Miracle-dz/UniFAR-FTUs huggingface dataset:
FLS-based cooperative fault-tolerant consensus control for non-affine nonlinear multi-agent systems with switching topologies

In this artical, the problem of fuzzy fault-tolerant consensus for non-affine nonlinear (NAN) multi-agent systems (MASs) with dead-zone and switching topologies is investigated. First, the non-affine term in system is transformed into an affine term, which can be realized by using implicit theorem and mean value theorem. Then, based on the switching undirected communication network, a fuzzy fault-tolerant consensus protocol (FFTCP) with adaptive update law with adjustable parameter is designed utilizing local state information of adjacent agents. Besides, it is demonstrated that all closed-loop signals are bounded, and all agents can reach a consensus when there are the actuator failures and dead-zone nonlinearity are modeled. Eventually, the numerical example is provided to testify the availability of the consensus algorithm.
An X- and Ku-band 7 bit Active Differential Phase Shifter

In this paper, an X- and Ku-band 7bit digital active differential phase shifter with current vector-sum is proposed to achieve high resolution and low error. The phase shifter is implemented in 0. 13μm SiGe BiCMOS process. Two orthogonal vector signals are generated by a quadrature all-pass filter (QAF) network with resistance compensation, and the vector modulator is composed of two Gilbert cell array circuits with current-steering. In addition, digital control signals are designed with redundant bits to enhance accuracy. The simulation results demonstrate that within the frequency range of 8 ~ 16 GHz, the phase shifter exhibits a RMS phase error of less than 2° for 128 states. Additionally, the RMS amplitude error is less than 0.6 dB, the gain is maintained between -1.5 dB and 1 dB, the input return loss is less than -10 dB, and the output return loss is less than -9 dB.
Effective Malicious PowerShell Scripts Detection Using DistilBERT

Malicious PowerShell scripts have become a prominent vector for cyberattacks, necessitating the development of efficient detection methods to mitigate associated risks. This research paper presents an innovative approach for the detection of malicious PowerShell scripts using the DistilBERT model, a distilled variant of the BERT architecture. Leveraging the power of transformer-based language models, our method capitalizes on the inherent ability of DistilBERT to capture intricate contextual and semantic information within scripts. Through extensive experimentation on a unique dataset containing diverse malicious and benign scripts, we demonstrate the effectiveness of our approach. Our model achieves superior accuracy, precision, and recall, while also maintaining a low false positive rate. This study not only showcases the potential of large language models for cybersecurity tasks but also contributes a robust solution to the real-world challenge of malicious script detection, paving the way for enhanced cybersecurity measures.
On the Necessity, Security Requirements and Performance Requirements of Digital Signature Co-processors in Li-ion BMSs

Modern lithium-ion (Li-ion) batteries integrate microcontrollers and dedicated resources for monitoring and control distributed in different physical layers. The overall system is called Battery Management System (BMS) and aims to estimate, for instance, the state of charge and the state of health maximizing the life and reliability of batteries. Anyway, BMSs can be threatened by security issues and attacks. Counterfeiting of components and data manipulation can alter the functionalities of the BMS, causing several consequences, such as the explosion of batteries due to the overcharge. In recent work, we proposed a novel and robust security approach for the integrity and authentication of BMS data and anti-counterfeiting mechanisms. In addition, it can be used to improve the performance (by hardware acceleration) and the security level (with advanced features such as secure storage and isolation of security-critical assets) of other solutions proposed in the literature, which otherwise might be compromised. For this purpose, the integration of a co-processor for the digital signature on elliptic curves (ECDSA) becomes fundamental. In this work, we demonstrate the necessity of integrating such a cryptographic co-processor, and we define its requirements in terms of both security and performance. Moreover, we confirm and extend the analysis related to the scaling of the characteristics (resources, frequency, latency) of ECDSA modules according to the internal data path. To the best of our knowledge, this is the first work in the literature addressing this topic for Li-ion BMS.
Parameter Degradation Features Selection Based on Multiple Evaluation Index in a DC-DC Converter

DC-DC converters play an important role in electrical systems. Its fault state has a major impact on the operation of the back-end components and the entire electrical system. Parametric degeneration diagnosis is essential which can decrease the likelihood of serious faults occurring, enormous economic loss, and severe injury. To effectively and rapidly diagnose the parametric fault in DC-DC converters, a sensitive fault features extraction and selection method is proposed. Firstly, statistical features in the time domain and wavelet energy and entropy values in the time-frequency domain of the output voltage and the input current were collected for each type of fault. Then, the multiple evaluation indexes method is used to select the most sensitive fault features. Finally, simulation results validate the practicability and effectiveness of the proposed method.
Odonata survey in Central and Western Bhutan covering eight Dzongkhags (Districts): An annotated species list with nine new records

