setting and/or adjustment of controller gains is highly required in experimental field sites, this paper provides a safety-oriented framework for controller gain adjustment. The control scheme still guarantees considered control performances represented in terms of linear matrix inequalities, although it is simple, straightforward, and safety oriented. Finally, the effectiveness of the proposed design approach is demonstrated in flight experiments.
Deep Learning in IoT: An LSTM Approach for NDVI Forecasting

This study presents a novel application of a Long Short-Term Memory (LSTM) deep learning model for time-series analysis of the Normalized Difference Vegetation Index (NDVI) from January 1, 1984, to April 21, 2023. As remote sensing technologies generate substantial environmental data, advanced analytics like LSTM provide essential tools for precise interpretation and forecasting. Through grid search optimization, hyperparameters were fine-tuned for optimal LSTM performance. The NDVI mean value over the study period is 0.332, indicative of a moderate vegetation presence. The data series’ sta-tionarity, confirmed through the Dickey-Fuller test, contributes to accurate prediction outcomes. The LSTM model demonstrates superior predictive performance, evidenced by the Root Mean Squared Error (RMSE) values of 0.000764 and 0.000900 for the training and testing datasets respectively. The high R-squared and correlation values further substantiate its efficacy. This study paves the way for leveraging LSTM models in large-scale NDVI data analysis, contributing to environmental monitoring, climate change tracking, and vegetation health assessments. Future work can extend this model to other remote sensing indices and explore various deep learning architectures for enhanced predictive accuracy. The main objective is to identify the optimal LSTM hyperparameters for NDVI prediction using grid search optimization. Our results are expected to provide valuable insights into how LSTM models can be effectively tuned for improved NDVI prediction, potentially benefiting environmental monitoring and decision-making processes.
Assessing the Implications of Data Heterogeneity on Privacy-Enhanced Federated Learning: A Comprehensive Examination Using CIFAR-10

In the context of an increasingly digital society with pressing privacy concerns, our research investigates the effectiveness of privacy-preserving artificial intelligence solutions like Federated Learning. This study focuses on three main areas: Federated and Centralized Learning applications, the influence of data heterogeneity on client data accuracy, and the evaluation of contemporary federated algorithms in scenarios of extreme heterogeneity. Federated Learning, using the FedAvg algorithm, demonstrated superior testing accuracy (88.54%) over Centralized Learning (87.98%) on the non-heterogeneous CIFAR-l0 dataset, indicating its potential as an efficient, privacy-preserving solution for various machine learning applications. Additionally, our findings highlight an inverse relationship between data heterogeneity and Federated Learning model accuracy, underscoring the need for strategies to mitigate this challenge and boost model performance. Upon evaluating several federated learning algorithms under high data heterogeneity (alpha=1.0), SCAFFOLD and FedOpt outperformed FedAvg and FedProx, demonstrating the significance of algorithm design in addressing data heterogeneity. SCAFFOLD and FedOpt showcased greater communication efficiency, attributed to their faster convergence and fewer required communication rounds. This study offers invaluable insights into addressing data heterogeneity, improving communication efficiency, and enhancing federated learning's performance and applicability in real-world scenarios, thereby furthering privacy-preserving artificial intelligence research.
AirMesh: A RIPng and Raft Based Resilient Networking Approach for Unmanned Systems

In self-organizing network scenarios such as unmanned aerial vehicle (UAV) swarms, the network topology is constantly changing, and the distributed nodes are relatively sparse. How to build a resilient network in this scenario has become an important and challenging problem. At present, most unmanned aerial vehicle swarms rely on ground control stations for management, but once the ground control station fails, the entire network often falls into chaos. To solve this problem, this paper proposes a distributed and fault-tolerant self-organizing network architecture for unmanned systems—AirMesh. The network architecture of AirMesh is based on the RIPng routing strategy and the Raft consistency protocol, which enables the entire unmanned aerial vehicle swarms to quickly auto-configure at the initial setup and automatically recover after communication interruption. To better illustrate these features of the AirMesh architecture, we design and demonstrate a prototype system, and verify its performance and effectiveness through experiments.
A Review of Multi-Robot Collaborative Simultaneous Localization and Mapping

Collaborative Simultaneous Localization and Mapping (C-SLAM), commonly known as multi-robot SLAM, is a prefatory technology that allows multiple robots to efficiently traverse large and intricate areas. This is possible due to the merging of maps and the recognition of shared spaces. Despite its many benefits, multi-robot systems encounter various challenges, such as communication barriers, co-localization issues, and joint optimization problems. This paper reviews the sensors and back-end optimization methods used for SLAM. Then, it summarizes existing collaborative SLAM frameworks and some novel solutions. Finally, it discusses trends and promising research directions for collaborative SLAM.
The comparison of EEG features in specific Mandarin words’ speech

