Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrids of K-means and linkage algorithms

Over the past two decades, there has been a growing interest in delving deep into clustering algorithms, primarily driven by the user’s dilemma. This surge in interest can be traced back to Kleinberg’s Impossibility Theorem for Clustering, [1], which examined the ability of clustering algorithms to meet a series of specific properties aimed at evaluating their robustness and versatility. The overall goal of these studies has been that of classifying clustering algorithms based on their capacity to handle perturbations in their internal parameters effectively and their ability to generate any conceivable partition of a given dataset. However, these studies typically evaluate the algorithms independently, without assessing whether a second algorithm can replicate the results of a given first algorithm under identical conditions (e.g., using the same dissimilarity matrix). This paper aims to compare the behavior of clustering algorithms by addressing the question: What adjustments are necessary to the internal parameters of a clustering algorithm A for this algorithm to be able to replicate the result obtained by a clustering algorithm B without altering the distance matrix? The paper provides necessary and/or sufficient conditions, with no assumptions made on the data generating process, for K-means to reproduce the partition returned by another clustering algorithm while preserving the original distance matrix. The same inquiry is also conducted for linkage-based algorithms. Additionally, the paper introduces a novel class of clustering algorithms called hybrids of K-means and linkage algorithms. These hybrids incorporate a function into K-means to autonomously compute the number of clusters that a linkage-based algorithm would produce for the given data set and distance matrix.
Enhancing Smart Grid Security with XMSS-Based Blockchain Technology

Smart grids are electricity network that can help improve the efficiency, reliability, and security of the power grid. Smart grid uses digital and other advanced technologies to monitor and manage the flow of electricity from generation to consumption It is a technological advancement in the management and delivery of electrical power. But the combination of a variety of IoT gadgets and there are serious security problems with the integration of communication networks into the smart grid infrastructure. The Traditional security methods can easily be broken and it will not be sufficient to safeguard against the modern evolving threats. Quantum resistant smart grids Blockchain is an emerging technology that has the potential to completely change how smart grids function. This study investigates the utilization of the extended Merkle Signature Scheme (XMSS) in combination with using blockchain technology to improve the smart grid’s security. We talk about the benefits of XMSS and how to incorporate it into a blockchain-based design to safeguard smart grid systems from a range of security risks.
Visualizing Health: Virtual Eyewear Try-On Shaping Informed Medical Choices

Incorporating the latest advancements in computer vision and virtual reality (VR) technology, this paper explores the fusion of computer vision and augmented reality, resulting in groundbreaking innovations applicable across various industries. This study introduces an advanced virtual eyewear try-on system that transcends its traditional application in the fashion sector. This cutting-edge system harnesses the power of precise facial and ocular recognition algorithms, sophisticated image manipulation techniques, geometric transformations, and real-time visual rendering. As a result, users are immersed in a lifelike environment, enabling them to virtually experiment with a diverse range of eyewear frames. Beyond its notable impact on the online retail experience, this technology opens up exciting possibilities in the realm of healthcare.
Routing Protocols Focussed on Energy-Efficiency for Wireless Sensor Networks: A Comparative Analysis

For dependable surveillance of various surroundings concerning civilian and defense purposes and to cooperate transmit the information they collect across the wireless network to a “Base Station (BS)”, a “Wireless Sensor Network (WSN)” is made up of geographically dispersed self-governing “Sensor Nodes (SNs)”. The efficiency of energy is a major concern for SNs since their batteries aren't able to last a long time. The major problem with WSN networks revolves around the severe drain on energy resources caused by the transmission of data from the source SN to the intended SNs. The aggregate volume of energy used by WSN systems is significantly influenced by their routing methodologies. Due to their fundamental properties, WSN systems need energy-efficient routing approaches. The limited availability of resources in WSNs has rendered it crucial to find ways to extend the network’s useful life. Energy savings and system longevity are currently being enhanced using a technique based on clustering. Several researchers are working on a clustering process that uses minimal energy. This study makes an effort to analyze three relatively new routing protocols, “ADEM”, “DRAC”, and “DMSSS”, which possess the potential to significantly affect the networks' total energy consumption. The metrics "Energy-Efficiency", "Packet-Delivery-Ratio (PDR)", “Throughput”, and "Routing-Overhead" were used to compare the aforementioned protocols. The purpose of this comparative article is to compare and contrast the above protocols currently in use to determine which one is superior in terms of providing higher energy-efficiency.
A novel reduced component sensor based switched capacitor multilevel inverter