122 specimens have been collected spreading to 46 species under 32 genera and 11 families from different parts of central and western Bhutan during August 13 to 23, 2013. Nine species and subspecies of them are the new records for Bhutan. Geographical position and collection details are provided for each species which are supplemented by abdominal length, hind wing length and some other identifying characters for the new records. After the present study a total of 84 species and subspecies of odonata are known to occur in Bhutan.
Prediction of heart disease using machine learning and hybrid methods

Heart disease is a major cause of death worldwide, and early diagnosis and treatment can significantly improve patient outcomes. Machine learning has been shown to be a promising tool for heart disease prediction, but existing methods have limitations such as low accuracy and high computational complexity. In this research, we investigate a novel method for heart disease estimate using various machine learning. Our method is based on a combination of deep learning and ensemble learning techniques. We first use a deep learning model to extract features from the patient data. These features are then used to train an ensemble learning model, which makes the final prediction. We evaluated our method on a publicly available dataset of heart disease patients. Our method achieved an accuracy of 94%, significantly higher than the existing methods' accuracy. Our method is also more computationally efficient than existing methods, making it suitable for use in real-world applications. We believe our method can potentially improve the early detection and treatment of heart disease. We plan to evaluate our method on a larger dataset further and explore the use of our method in clinical settings.
Domain Knowledge Enhanced BERT for Chinese Named Entity Recognition

Digitalization of educational resources and revolutionizing knowledge frameworks are essential in achieving smart education. Knowledge graphs play a pivotal role in addressing knowledge representation, correlation, and sharing within digital education. Named Entity Recognition (NER) is a fundamental task in constructing knowledge graphs. This study introduces a Domain Knowledge-Enhanced BERT Chinese NER model, DK-BERT-CRF (Domain Knowledge BERT CRF), to address the deficiency of lexical information features within the Chinese NER task using the BERT pre-trained model. To construct an automated labeling dataset, we perform an automated labeling dataset construction based on ChatGPT, focusing on the example of computer science's data structures. We conduct experiments and evaluations using this dataset and the general CLUENER2020 dataset. Comparative experiments with BERT+CRF and BiLSTM+CRF are also conducted. The experimental results demonstrate that the DK-BERT-CRF model, enriched with domain knowledge, exhibits an improved F1 score compared to the other two models. Particularly, the DK-BERT-CRF model showcases enhanced F1 scores on the computer science data structure dataset after the incorporation of domain knowledge.
Nonlinear filtering based on lattice trajectory piecewise linear approximation with application to a wastewater treatment plant

State estimation is a vital part of state-feedback controller design. The extended Kalman filter (EKF) approximates the original nonlinear system through successive linearization. The linearization points are selected at each sample instant, which is computational complex and may not reflect the overall trend of the nonlinear system. In this paper, we propose a nonlinear filtering method based on lattice trajectory piecewise linear (LTPWL) approximation, named LTPWL-KF, in which the nonlinear system is approximated by the LTPWL model offline, and the online state estimation is then based on the constructed piecewise linear (PWL) system. The boundedness of the variances of the estimation error is proved. A simulation study on a wastewater treatment plant (WWTP) is performed. The results show that the estimation performance of LTPWL-KF is comparable with that of EKF, and the online computational burden of LTPWL-KF is less.
Assessment of the South African sardine resource using data from 1984-2015: Results at the joint posterior mode for the two mixing-stock hypothesis.

The Operating Model (OM) for the South African sardine resource has been updated from that used to develop OMP-14 given four more years of data. The model has been altered from previous assessments to now model infection of sardine by a “tetracotyle”-type metacercarian parasite, and includes data on this parasite prevalence-by-length in the likelihood. A Hockey-Stick stock recruitment relationship is used for this OM, with a different median recruitment and higher variability estimated for the west stock during “peak years”. Time-invariant natural mortality is assumed to be 1.0 year-1 for juveniles and 0.8 year-1 for adults as before. The total resource abundance is estimated to be 688 thousand tons in November 2015, with the west stock consisting of 142 thousand tons and the south stock consisting of 546 thousand tons. These biomasses are well below the long term average of 1 039 and 492 thousand tons for the total resource and west stock respectively, and near the long term average of 547 thousand tons for the south stock. The west stock has experienced below average recruitment in 11 out of the 12 most recent years.
Evaluating Acoustic Parameters for DeepFake Audio Identification

