Miracle-dz/UniFAR-FTUs huggingface dataset:
Event-Triggered Time-Varying Group Formation Control for Heterogeneous Multi-Agent Systems Under Jointly Connected Topologies

This paper investigates the problem of time-varying group formation for heterogeneous linear multi-agent systems (MASs) under joint switching topologies. In order to accomplish complicated coordination tasks, the MAS is divided into multiple groups, each consisting of a leader and multiple followers. The leader is responsible for providing trajectory as a reference for movement, while the followers form a certain formation around the leader within their respective groups. To address the situation of incomplete information availability of agents, compensators and observers are designed in this study. On this basis, an event-triggered formation control protocol is proposed for each follower to ensure that the output tracking error converges to zero. Finally, the effectiveness of the theoretical results is verified through a numerical simulation.
Phenomenology of Retained refractoriness: on Semi-Memristive discrete Media

We study two-dimensional cellular automata, each cell takes three states: resting, excited and refractory. A resting cell excites if number of excited neighbours lies in a certain interval (excitation interval). An excited cell become refractory independently on states of its neighbours. A refractory cell returns to a resting state only if the number of excited neighbours belong to recovery interval. The model is an excitable cellular automaton abstraction of a spatially extended semi-memristive medium where a cell's resting state symbolises low-resistance and refractory state high-resistance. The medium is semi-memristive because only transition from high- to low-resistance is controlled by density of local excitation. We present phenomenological classification of the automata behaviour for all possible excitation intervals and recovery intervals. We describe eleven classes of cellular automata with retained refractoriness based on criteria of space-filling ratio, morphological and generative diversity, and types of travelling localisations.
Structure Guided Unsupervised Condition Monitoring of Wind Turbine Pitch System: A Deep One-Class Classification Approach

Currently, the operation and maintenance of wind turbine (WT) pitch systems have gained increasing attention and data-driven monitoring approaches have been widely studied. However, existing data-driven methods often cause false positives and alarms due to their purely data-driven nature. To further improve the monitoring performance, we proposed a deep one-class classification network model named Pitch-OC to detect early faults with the available supervisory control and data acquisition (SCADA) data. The training of the Pitch-OC model does not require data labels but only learns the WT pitch system health behavior characteristics. Pitch-OC integrates the prior knowledge of the pitch system structure. Specifically, we design a structure-guided group learning layer to combine the prior knowledge of the pitch structure and further capture the complex intervariable relationship in a group manner. Then, we map the hypersphere aggregation layer to obtain a hypersphere model. This hypersphere model is used to monitor the pitch system online. Our experiments on the SCADA data of two WTs prove that our model outperforms existing methods.
Applying Design of Experiments in Testing and Validation of Statistical Software

Validating statistical software involves a variety of challenges. Of these, the most difficult is the selection of an effective set of test cases, sometimes referred to as the “test case selection problem”. To further complicate matters, for many statistical applications, development and validation are done by individuals who often have limited time to validate their application and may not have formal training in software validation techniques. As a result, it is imperative that the adopted validation method is efficient, as well as effective, and it should also be one that can be easily understood by individuals not trained in software validation techniques. As it turns out, the test case selection problem can be thought of as a design of experiments (DOE) problem.
Estudio in vitro del diagnóstico de lesiones de caries dental oclusal utilizando la técnica radiográfica bitewing digital (sistema de placas de fósforo) y tomografía computarizada de haz cónico (Cone Beam)

Objective: To compare occlusal caries lesions diagnosis using photostimulable phosphor plates (PSP) (bitewing technique) and cone-beam computed tomography (CBCT). Materials and methods: 108 teeth (50 molars and 58 bicuspids) were evaluated with CBCT (I-cat pictures Sciences ®) and PSP (phosphor plates Dürr Dental System). These images were classified by two observers according to the occlusal caries type in R0, R1, R2, R3, R4. The discrepancy in the diagnosis was calculated using Chi-square test corrected by Yates. was cone-beam computed tomography used as the gold standard for further analysis. Results: The concordance of occlusal caries lesions diagnosis with both modalities was noted where a statistically significant difference were found in both techniques (p <0.001). According to the occlusal caries lesions diagnosis a total of 76 (70.4%) healthy occlusal surfaces were obtained with photostimulable phosphor plates and 53 (49.1%) using cone-beam computed tomography respectively. None R1 occlusal caries lesions on molars and bicuspids were diagnosed with photostimulable phosphor plates and cone-beam computed tomography zero (0%) detectable lesion were found. From occlusal assessed for R2 caries lesions detection, zero (0%) was found in photostimulable phosphor plates surfaces and 13 (12%) were diagnosed using cone-beam computed tomography. On R3 occlusal caries lesions were diagnosed in 21 (19.4%) and 28 (25.9%)cone-beam computed tomography (25.9%) for and photostimulable phosphor plates in 11 (10.2%)and 14 (13%) for R4 respectively. Conclusions: We found little concordance on radiographic diagnoses with photo stimulable phosphor plates in deep occlusal dental caries lesions compared to CBCT.
Enhanced Social Media Profile Authenticity Detection Using Machine Learning Models and Artificial Neural Networks

