Miracle-dz/UniFAR-FTUs huggingface dataset:
MedTiny: Enhanced Mediator Modeling Language for Scalable Parallel Algorithms

This paper introduces MedTiny, a streamlined component-based modeling language, evolved from Mediator. Existing modeling languages struggle to provide convenience in expressing recurrent component configurations, which are common in modern large-scale parallel computation models in distributed environments. Furthermore, Mediator frequently conducts repetitive flag manipulations to synchronize between distributed entities. To address these issues, we propose syntactic enhancements, including component array with static foreach iterator and automatic synchronization flag manipulation. These enhancements significantly reduce code size and effectively address the aforementioned problems. After simplification, we successfully incorporate language feature enhancements into MedTiny without compromising its formality. Subsequently, we develop a prototype code generator that targets multi-threaded Python for code execution and PRISM model checker for verification. To demonstrate MedTiny's efficiency, we include a case study on a character counting MapReduce algorithm.
Detecting Anomalous Traffic in IoT Networks Through Deep Learning

In recent years, the popularity and growth of Internet of Things (IoT) devices have led to an increase in network traffic. Detecting anomalous traffic in these networks is critical to maintaining network security and preventing attacks. In this paper, we propose a deep learning-based approach to detect anomalous traffic in IoT networks. Our approach combines convolutional neural networks (CNN), density-based spatial clustering of applications with noise (DBSCAN), and transfer learning. We evaluate our approach on a real-world dataset and achieve promising results.
Smart Image Recognition System for The Visually Impaired People

The inclusion of people with specifics in society has become a big challenge. Vision is extremely relevant in the relationship with objects, space, and others, and their absence can negatively affect the quality of life. Object detection can be applied in various forms, such as autonomous cars, industrial 4.0, and people control. This research work aims to objectively the application of computer vision techniques to present a proposal for an application of image recognition for visually impaired people and give real-time voice feedback of the object. It is possible to build an application of recognition of images that can help visually impaired people to easily surround things and live independently. The application uses a special algorithm based on machine learning and deep learning.
Multilayer Dual-Band Bandpass Filter Using Microstrip-to-Slotline Transitions and Transversal Signal-Interference Microstrip Lines

This paper reports a type of multilayer dual-band bandpass filter (BPF). It consists of two back-to-back-cascaded microstrip-to-slotline transitions connected by a transversal signal-interference microstrip close loop. Due to the employed transversal in-parallel-connected asymmetrical microstrip lines, a second-order dual-band BPF with three inter-band transmission zeros (TZs) can be attained, where the two created passbands are symmetrical regarding the design frequency. The operational principle and design rules of the devised dual-band BPF are discussed through the presented theoretical frequency responses. For experimental-validation purposes, a two-layer second-order dual-band microstrip BPF prototype is designed, fabricated, and characterized. The two measured passbands are centered at 1.1173 GHz and 2.8965 GHz with minimum in-band power-insertion-loss levels of 0.25 dB and 0.84 dB, and 3-dB fractional bandwidths of 40.41% and 15.36%, respectively.
A Review: Medical Image Analysis Using Deep Learning Models

The aim of medical image analysis is to enhance the effectiveness of clinical research and treatment approaches. Deep learning has brought about a transformation in medical image analysis, delivering remarkable outcomes across various image-processing tasks, including preprocessing, segmentation, feature extraction, and classification. The deep learning technique observed a valuable hidden pattern in images which is supported for better diagnostic perfection. Multiple deep-learning methods have been introduced to medical image analysis and classification. This paper offers an overview of recent developments in utilizing deep-learning techniques for medical image analysis. First, we begin with the medical image analysis tasks and give the overview of the pre-trained model that aims to improve the performance of Deep neural networks second, we explore the current survey paper of deep learning models in different medical image analysis tasks across a range of modalities.
FCPENet: Feature Complementary Property Enhancement Network for RGB-Thermal Semantic Segmentation