Due to advancements in brain signal application technology, there has been a growing focus on the human speech Brain-Computer Interface (BCI) in recent years. An essential initial phase in crafting speech recognition systems is comprehending the inherent features of nature speech processing. To ensure the decoding capability of human speech BCI, researchers have to investigate the differences between overt speech and imagined speech, and they also need to find the best paradigm to build a reliable BCI. Thus, this study compared the differences in brain dynamics between overt speech and imagined speech while using the four Mandarin words "上" (up), "下" (down), "左" (left), and "右" (right). We first calculated the EEG alpha and beta band power for multiple channels situated in the regions of Broca's and Wernicke's areas (specifically F5, F7, FC3, FC5, and C5), to analyze the brain activity changes while speaking or imagining speech. Second, we used Daubechies 4 (db4) wavelet to do a 7-level decomposition to the EEG signal. The corresponding discrete wavelet transform (DWT) coefficients were further calculated with six statistical features for support vector machine (SVM) classification. We found that the "speak (overt speech)" task and the "imagine (imagined speech)" task resulted in significantly different alpha band power changes, and the beta band shows the same phenomenon. The result of average classification accuracy is 71.97% for "speak" and "imagine" distinction. In conclusion, we analyzed the brain dynamics and find that overt speech and imagined speech could be differentiate through DWT coefficients features. This may pave the way to establish a Mandarin imagined speech BCI.
Gain Enhancement of Antenna-on-Chip at 94 GHz with an Integrated Artificial Magnetic Conductor for 6G System-on-Chip

Silicon-based complementary metal oxide semiconductor (CMOS) process has become one of the most popular processes to realize system-on-chip (SoC). However, as one of the essential components of wireless SoC, antennas are typically suffering from the poor radiation because of the highly conductive silicon substrate. Such antennas are known as antenna-on-chip (AoC). To enhance the radiation performance of AoC, artificial magnetic conductors (AMC) with double periodic strip structure layers has been proposed in this paper that can not only provide in-phase reflection but also isolate the antenna from the lossy silicon substrate. The proposed AMC shows a gain enhancement of 4.5 dB. The AMC-backed AoC is well-matched within 77-125 GHz and provides a boresight gain of 2 dBi at 94 GHz.
Super-Twisting Sliding Mode Based Nonlinear Control for Dual-Arm Unmanned Aerial Manipulator Systems

With high maneuverability and large work space, dual-arm unmanned aerial manipulator systems have shown great application potential in load transportation and coordinate manipulation. However, the presence of dual-arm may introduce security risks to the system as the manipulator's movements can alter the CoM (center of mass) of the system. Additionally, the system's complex dynamic coupling and strong nonlinearity present major challenges. To mitigate the uncertain effect exerting on the UAV caused by the dual-arm and mitigate potential external disturbances, a super-twisting based controller is specifically designed for the dual-arm aerial manipulator system. The proposed controller incorporates a nonlinear switching function and is designed based on the first-order derivative of the control input, which has the properties of continuous control input and effective disturbance rejection. Subsequently, it is demonstrated that the system's state converges within a finite period of time. Finally, comprehensive simulation results are presented, demonstrating the controller's effectiveness even in the presence of external disturbances.
Review Study on Inverse Problems Using Deep Learning Techniques with applications

Current research in machine learning indicates that deep neural networks have the ability to address complex challenges in computational imaging, specifically referred to as inverse problems. The inverse problems in image reconstruction and image recovery have the significant importance in the tomographic imaging applications. In this review paper, we introduce a comparison between different regularization techniques employed in the literature for image recovery. Furthermore, this involves comparing various numerical optimization algorithms and techniques used in image reconstruction processes, which includes integrating the conjugate gradients algorithm within the network. These techniques encompass Conjugate Gradient-based Splitting Total Variation (CSTV), Splitting Douglas-Rachford with Edge-Thresholding and Weighted Shrinkage (SD-ET-WS), and Conjugate Gradient - Primal Dual - Nesterov Scheme (CG-PD-NS). These models depend on proximal gradient steps to maintain data consistency. Furthermore, a comparison between different deep learning frameworks is introduced to investigate the best model performance in terms of acceleration factors.
Time-Domain Analysis Of Harmonics On 20-Bus System Due To GMD

In this paper harmonic currents that are introduced in a system due to geomagnetic induced current (GIC) are studied for a 20-bus system. When a transformer is subjected to a DC bias during a geomagnetic disturbance event (GMD) it leads to an effect known as half-cycle saturation. Due to this, reactive power consumption increases and the transformer also starts to draw exciting current rich in harmonic contents. It is well known that excess harmonic currents can cause issues such as increased losses, misoperation of power system protection, equipment overheating, and damage to capacitor banks in the power network. An important consideration while doing harmonic analysis during GIC is the detailed core modeling of a transformer, since characteristics of harmonic current depend on the nonlinear effects of core saturation. Time-domain simulations are conducted in ATPDraw of EMTP-ATP to analyze harmonics in 20-bus system. This is useful in understanding how the harmonics behave with respect to time which can determine what corrective actions can be taken to reduce the detrimental effects of GIC on a system.
Meta-analysis on the Prevalence and Impact of False, Nonactionable, and Nuisance Alarms in Critical Care Environments: Informing Evidence-Based Practices for Improving Patient Safety in Alarm Monitoring Systems