Fake engagement is one of the main issues with online networks or ONSs, which are used to artificially boost an account's popularity, this study examines the effectiveness of seven sophisticated Machine Learning Algorithms, Random Forest Classifier, Decision Tree Classifier, XGBoost, LightGBM, Extra Trees Classifier, and SVM, and got 93% accuracy in Decision Tree Classifier. In order to solve overfitting issues and improve model resilience, the paper proposes Generative Adversarial Networks (GANs) and uses K-Fold Cross-Validation. Furthermore, design a Gan-ANN model that combines Batch Normalization and Artificial Neural Networks (ANN) with GAN-generated synthetic data is investigated. The enhanced dataset seeks to strengthen model performance and generalization when combined with cutting-edge modeling methods. This study aims to improve model scalability, predictive accuracy, and dependability across different machine learning paradigms.
Achieving Stable Through-Silicon Via (TSV) Capacitance with Oxide Fixed Charge

Through-silicon via (TSV) is an important enabler for future 3-D integration of integrated circuits. TSV typically contains a high-aspect-ratio metal via embedded in silicon and electrically isolated from the silicon by a layer of dielectric liner hence forming a metal-oxide-semiconductor structure. The parasitic capacitance introduced by TSV must be kept as low as possible for low latency signal transmission. It is also equally important to ensure that the capacitance within the operating voltage is stable. It is shown that careful process tuning can induce the appropriate oxide fixed charge (|Qf| ~ 8.4 × 1011 cm-2) in order to shift the CV curve such that the TSV capacitance is kept stable at the value of accumulation capacitance (Cox) within the operating voltage range of interest (~0-5 V).
Internet of Things Assisted Learning Strategy based Effectual Parking System

To manage data, parking systems now depend on a centralized gateway solution (i.e., the cloud), which creates various possible failure modes, including data loss, extensive delays, and multiple points of failure. Furthermore, features that do not improve parking utilization and revenue are considered throughout the parking space selection process. Because a steady price point is chosen, the pricing strategy is not particularly lucrative. We describe a cloud-based parking management platform that makes use of IoT devices in this research. The proposed Smart Parking system employs Internet of Things (IoT) modules installed regionally to monitor and inform users to the availability of parking spots. A user may also use a mobile app to enquire about parking space availability and book a place. In addition, the paper gives an overview of the system architecture. The paper finishes with a description of system functioning provided as a use case, demonstrating the effectiveness of the proposed paradigm.
Distributed Time-Varying Nash Equilibrium Algorithm Based on Prediction-Correction Technique with Set Constraints

In this paper, a class of time-varying noncooperative game problems with set constraints is investigated. A distributed time-varying Nash equilibrium seeking algorithm based on the prediction-correction technique is designed to track the time-varying Nash equilibrium, in which the prediction term based on the Newton’s method is employed to offset the changes of the Nash equilibrium and the correction term enhances the convergence performance. The projection operator is also introduced to settle the set constraints. It is theoretically demonstrated that the proposed algorithm can be tracked within the bounded neighborhood of the time-varying Nash equilibrium under the undirected connectivity graph when appropriate parameters are chosen. Finally numerical simulations validate the effectiveness of the algorithm by applying it to solve the formation tracking problem of a multi-USV system.
A tour of DDoS attack using Machine Learning Techniques under one umbrella

A distributed denial of service (DDoS) attempt is a sort of cyber-attack in which the perpetrator attempts to starve a network/server of services by flooding the network/server with unnecessary requests, rendering it unable to process valid requests. This attack is the most dangerous in the security domain because this attack denies the availability of the service. The false requests are done by a hacker using the bots army which makes the system busy and service unavailable for the genuine user. Bots can be devices that are connected to the internet i.e., Webcam, mobiles, laptops smart citv, etc. As these cyberattacks grow more common, it is becoming more difficult to identify them and protect online businesses against them. Every day, the rate of cybercrime is increasing. Many sorts of cyberattacks are becoming increasingly widespread, including ransomware, phishing, and DOS / DDOS attacks. Researchers are working hard to figure out how to defend against these attacks. Machine Learning contributes to training models to identify attacks and prevent them from causing maximum harm. Many machine learning algorithms for detecting DoS/DDoS threats are covered in this research. As discussed in recent years' big cyber-attacks and their peak traffic. As this paper discuses distinct datasets that are widely used by researchers and their accuracy which contribute for the further study. This paper concludes with the insights of the latest datasets to combat the DDoS attacks and also reveal the analytical approaches for the detection of DDoS attacks under one sight to ease the detection or identification.
A Two-Stage Receiver for MIMO Systems