RGB-thermal (RGB-T) semantic segmentation models have shown their effectiveness in achieving robustness under a variety of illumination conditions by leveraging the complementary information from RGB and thermal images. Despite significant progress in this field, existing RGB-T semantic segmentation methods often overlook the issue of information redundancy in multimodal learning. This oversight weakens the complementary property of cross-modal features, which contradicts the fundamental purpose of multimodal learning. To address this challenge, we propose a novel approach called the Feature Complementary Property Enhancement Network (FCPENet) for RGB-T semantic segmentation. We introduce a Feature Complementary Property Enhancement Module (FCPEM) that tackles the problem of redundancy between two modal features. The FCPEM employs a mutual information minimization constraint to effectively reduce redundancy and enhance the complementary nature of these features. Additionally, a channel attention mechanism is incorporated in the FCPEM to dynamically select critical complementary multi-modality features for further fusion. Benefited from the proposed FCPEM, our FCPENet enhances the complementary property of multimodal features and achieves improved segmentation performance. Extensive experiments conducted on the MFN dataset demonstrate the superiority of our FCPENet over state-of-the-art (SOTA) methods. Both subjective visual comparisons and objective metrics reveal the effectiveness of our approach, with an mIoU score that surpasses the top-ranked method on the current competition list by 0.5%.
Anonymization, Hashing and Data Encryption Techniques: A Comparative Case Study

The Confidentiality of Personal Data has proven to be an important concern for both individuals and organizations around the world in the present day. As a result of rising numbers of security incidents, computer hacking and illicit utilization of personal data effective techniques for preserving the confidentiality of personal data are becoming increasingly important. Three frequently implemented techniques in maintaining data privacy: Anonymization, Hashing and Data Encryption have gained in popularity as time passed. But in general, the right choice of technique relies on the particular application and the necessary degree of integrity and confidentiality of data. In this study, a detailed examination of several anonymization, hashing, and encryption techniques are described. Each technique's strengths and weaknesses will be addressed, as well as recommendations and best practices when using each technique in a variety of situations, based on experimental healthcare data.
Comprehensive Examination of Instruction-Based Language Models: A Comparative Analysis of Mistral-7B and Llama-2-7B

In natural language processing and computer vision, both of which are developing rapidly, analytical methods are very important to improve modeling This study uses ROUGE, BLEU, and CIDEr as key performance indicators to assess how Mistral-7b and Llama-2-7b are better model perform. The preliminary results show that the LAMA-2-7b is the best in all dimensions. Despite these results, the new Mistral-7b, which is supposedly more capable, may still be in the data collection stage, needing larger datasets to shine This article learns more about their capabilities and how important they are to give testing examples continue to illuminate.
A Research of Page Automatic Publishing Scheme Based on Low Code Platform

With the widespread application of low code platforms in business development, the efficiency and quality of page publishing have become a focus of attention. This article studies and implements a page automatic publishing solution based on low code platforms, mainly involving template process management technology and optimizing the development process of low code platforms. In today's fast-paced software development environment, improving the efficiency and quality of page publishing has become particularly important for enterprises. The traditional manual publishing method has the problems of time-consuming and error prone, and the use of low code platforms combined with template workflow management technology can effectively solve these problems.By introducing template process management technology, we can achieve template construction and one click publishing, thereby improving the efficiency of page publishing and reducing the possibility of errors. By introducing template workflow management technology, we have successfully implemented a page automatic publishing solution based on a low code platform. This solution not only has advantages in improving publishing efficiency and quality, but also simplifies the workflow of developers, improves collaboration efficiency, and can provide enterprises with more efficient, reliable, and consistent page publishing methods, bringing great convenience and benefits to software developers and business organizations.
Grid Interactive PV-fed BLDC Motor with Power Quality Features using Predictive Current Control

Renewable energy features have contributed to the popularity of solar Photo Voltaic (PV) in recent decades, which can operate in grid-connected mode or islanded mode with energy storage systems. The expected features of the controller are to provide reliable power even under high-fidelity dynamic conditions. In this paper, a grid-interactive solar PV-fed Brushless Direct Current (BLDC) motor water pump is implemented, using a non-linear model predictive current controller. Additionally, power quality features are included in PV DC-DC converters and grid-side three-phase voltage source inverters (TPVSI). It is not recommended to use linear control structures that operate at a single operating point, such as Proportional Integral (PI) controllers. Also, under dynamic load conditions; such as sudden changes in DC loads, starting of BLDC motor, sudden load applied or removed from the motor pump, and changes in linear or non-linear loads applied at Point of Common Coupling (PCC). Therefore, a proper control scheme is required to alleviate these challenges. A grid-interactive solar PV-fed BLDC motor with a non-linear-based model predictive controller is required. The proposed predictive controller helps to achieve (i) better dynamics during the sudden changes, (ii) unity power factor on the grid side despite non-linear loads being connected, and (iii) the PV panel operates at its maximum power point under all conditions. The proposed model predictive controller with solar pump is validated through MATLAB/SIMULINK and results are presented.
Comparative Assessment of Prony Analysis and Eigensystem Realization Algorithm for Forced Oscillation Detection and Mode Estimation Considering PMU Noise