The progress made in the field of machine learning applied to signal processing offers interesting perspectives in terms of technological evolution but also causes some troubles in terms of ethics and security. For example, we are witnessing the emergence of audio deepFakes used to orchestrate scams. However, although the tools used in the generation of these deepFake audios show good results which can sometimes produce audios that seem to be confused with real audio, it is not impossible to dissect them. In order to detect them, many methods exist, in particular the analysis of the acoustic parameters which can attest to the authenticity of an audio extract. These parameters include energy, power, pitch, signal spectrum, cepstral coefficients, etc. However, these acoustic parameters are numerous and not all of them are suitable for detecting deepFake audio. This paper presents a comparative review of acoustic parameters useful in detecting DeepFake audio. Among them, we highlight the relevance of the study of cepstral parameters such as MFCC compared to other acoustic parameters such as mel-spectograms. The objective is to provide reliable leads in the detection of deepFake audio.
Common Ground Single-Phase Single-Stage Transformerless Inverter Five Levels Using Reduced Components and Switched Capacitor Cell

A new transformer-less inverter (TLI) topology for photovoltaic (PV) applications is presented in this study. The common ground topology (CGT) serves as its foundation. One of the main problems, the leakage current (LC), can naturally be reduced by CGC TLI. This TLI provides a five-level dual gain output voltage. It uses seven switches and a switched capacitor (SC) for output voltage generation. Boosting features of the inverter make it appropriate for grid integration of lower PV voltages than the grid voltage. The appropriate charging and discharging of SCs allow it to self-balance themselves. Level shift pulse width modulation (LS-PWM) controls the inverter operation. Proper thermal modelling is carried out to get the power loss analysis and estimate the efficiency of the proposed inverter is 97.5% in SIMBA power electronics. Additionally, comparison investigations and component selection processes are completed. MATLAB/Simulink is used to confirm the topology’s performance in further detail and later experimentally validated to prove the applicability and feasibility for PV applications.
Disease Detection Based on Iris Recognition

Disease detection using iris recognition technology has emerged as a transformative paradigm in the realm of healthcare diagnostics. Leveraging the intricate patterns within the iris, this study explores the development and evaluation of a sophisticated machine learning model for disease identification. Through meticulous pre-processing and feature extraction, the iris patterns are translated into a comprehensive set of data points. Utilizing state-of-the-art machine learning algorithms, the model achieves a remarkable accuracy rate, revolutionizing the precision of disease diagnostics. Ethical considerations play a pivotal role in this research, with a strong emphasis on patient privacy and algorithmic fairness. Rigorous anonymization protocols and bias-mitigating strategies are integrated, ensuring that patient data is handled responsibly and diagnostic outcomes are equitable across diverse demographic groups. Looking forward, the potential applications of iris recognition in healthcare are vast. From real-time disease detection to secure access control within medical facilities, the technology’s versatility promises transformative shifts in healthcare delivery. Moreover, the integration of iris recognition with telemedicine platforms opens doors for remote diagnostics, bridging healthcare disparities and ensuring accessibility to even the most remote populations.
Real-time plastic litter detection using hyperspectral sensing on drone

A hyperspectral sensing system operating in the SWIR band (900-1700nm) was previously developed for drone-based detection of plastic litter in the environment. The detection software is based on a linear classifier trained on examples, that detects plastics and transmits information to a ground station that visualizes on a map the location of points surveyed, labeled as plastics where applicable. In this paper, we report on implementation of plastics litter detection in real time, while the drone is flying.
Unsupervised Domain Adaptation for One-Stage Detector in Remote Sensing Imagery

We propose a novel domain-adaptive one-stage framework to address the unsupervised domain adaptation for object detection in remote sensing. To benefit from the inherent multiscale representations of the one-stage detector, several domain discriminators are used at different scales to help the model generate domain-invariant features for objects of different sizes. Moreover, we implicitly learn the invariant features from image-level/global to instance-level/local by exploiting a self-attention mechanism that allows the network to gradually recognize local regions that are crucial for detection and adaptation. The experimental results on various remote sensing datasets confirm the effectiveness of the proposed framework. Compared to the baseline model trained on the source dataset, our method consistently improves the detection performance on the target dataset by 4.5%-11.6% mAP under two domain adaptation scenarios and achieves new state-of-the-art performance.
Interaction between Machine Learning and Maintenance Planning for Wind Energy Systems: A Theoretical Review and Analysis

Maintenance planning for wind energy systems attempts to reduce downtimes under operating conditions that could leave possible damages ranging from loss of reputation to financial and potential losses of life. Today, with advanced sensing technologies, machine learning plays an important role and becoming dominant in the field of condition monitoring (i.e. detection, diagnosis, and prognosis) while assessing health conditions timely. By using specific methods according to specific scheduling criteria, results of condition monitoring systems are considered very useful in predicting the required maintenance tasks and their appropriate timing. More simply, maintenance schedule is constructed by studying the interactions between machine learning predictions and maintenance strategies. To the best of authors’ knowledge, there are no theoretical studies including specific review papers made so far introducing such a topic. Against this background, this paper is dedicated to exploring related review papers from a general perspective, analyzing research gaps, and providing insights required to craft a future comprehensive review paper specifically dedicated to exploring such a subject. More attention is paid to the theoretical point of view, as it tends to pave the way for future research on the subject. The related review papers are therefore analyzed based on how machine learning and maintenance planning are viewed from the perspective of condition monitoring and task scheduling. Examination of the results reveals important gaps in research, while suggesting future possibilities for practical, and effective comprehensive review study.
Cardiovascular Disease Prediction Using Extreme Gradient Boosting Algorithm