This paper considers a receiver for multiple-input multiple-output systems. To improve the error performance of a receiver with reasonable computational complexity, we propose a two-stage receiver. The proposed receiver partially detects signals with a linear receiver, and the remaining signals are detected by a maximum likelihood receiver. Moreover, the selection criteria for signals to be detected by a linear receiver are presented. The simulation results show that the proposed receiver outperforms the conventional linear receiver.
Perception of pathogenic or beneficial bacteria and their evasion of host immunity: pattern recognition receptors in the frontline

Plants are continuously monitoring the presence of microorganisms to establish an adapted response. Plants commonly use pattern recognition receptors (PRRs) to perceive microbe- or pathogen-associated molecular patterns (MAMPs/PAMPs) which are microorganism molecular signatures. Located at the plant plasma membrane, the PRRs are generally receptor-like kinases (RLKs) or receptor-like proteins (RLPs). MAMP detection will lead to the establishment of a plant defense program called MAMP-triggered immunity (MTI). In this review, we overview the RLKs and RLPs that assure early recognition and control of pathogenic or beneficial bacteria. We also highlight the crucial function of PRRs during plant-microbe interactions, with a special emphasis on the receptors of the bacterial flagellin and peptidoglycan. In addition, we discuss the multiple strategies used by bacteria to evade PRR-mediated recognition.
What do benthic δ13C and δ18O data tell us about Atlantic circulation during Heinrich Stadial 1

Approximately synchronous with the onset of Heinrich Stadial 1 (HS1), δ13C decreased throughout most of the upper (~1000–2500 m) Atlantic, and at some deeper North Atlantic sites. This early deglacial δ13C decrease has been alternatively attributed to a reduced fraction of high-δ13C North Atlantic Deep Water (NADW) or to a decrease in the NADW δ13C source value. Here we present new benthic δ18O and δ13C records from three relatively shallow (~1450–1650 m) subpolar Northeast Atlantic cores. With published data from other cores, these data form a depth transect (~1200–3900 m) in the subpolar Northeast Atlantic. We compare Last Glacial Maximum (LGM) and HS1 data from this transect with data from a depth transect of cores from the Brazil Margin. The largest LGM-to-HS1 decreases in both benthic δ13C and δ18O occurred in upper waters containing the highest NADW fraction during the LGM. We show that the δ13C decrease can be explained entirely by a lower NADW δ13C source value, entirely by a decrease in the proportion of NADW relative to Southern Ocean Water, or by a combination of these mechanisms. However, building on insights from model simulations, we hypothesize that reduced ventilation due to a weakened but still active Atlantic Meridional Overturning Circulation also contributed to the low δ13C values in the upper North Atlantic. We suggest that the benthic δ18O gradients above ~2300 m at both core transects indicate the depth to which heat and North Atlantic deglacial freshwater had mixed into the subsurface ocean by early HS1.
Brands Logo Recognition with Semi-Supervised Learning Using a Robust YOLOv8 Detection Model

Semi-supervised object recognition has emerged as a prominent area of research within computer vision. It offers the potential to significantly decrease the necessity for costly bounding-box annotations. Although there has been considerable success, the current advancements primarily concentrate on two-stage detection networks such as Faster RCNN, while study pertaining to single-stage detectors receives limited attention. This paper centers its attention on semi-supervised learning applied to the advanced and widely adopted single-stage detection network YOLOv8. Our method uses only a minimal amount of labeled data to successfully carry out the training, where we have explored various approaches like data augmentation, student-teacher network, pseudo labeling, and transfer learning. Furthermore, we have refined YOLOv8 implementation to optimize the advantages offered by semi-supervised learning. To validate our approach, we performed extensive experiments on the challenging OpenLogo Dataset, which contains 27,000 images across a total of 352 classes. The results were obtained with a limited amount of labeled data and a substantial amount of unlabeled data.
Leak Detection and Classification of Water Pipeline Data Using LSTM Auto-Encoder with Xavier Initialization

Modern cities operate extensive water supply networks. In Korea, the water supply network coverage reaches 98.9%, but more than 10% of water is still leaking every year. Previously, leaks in water pipelines were detected using flow meters and pressure meters, but these methods could only detect leaks after they occurred, necessitating on-site investigations to accurately pinpoint the location of the leak. In the United States, aging water and sewage facilities incur economic losses of hundreds of billions of dollars every year. To address this issue, this paper proposes an LSTM (Long Short-Term Memory) Auto-Encoder model, enhanced with Xavier Initialization, that can detect leaks using water pipeline data and automatically identify the location and extent of the leakage. This model achieved 93.7% performance by applying the Xavier Initialization technique to the LSTM Auto-Encoder model and achieved relatively high accuracy compared to the model using only LSTM Auto-Encoder. The results of this paper can serve as an effective predictive tool for monitoring water pipeline leaks.
Contributions of the Individual Differences Approach to Cognitive Aging