In this study, Prony and Eigensystem Realization Algorithm (ERA) are compared for identifying electromechanical and forced oscillation modes. The varying number of excited modes in practical power systems makes the process of determining model order challenging. Prony requires an iterative procedure, while ERA utilizes Singular Value Decomposition (SVD) for direct model order determination. Comparitive studies shows that ERA accurately estimates the signal, and generates fewer insignificant modes compared to Prony. Identified modes are used to design a filter, which estimates desired signal frequency. The filtered signal’s periodogram is then compared with detection threshold to detect Forced oscillation (FO) at specific frequencies.
Intelligent Marginal Particle Filter for Maneuvering Target Tracking

The particle filter (PF) has proven effective in maneuvering target tracking due to its capability to manage systems that are both nonlinear and non-Gaussian. However, challenges such as the curse of dimensionality and particle impoverishment are primary obstacles to applying PF in maneuvering target tracking. To tackle these issues, an intelligent marginal particle filter (IMPF) for maneuvering target tracking is proposed in this paper. The IMPF is formed by combining the intelligent particle filter (IPF) with the marginal particle filter (MPF). To reduce the dimension of particle filtering, the state vector has been initially decomposed into linear and nonlinear parts by the proposed filter. As a result, the linear part can be managed by a Kalman filter, and the nonlinear part is handled through particle filtering. Subsequently, genetic operators are introduced into the resampling process of particle filtering to adjust the small-weight particles. This action helps to alleviate particle impoverishment while introducing a moderate rise in computational demands. The outcomes derived from tracking maneuvering target experiments showcase the effectiveness of IMPF, especially when dealing with abrupt changes in the target’s state.
Confirmatory factor analysis of individual perception towards peer-to-peer lending platforms

As Fintech innovation continues to dominate the financial industry, peer-to-peer lending platforms emerged and are adopted in several countries including Malaysia. This digital lending platform aids Micro, small and medium enterprises to obtain funds with less efforts hence, experiencing a constant growth in Malaysia. Despite the growth, a lot is unknown including investors perception towards the platforms hence, this paper seeks to confirm factors that may constitute investors perception towards peer-to-peer lending platforms. Data was collected for 20 predetermined items based on previous studies in conjunction with the theory of planned behavior. Result indicates that 17 of these items are valid and reliable to measure individual perception towards peer-to-peer lending platforms. In addition, structural equation models indicate a positive relationship between peer-to-peer lending platform awareness, digital financial risks awareness and time pressure on investors’ perceptions towards P2P LP, and perceived risk has no connection. These findings are useful for further studies, and to enhance platform lending operations when consolidating these factors in the lending process.
Fourier Synchro squeezed-LSTM-CNN for Categorization of Power Quality Disturbances

Integration of renewable energy has resulted in an increase in the power quality disturbances (PQDs) in a power network, thus, necessitating accurate PQD categorization for guaranteed reliability and stability of the power system. In this work, the voltage signal characteristics are used for PQD event categorization using deep learning techniques. A novel architectural deep neural network is built on an LSTM-based convolutional neural network to efficiently classify various power quality disturbance signals using a support vector machine classifier. Fourier Synchro squeezed Transform (FSST) is utilized to transform the PQD signal information into 2-dimensional data which is further represented as an image. Automated feature extraction from the FSST-transformed image of the PQD signal was adopted in this work for better classification of PQDs. Using this special combination, 99.99% accuracy, 99.65% precision, and 99.63% sensitivity were achieved. The method was also validated using real-life PQD signals.
SRGAN Remote Sensing Image Super-Resolution Algorithm Based on Multi-Expert Fusion Summaries

High-resolution remote sensing images have very important application value in remote sensing image target detection, remote sensing image classification, etc., but the texture details of remote sensing image generated by the current algorithm for improving image quality are still not accurate enough and ignore the more important elements contained in the image. value of information, this paper proposes a multi-expert fusion algorithm based on SRGAN remote sensing image super-resolution improvement. The improved algorithm designs a multi-expert fusion module for the generator, which contains multiple expert networks and can adaptively adjust the feature weights output by different expert networks, increases the amount of feature information extracted by the network, and dynamically obtains the fusion results, which enhances the network The ability to extract features of detailed information. After the comparative experiment on UCMerced_LandUse public data set, the results show that the PSNR value and SSIM value of the method proposed in this paper have been improved, and this experiment fully confirms the effectiveness of the algorithm.
Developing Automated Feature Selection Methods with Artificial Intelligence