This paper proposes a unique reduced component sensor-based topology of capacitor-switched multilevel inverter (CSMLI) which is compatible for low, medium and high voltage application. In this configuration only one voltage source is required. The proposed circuit is a seven-level inverter that uses least number of switches with only one capacitor and one voltage source. Special attention is given to charging of capacitor, eliminating any kind of voltage imbalance or abrupt change in voltage across capacitor with minimum inrush charging current. The design of the circuit is accomplished to have minimum voltage stress across various circuit elements. The operational principle of this inverter and some known modulation strategies are used to evaluate the overall circuit performance. The proposed work is compared with other work of similar type with regards to device count and performance. The simulation with result verification is carried out to validate the proposed circuit.
A Distributed Hyperspectral Target Detection Algorithm Based on Background Reconstruction for Cloud Platforms

Target detection is an important research direction in the field of hyperspectral image interpretation. This paper proposes a Spark-based distributed and parallel implementation of a hyperspectral target detection method that integrates variational autoencoder with constrained energy minimization to detect the residual image. Our proposed method can significantly reduce the computation time in hyperspectral target detection by means of distributed computing and is scalable with the increasing size of hyperspectral data. In addition, we introduce an AUC score-based data pruning (ASDP) strategy as well as its distributed version that facilitates parallel processing in our proposed method. Specifically, we use a data pruning scoring criterion to evaluate the hyperspectral data and remove redundant data with insignificant impact on the final training accuracy. Experimental results on several real-world hyperspectral datasets demonstrate that the distributed and parallel implementation can reduce the total amount of computational data for target detection without accuracy degradation.
Real-time Positioning and Mapping Based on Multi-sensor Fusion for Vehicle System

In the work, we propose a real-time positioning algorithm for vehicle state estimation during localization and mapping process. The method mainly relies on multi-sensor fusion and classical simultaneous localization and mapping (SLAM) technique. A factor map-based optimization framework is firstly designed which tightly couples the measurement produced by LiDAR, IMU and GNSS. New loopback constraints are further added to the back-end optimization problem thereby eliminating cumulative errors. Globally consistent trajectories and mapping results are finally obtained through the proposed method. We demonstrate its better real-time performance and higher localization accuracy compared with existing map-building algorithms by running the algorithm on a Jeston TX2 board.
Music Generation Using RNN-LSTM with GRU

Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are two methods for music production that have been studied by researchers. A lot of LSTM networks have been employed to create character-level musical scores. However, in order to produce pleasing and grammatically accurate sheet music, these LSTM models necessitate a significant amount of training time. As an alternative to LSTM models in this study, we use Gated Recurrent Unit (GRU) networks, which have three gates and do not maintain an internal cell state. Peer evaluations of the resulting music's quality use qualitative criteria. The suggested GRU-LSTM model is compared subjectively with another model without similar techniques.
Robust Frequency Regulation for Dual-Area Interconnected Grids Using Hybrid Controller: A Comparative Study

This paper introduces a robust controller designed for load frequency control in a dual-area integrated power grid. Here, the suggested controller is a blend of a fuzzy logic controller with an integral controller. The parameters of the integral controller and frequency bias settings are optimized to enhance the dynamic response of the proposed controller. To attain this, three distinct optimization algorithms are employed, i.e., genetic algorithm, simulated annealing, and pattern search. A new objective function using settling time, settling-max, and settling-min with a suitable weight factor is used to improve the parameters estimation process. A comparative analysis is performed using the proposed approach and classical-I controller under two cases considering several load disturbance scenarios. Furthermore, the suggested hybrid controller is evaluated and compared with similar models in the literature. As expected, the proposed controller exhibits superior performance.
Design of a 3 Degree-of-Freedom Upper-Limb Active Exoskeleton with Cable-Driven Actuators for Neuromotor Rehabilitation

Exoskeletons are wearable mechanisms which, parallel to the wearer’s joints, modulate movements through active or passive torque application. While existing upper-limb exoskeletons offer benefits in strength augmentation and therapy, they often suffer from limited degrees of freedom and excessive bulk, hindering their utility in neuromotor rehabilitation. This study introduces a three degree-of-freedom active upper-limb exoskeleton optimized for neuromotor rehabilitation. Employing anthropometric data and rigorous kinematic analysis, the design accommodates elbow flexion/extension, forearm pronation/supination, and hand gripping. The upper limb dynamics, kinematics, and physiology were evaluated to provide adequate joint ranges of motion. Cable-driven actuators in the elbow and forearm joints were designed to provide low worn weight through the relocation of the motor units away from the user’s joints. The result is a lightweight, compact exoskeleton that provides optimal conditions for neuromotor rehabilitation. Finally, a preliminary control strategy was tested using myoelectric signals to trigger motion.
Identifying Influential Nodes in Complex System from Multiscale Complex Network Perspective