Objective To review selected research in cognitive aging incorporating an individual differences approach. Method Three contributions of the individual differences perspective in cognitive aging are illustrated with data from the Virginia Cognitive Aging Project. Results Research capitalizing on the variability among individuals has been used to: (a) improve sensitivity and validity of measurement of cognitive functioning, and evaluate possible age differences in the meaning of the measures; (b) investigate relations between age and individual cognitive measures in the context of other types of cognitive measures; and (c) examine the degree to which age-related influences on target measures are statistically independent of age-related influences on other cognitive measures. Discussion Although the primary focus of much of the research in cognitive aging has been on mean differences between people of different ages, people differ in many respects besides age. A fundamental assumption of the individual differences perspective is that at least some of those differences may be informative about the nature, and causes, of the relations between age and cognitive functioning.
EHT-SR: An Entropy-Based Hybrid Approach for Faster Super-Resolution

Deep neural networks have produced tremendous advancements in super-resolution (SR) results. These improvements often come at the cost of inference latency, which is particularly important in low-resource devices. This paper therefore proposes a novel method to optimize the inference latency of SR models, called EHT-SR (Entropy-Based Hybrid Tiled SR), which leverages both accurate but slow DNN-based methods and a simple but fast bicubic interpolation for super-resolution. Particularly, we observe that a lightweight bicubic interpolation can still provide good image super-resolution quality for selected regions of the input image. An entropy-based heuristic, which we derive from a rigorous analysis of the bicubic interpolation, allows to select the best tiles in the input image that can be super-resolved using bicubic interpolation, while the remaining tiles are processed using a DNN-based SR method. This approach allows us to consistently speed-up the SR inference latency with only minimal degradation in image quality. Extensive evaluation results with different SR baselines and datasets show how our EHT-SR approach can speed-up inference by up to 27% and 39% on GPU and CPU platforms, respectively, without negatively impacting the quality of the super-resolved content.
A Low-cost and Efficient Fuzzy logic MPPT Technique based Current Sensor-less Strategy for Solar Battery Charging

The objective of this study is to develop a hybrid technique for maximum power point tracking (MPPT) in photovoltaic (PV) battery charging, which does not require a current sensor. The research utilizes a cost-effective and efficient MPPT controller that eliminates the need for a single sensor in the control circuit. Additionally, a hybrid MPPT approach is employed by integrating a fuzzy logic (FL) method-based MPPT with a single sensor incremental conductance (INC) technique. The goal of this method is to minimize the power oscillation via smoothing the change in the duty ratio. The proposed methodology is verified through simulation using MATLAB/Simulink. The results obtained from the simulation demonstrate that the proposed method achieves a high efficiency of 96.6%, a fast convergence speed of 0.001 seconds, and minimal power oscillation of 0.5 watts.
Contextual Learning for Missing Speech Automatic Speech Recognition

In this paper, we present an automatic speech recognition (ASR) system that is capable of decoding complete transcriptions from speech even in cases where there are missing segments in the audio. To predict complete transcriptions from speech that may have missing segments, we utilize a contextual learning approach inspired by recent language model training approaches, in which our model leverages surrounding speech segments as cues for the prediction. Our model consists of two modules: a contextual feature extractor designed with the structure of wav2vec 2.0, and a projection layer. We further explore various masking lengths for model training so as to optimally benefit the ASR system without compromising its performance. Our proposed methodology demonstrates high-quality ASR performance on missing speech segments of various lengths, ranging from a word error rate (WER) of 4.7% on 0.25 seconds segments to 18.5% on 1 second segments.
Artificial Intelligence Driven Intelligent Computational Model for Heart Disease Prediction: Leveraging Feature Selection

In recent years, heart disease has become a very serious threat to the health and safety of people all over the globe. Typically, this condition occurs when there is an insufficient supply of blood from the heart to various parts of the body, which hampers their usual operations. Early and timely detection of this disease holds paramount importance in preventing patients from further harm and saving their lives. Artificial intelligence (AI) has emerged as a pivotal tool in advancing heart disease prediction through its multifaceted roles. In this study, an intelligent computational model is introduced. This intelligent computational model encompasses multiple stages, including comprehensive data preprocessing and a strategic feature selection process utilizing correlation-based techniques. It also utilizes machine learning and deep neural networks to obtain a robust model for heart disease classification. Serval performance metrics are evaluated to observe the effectiveness of the proposed model. The proposed model achieved the highest classification accuracy of 99.01%. The proposed model contributes a powerful predictive model aimed at enhancing heart disease diagnosis, an imperative step toward effective patient care and potentially life-saving interventions.