This studies article examines the capability of using artificial Intelligence (AI) to improve automatic feature choice strategies (AFSM). AFSM is a procedure that determines the maximum essential capabilities in a dataset, which allows to reduce fees and improve version accuracy. By using combining AI and AFSM strategies, its miles feasible to broaden greater sensible and effective characteristic selection algorithms that may discover the highest quality function combos. The research focuses in the main on supervised gaining knowledge of algorithms, together with assist Vector Machines (SVMs), to conduct the evaluation. It explains the idea of characteristic selection, provides an outline of existing AFSM strategies, and then explores capacity improvements that may come from combining AI and AFSM. It additionally offers an example of an AI-enabled AFSM set of rules and discusses viable advantages and concerns. The studies in the long run concludes that AI-enabled AFSM should provide better feature selection accuracy and better performance than traditional techniques when implemented to numerous datasets.
IBS: Secured Healthcare Data Management over Cloud Environment using Integrated BlockChain Strategy

Healthcare needs better diagnosis and disease analysis methods to simplify the process.To address these concerns, a novel approach called Integrated Blockchain Strategy (IBS) has been developed, combining various cutting-edge technologies, including Blockchain, and Cloud-based Data Handling.IBS aims to enhance healthcare disease prediction and secure data management. It leverages the logical Blockchain and secured cipher strategy, with a focus on the Advanced Encryption Standard (AES) algorithm for data security. A unique aspect of this approach is the Attribute-Based Secret Key Generation Logic (ASKGL), which modifies the key generation process by incorporating user authorization attributes and concatenating them with a random key to create a new cipher key. By integrating these elements, the proposed approach ensures data security on the server end and facilitates disease prediction analysis with improved accuracy. Artificial Intelligence (AI) plays a vital role in handling complex healthcare-assisted data management tasks, simplifying the overall process. The result provides compelling evidence to support the efficiency of the proposed approach. It demonstrates reduced time consumption constraints, enhanced prediction accuracy, and other relevant benefits. In conclusion, the suggested approach offers a comprehensive and effective solution for healthcare data management, addressing the challenges faced in the healthcare domain with clarity and precision
AGI in Metaverse for Smart Cities and Societies: A Cyber Physical Social Approach

The development of artificial general intelligence (AGI) and metaverse technologies hold great potential to enhance the effectiveness of smart cities and societies. we present a cyberphysical-social approach to integrating AGI into metaverse-based smart cities and societies, with a specific focus on parallel intelligence. The proposed approach considers the interplay between the physical environment, digital systems, social dynamics, and emerging forms of parallel intelligence, as well as swarm intelligence and collective intelligence. By leveraging these forms of parallel intelligence, the integration of AGI can create more responsive and proactive infrastructure, personalized and efficient services, and foster greater community engagement. The metaverse acts as a platform for simulating real-world scenarios and testing and refining AGI models before deployment in the physical world, while also facilitating the development and coordination of parallel intelligence. However, ethical considerations must be taken into account to ensure responsible deployment and avoid exacerbating existing societal inequalities or infringing upon individual privacy rights. This report provides a comprehensive framework for the integration of AGI and parallel intelligence into metaverse-based digital and smart organizations, while emphasizing the importance of human ethics, social responsibility, and ecological sustainability.
KCDR: Knowledge Graph Based Model for Cross-Domain Recommendation

In recent years, researchers and existing studies have proved the advantages of knowledge graph in the field of recommendation system. However, current researches on recommendation algorithms based on knowledge graph generally focus on single-domain recommendation and only focus on the modeling of item-side knowledge graph. In this paper, we propose a knowledge graph based cross-domain recommendation model (KCDR), which combines users, items, and ratings three sides into the knowledge graph, uses the knowledge graph as a bridge to connect different domains, uses the importance to sample neighbors, and propagates global structural information and semantic information through graph convolution to obtain users ’cross-domain preferences. We apply real-world datasets to design two tasks to verify the performance of our model, and the experimental results show that our model significantly outperforms the current state-of-the-art recommendation baselines.
Improved Multi-Face Detection with ResNet for Real-World Applications

This paper presents an improved approach to multi-face detection using ResNet50 in Python for real-world applications. The study focuses on enhancing the accuracy and efficiency of detecting multiple faces in a single frame, a challenge often encountered in real-world scenarios such as surveillance and social media platforms. The proposed method leverages the power of ResNet, a deep residual learning framework known for its superior performance in image recognition tasks. The paper discusses the implementation details of the ResNet model in Python and how it has been optimized for multi-face detection. Experimental results demonstrate that the improved ResNet model outperforms traditional face detection methods regarding accuracy and speed, making it a promising solution for real-world applications. The paper concludes with potential future work and improvements that could further enhance the model's performance.