Modeling complex systems as networks and identifying influential nodes is still an open and challenging issue due to the increasing system scale, complex structure, and dynamic behaviors. A number of methods have been recently proposed to solve this problem but most existing studies have the limitations, and few of them considering multiscale information of the network. In this article, therefore, a novel method to identify influential nodes is proposed, which takes into account three scales attributes, i.e., local, semi-local and global, instead of only a single scale structure attribute. Empirical studies were performed on six Open-source software projects, and the experimental results indicated that the proposed method outperformed traditional baseline approaches. Our findings demonstrate the proposed method is an effective method for identifying influential nodes in complex system, and this study is expected to provide useful insights into smart control of complex system.
Analysis and Development of Electric Vehicle Charging Generator for London Barnet Charging Data

Global warming, air pollution, fossil fuel depletion are the main reasons of the epoch that has called for electrification of the transportation. The expanded reception and utilization of Electric Vehicles has presented novel hurdles for overseeing and fostering the framework required for charging. Arranging a feasible charging framework that can fulfill the different charging prerequisites of EV purchasers as keeping up with ideal asset utilization is one of the serious issues. Analyzing and understanding the charging pattern is the key to planning charging infrastructure. Data analytics can be used to monitor and predict charging behavior. This work focuses on analyzing the real time charging dataset of London Barnet and developing a charging generator based on clustering for the same dataset.
CBAM-DnCNN: An Improved Method For Image Denoising

In modern society, with the wide application of digital images in the fields of social media, medical diagnosis, security monitoring, and automatic driving, the quality of images has become particularly critical for information transfer and decision-making. With the continuous development of artificial intelligence and computer vision, Image denoising techniques are gradually applied to improve image quality. In this paper, an Image denoising method based on a modified DnCNN network (CBAM-DnCNN) is proposed. Our method first convolves the noisy image to extract features, then captures the spatial structure of the image through the Convolutional Block Attention Module, and then feeds it to this DnCNN network for residual learning. In addition, the combination of BCEWithLogitsLoss and MSELoss is used as a loss function during training and the introduction of these methods can improve the quality of the results. This work provides a new method for image noise reduction and proves the effectiveness of the method in experiments.
Ambient Environmental Energy Harvesting for Supercapacitor Driven Battery-less IoT Devices

The research project centres on developing a robust energy harvesting system for IoT devices, emphasizing the potential of green energy technologies. Current findings underscore the dominance of solar panels, highlighting their superior power generation compared to thermoelectric generators and Piezoelectric harvesters. Supercapacitors emerge as effective energy storage, replacing batteries and ensuring consistent power supply to IoT devices during energy harvesting fluctuations. Challenges include optimizing thermal and vibration harvesters, and refining the power management module. Future work aims at reducing component inefficiencies, implementing an advanced control system, and integrating smart sensors for dynamic environmental adaptation. These efforts anticipate elevating the efficiency and sustainability of IoT devices reliant on green energy harvesting technologies.
Framework for Covert Timing Communication Detection

“Covert channels,” methods of transmitting data, leverage existing system resources originally not designed for such purposes, like firewalls, to discreetly convey information. This allows for the disclosure of sensitive and confidential data from one entity to another with lower security permissions, establishing what appears to be a secure communication pathway between them. The ease of communication and information exchange between two parties on a shared network facilitates covert interactions without detection. Consequently, identifying concealed communications becomes a challenging task. Covert Storage Channels (CSC) adheres to network protocols, limiting deviations from a set of rules. In contrast, Covert Timing Channels (CTCs) exhibit stochastic behavior, increasing the difficulty of detection. A potential solution to this challenge is the adoption of a generic technique capable of identifying concealed interactions within network traffic.
Redes alimentarias alternativas en Medellín: caracterización y perspectivas de productores, comercializadores y consumidores.