This meta-analysis aims to provide insights into the prevalence of nuisance alarms in physiologic bedside monitoring within intensive care unit (ICU) settings. Through a systematic synthesis of prevalence studies, a meta-analysis was conducted using inverse variance weighting. A comprehensive search was conducted to identify relevant studies for inclusion in the meta-analysis. The selection criteria were defined, and the quality assessment of the included studies was performed using the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) tool. The estimated prevalence of nuisance alarms was calculated based on the pooled data from the selected studies. The heterogeneity among the included studies was evaluated using Cochran's Q test and I^2 statistic. Statistical significance was set at p < 0.05 to determine the presence of significant heterogeneity. The meta-analysis revealed an estimated prevalence of approximately 70% [0.708 (95% CI: 0.547-0.830, p < 0.01)] for false alarms in ICU monitoring. These findings underscore the urgent need to address this prevalent issue in healthcare settings. The identified themes highlighted the significance of reducing false alarms to improve healthcare provider efficiency, enhance patient safety, and optimize alarm management practices. The high prevalence of false alarms emphasizes the need for concerted efforts to mitigate this issue. By addressing the identified themes and implementing evidence-based strategies, healthcare providers can enhance alarm management practices and optimize patient safety in ICU settings. Further research and advancements in medical engineering are warranted to refine alarm systems and improve their reliability.
Modelling Major Depressive Disorder Antidepressant Treatment Response: A miRNA-based Machine Learning Study

Major depressive disorder (MDD) is a psychiatric disorder but currently defined by symptoms rather than biological mechanism. This in turn sets a huge barrier to effective diagnosis and treatment planning. Investigations were done through neuropathogenesis and neuroimaging analysis as an effort to identify discriminative biomarkers for MDD while understanding the biological dependencies. The literature suggested that microRNA or miRNA transcripts are more likely to deliver substantial predictive power in diagnosis and antidepressant treatment response (ATR) prediction. Yet, there presents discrepancy in unique markers, and such discrepancy might be due to the small sample size over some of the reported studies. This study utilized miRNA as a predictor to model MDD ATR using k-nearest neighbour (kNN). The shortlisted miRNA through feature selection techniques scored 71.20%, 68.13%, 72.13%, and 84.07% for three response levels in accuracy, sensitivity, specificity, and precision, respectively. Synthetic Minority Oversampling TEchnique (SMOTE) was then applied to the shortlisted miRNA and three response levels reported at least 98% in each of the mentioned performance metric.
Balancing Crop Priorities: PSO-Based Equitable Irrigation Water Distribution for Efficient Resource Management

Agriculture consumes a significant proportion of water reserves in irrigated areas. Improving irrigation is becoming essential to reduce this high-water consumption by adapting supplies to crop needs and avoiding losses. This global issue has prompted many scientists to think about sustainable solutions using innovative technologies. This study aims to solve the problem of equitable distribution of irrigation water to crops using the PSO (Particle Swarm Optimization) algorithm. The problem involves the allocation of water between N crops, taking into account the priorities of each crop, the minimum and maximum water thresholds required, and the total amount of water available. A precise modeling of the problem is proposed, followed by a description of the PSO algorithm adapted to this situation. The implementation of the algorithm in Python is also presented. The results and performance of the algorithm are discussed, showing its ability to find a fair and efficient solution for irrigation water allocation.
Study on Hybrid Control Strategy of DC Microgrid Bus Voltage Considering Additional Inertia Adjustment

In DC microgrids, the problem of conflicting bus voltage offset and distribution accuracy exists. Therefore, this paper proposes a hybrid control strategy for DC microgrid bus voltage considering additional inertia adjustment. The control strategy combines the step-down control and the additional control, where the step-down control is used to realize the DC bus voltage unification, and the additional control provides inertia to the microgrid to improve the dynamic response capability. Finally, the proposed strategy is validated by MATLAB/Simulink simulation. The results show that the novel control is able to improve the current distribution accuracy degree while ensuring the bus voltage fluctuation, and at the same time effectively improve the stability of the DC microgrid in a wider impedance range.
The Way to Analyse MD Simulation Results of Cluster Ion Bombardment

Molecular Dynamics (MD) is a simulation method commonly employed in scientific research, specifically in the field of solid state physics. In particular, MD is a valuable tool for analysing rapid processes that take place on the surface, such as the stopping and fragmentation of accelerated cluster ions, sputtering of the target material and the formation of crater and rim. These phenomena necessitate the analysis of substantial amounts of data, encompassing the velocity, coordinates, and energies of each atom in the simulation. In this contribution, we propose scalable and automated methods to analyse size of impact crater, rim characteristics, surface deformation and the energy and directions of sputtered particles. To illustrate the efficacy of these methods, we present results obtained during MD simulation of (100) Si crystal surface bombardment with accelerated C60 molecules.