Cardiovascular disease prediction using machine learning involves algorithms that can accurately predict an individual’s risk of heart disease based on their medical history and other risk factors. The current paper introduces a basic effective system designed to diagnose heart disease, which utilizes the Extreme Gradient Boosting technique using a feature selection technique. The system can predict heart disease accurately with a large amount of data. To ensure the effectiveness of the classifier, proper hyperparameter tuning is critical. In this regard, we employed random search, an ideal approach for hyper-parameter optimization, to improve the hyper-parameters of XGBoost. Moreover, the model testing technique called cross-validation was employed to check the efficiency of the prediction of the forecast model. To analyze the proposed algorithm, the researchers compared it to other algorithms. The proposed model has an area under the ROC score of 0.98 and an accuracy of 98 %, outperforming KNN, Naïve Bayes, SVM, Random Forest algorithm in terms of accuracy, positive and negative rates of the model according to the experimental results.
Knowledge Sharing and Innovation Capability in a Telecommunications Company in the Kingdom of Bahrain

This study investigated the relationship between individual, organizational, and information and communication technology (ICT) factors as enablers of knowledge-sharing processes and firm innovation capability (IC) for a telecommunications company in Bahrain. The study used simple random answers from 77 employees’ questionnaires and the partial least squares (PLS) structural equation modeling method. The results indicated that enjoyment of helping others significantly correlated with knowledge collection. Top management support was strongly associated with knowledge donation, which was also strongly linked to a company’s ability to innovate. Regarding the study’s implications, the company must change and add knowledge-sharing mechanisms to inspire innovation.
Implementation of Hummingbird E203 Based Electric DC Motor Controller

Against the size issue that DC motor controller uses multiple chips to implement, the chiplized effort is made based on FPGA in this paper. RISC-V ISA based HE203 (Hummingbird E203) is as the core, in which redundant peripherals are customized. Due to the lack of ADC in HE203, the ADC (analog-to-digital converter) module is added on the FPGA, and the corresponding software driver of the ADC module is developed to control the DC motor. Finally the closed-loop control of the motor is implemented. Work waveforms are simulated in terms of PWM pulses. The experiments show that there are correct control functions.
Influence of Contextual Information on Bengali-English Forward and Backward Transliteration Using Binary Coding

Transliteration of words across different scripts carries importance for both social media platforms and messaging apps. It preserves the phonetic meaning of words across various writing systems. This study evaluates the influence of contextual information within words on the transliteration of Bengali and English language pair. It utilizes the Dakshina and NEWS 2018 datasets and binary encoding for feature representation. With the random forest classifier, the results demonstrate that using neighboring transliteration units as context yields the best performance on both datasets, obtaining the highest accuracy of 81.04%. These findings highlight the vital role of contextual information within words in enhancing the performance of transliteration.
Enhancing Transportation Mode Prediction from GPS Trajectories using Machine Learning and Feature Engineering

This manuscript proposes a comprehensive framework for the automated determination of travel modes based solely on GPS trajectories. To improve prediction accuracy, additional preprocessing features are introduced, including speed, acceleration, jerk, and bearing rate. Our approach employs various machine learning techniques, such as Random Forest, Multilayer Perceptron (MLP), AdaBoost Classifier, Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), to achieve notable classification results. Extensive evaluations demonstrate that our framework surpasses existing state-of-the-art algorithms for transport mode prediction. This investigation presents a promising approach for accurate and reliable prediction of travel modes, with potential applications in various real-world scenarios.
ICSS: Semantic Segmentation of Remote Sensing Images Based on Image Inpainting and Contrast Self-Supervised

Semantic segmentation of remotely-sensed images is a fundamental problem for remote sensing research. However, unlike natural images, remote sensing images cover a larger surface area, contain more complex object categories, while often have a similar visual features across different categories. These characteristics pose new challenges for semantic segmentation. To address the complexity of semantic segmentation in remote sensing images, we have proposed a self-supervised method called ICSS (Inpainting and Contrast Self-Supervised) to extract image information and improve the generalization ability of the segmentation model. Specifically, we utilize self-supervised method to train remote sensing images and obtain pre-trained weights that can adapt to the unique features of these images. These pre-trained weights are then applied to the semantic segmentation task. Our experiments on the Potsdam dataset demonstrate that the self-supervised method effectively enhances the accuracy of semantic segmentation.