The objective of this investigation is to depict alternative food networks in Medellin, Colombia, analyzing their benefits and limitations for producers, retailers, and consumers, while seeking to establish a framework for future work and/or investigations related to alternative food networks in the region. The information was obtained by identifying alternative markets operating in the city and carrying out interviews with producers, retailers, and institutions as well as surveying consumers. Through this process, three types of alternative food networks, 16 alternative markets, and three kinds of producers were identified. Furthermore, the socioeconomic characteristics of consumers were determined as well as the main social, economic, and environmental benefits and limitations that all three players identified within the production, commercialization and consumption of organic food.
A Comprehensive Study on Medical Image Denoising using Convolutional Neural Networks

Medical images are considered as one of the most important medical data for diagnosis and treatment purposes. For remote consultation and diagnosis, the medical images are frequently transferred using different communication methods. There is a chance of noise degradation of these medical images during transmission. Denoising such medical images is crucial for enhancing the quality and diagnostic precision. The remarkable ability of Convolutional Neural Networks (CNNs) to effectively denoise medical images has attracted considerable interest. This paper discusses various CNN architectures, training strategies, and data augmentation techniques for denoising tasks. In addition, the difficulties and limitations of CNN-based denoising techniques, such as computational complexity and data scarcity, are discussed. In addition, incorporating Generative Adversarial Networks (GANs) and transfer learning for improved denoising performance are discussed. This survey aims to serve as a valuable resource for researchers, clinicians, and developers by fostering a better comprehension of the current state-of-the-art techniques and future directions in medical image denoising using CNNs.
Design of a CNN1D for Control and Monitoring of Blood Pressure

The ability to continuously monitor the parameters associated with the state of health of an individual results in a large volume of data, which presents both challenges and opportunities for data analysis. There are several studies of convolutional neural networks (CNN) used successfully in the implementation of tools for the monitoring and analysis of a large amount of medical data, accomplishing faster and more accurate data processing for the forecasting of health-related variables. This paper will address the design of a predictive model of ECG and PPG signals to estimate blood pressure (BP) with an one dimensional convolutional neural network (CNN1D) that optimizes performance to reduce the amount of processing on devices.
Sparse-Aware Transformer for Single Image Super-Resolution

In the current era of continuous innovation in deep learning technology, the performance of single image super-resolution (SR) has been further improved. Recently, some researchers have introduced the Transformer architecture in the field of single image super-resolution, as it has a significant advantage in capturing long-range correlations.However, Transformer network densely samples tokens from the original image to calculate the multi-head self attention (MHSA) using all the tokens. Due to the sparsity of SR tasks, SR networks mainly focus more on texture regions and edges of images to learn nonlinear mapping between high-resolution (HR) and low-resolution (LR) images. Therefore, tokens sampled from flat image regions in the Transformer-based SR network are less informative, which causes lots of redundant calculation. For the purpose of this study, in order to improve the efficiency of Transformer based SR networks, we chose to use the sparsity of SR tasks, which can also effectively maintain the modeling of long-range dependencies. So, we design and develop a sparse perception converter network (SAT) for single image SR. The experimental results show that the SAT designed in this article can make calculations easier and make SR performance more competitive on the SR benchmark dataset.
Vereinfachte Berechnung der Strömung bei der Strahlreinigung

Kurzfassung Für die Entwicklung eines praxistauglichen Vorhersagemodells für die Strahlreinigung wird ein Ansatz motiviert, welcher auf der Zerlegung in abhängig vom Anwendungsfall parametrierbare Teilprozesse beruht. Als Ausgangspunkt der Modellierung dient eine reduzierte Konfiguration: der senkrecht auf eine glatte Verschmutzung auftreffende, kohärente isotherme Wasserstrahl. Für diesen Testfall wird die zweidimensionale numerische Berechnung der mittleren Strömung erprobt. Die Validierung erfolgt anhand eigener Messwerte, experimentell und numerisch ermittelter Daten aus der Literatur sowie analytischer Korrelationen für Reynoldszahlen bis = / = 37000.
A Fine-Grained Image Classification Method Based on Attention Guided Channel Interaction Networks

The network model based on data-enhanced channel interaction is proposed on the basis of trilinear attention sampling network for fine-grained images where the discrepancy information is often at the component level, but the connection between the overall features and the components is ignored if only component-level features are used, resulting in poor recognition accuracy. The channel interaction is used to obtain and powerful feature representation, and the overall features and local features are effectively combined. In addition, an attention-guided data enhancement mechanism is added to enhance the generalization ability and robustness of the model. Finally, the proposed algorithm is validated on a fine-grained image classification dataset, and it is demonstrated that the method can effectively improve the fine-grained image classification accuracy.