Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Solar Radio Burst Detection Using Deep Learning

Solar radio bursts (SRB) play a crucial role in understanding solar activity and its influence on systems across the world. The classification of SRB into distinct types based on morphology and frequency drift requires vast data and poses significant challenges for automated detection and classification. In this paper, we introduce a deep learning-based approach to address this challenge by leveraging a curated dataset from the CALLISTO network and a ground-based radio astronomy station. A convolutional neural network is trained to identify and classify SRB, despite the low signal-to-noise ratios, dynamic solar atmosphere and limited training data. This work contributes to the automation of SRB analysis and showcases the potential of deep learning in decoding complex astrophysical phenomena. Preliminary results demonstrate the efficacy of our approach, paving the way for a revolutionary advancement in the field of SRB analysis.
Opportunities and Challenge of RPL Security Measures

The utilization of Internet of Things (IoT) devices has seen a significant increase in recent years, resulting in the proliferation of connected devices in various environments. However, this growth has also brought forth security concerns within IoT networks, including device vulnerabilities, network attacks, and communication security issues. To tackle these security challenges, this study investigates the utilization of RPL, a routing protocol specifically designed for IoT networks, and the integration of Artificial Intelligence (AI) techniques to enhance its security. This study provides a comprehensive examination of both RPL and AI techniques, as well as a thorough evaluation of their effectiveness in enhancing the security and performance of IoT networks. The findings of this study offer insight into the opportunities and challenges of using RPL and AI techniques for the security of IoT networks and underscore their potential for future research in this field.
Adaptive Multi-User Clustering and Power Allocation for Hybrid OMA-NOMA System with Imperfect SIC

Non-orthogonal multiple access (NOMA) faces challenges from imperfect successive interference cancellation (SIC) impacting the achievable rates of the users. Addressing this, our hybrid OMA-NOMA system introduces a lower bound on power allocation factors, ensuring each user in a multi-user NOMA cluster achieves at least its corresponding orthogonal multiple access (OMA) rate. We propose a clustering criterion based on minimum signal-to-interference-plus-noise ratio difference for NOMA clusters, to outperform an equivalent OMA system. Adaptive multi-user clustering and power allocation algorithms are proposed, demonstrating superior rates than state-of-the-art through extensive Monte Carlo simulations.
Navigating the Gray Area: A Three-Label Framework for Uncovering Uncertainty in Fake News

Detecting the veracity of textual content, and distin-guishing between fake and real news, has been greatly improved with the advent of BERT-based transformers. However, an in-herent challenge arises when encountering news articles that fall within the borderline of the latent vector space between fake and real. In this paper, we propose a three-label framework to address this uncertainty issue and leverage an external data enrichment module, Google Fact Check, to handle such cases. Our approach utilizes a constraint dataset for training both the CT-BERT and BERT base models. Experimental results demonstrate that while the BERT base model achieves an accuracy of 80%, the accuracy of CT-BERT, our proposed framework, reaches 93%. This significant improvement in a ccuracy showcases the effectiveness of our approach in tackling uncertainty in fake news detection.
Navigating Data Privacy and Analytics: The Role of Large Language Models in Masking conversational data in data platforms

In the rapidly evolving landscape of data analytics, safeguarding conversational data privacy presents a pivotal challenge, especially with third-party enterprises commonly offering analytic services. This paper delves into the innovative application of Large Language Models (LLMs) for real-time masking of sensitive information in conversational data. The focus is on balancing privacy protection and data utility for analytics within a multi-stakeholder framework. The significance of data privacy is underscored across sectors, with specific attention to challenges in industries like healthcare, particularly when analytics involve external entities. A comprehensive literature review reveals limitations in existing data masking techniques and explores the role of LLMs in diverse contexts, extending beyond direct healthcare applications.The proposed methodology utilizes LLMs for real-time entity recognition and replacement, effectively masking sensitive information while adhering to privacy regulations. This approach is particularly pertinent for third-party analytics providers dealing with conversational data from various sources. Hypothetical case studies, including healthcare scenarios, showcase the practical application and efficacy of the method in real-world settings with external data analytics providers. The dual assessment evaluates the method’s efficiency in preserving privacy and maintaining data utility for analytical purposes. Experimental results using synthetically generated healthcare conversational data sets further illustrate the effectiveness of the approach in typical third-party analytics service scenarios.The discussion highlights broader implications, addressing challenges and limitations [1] across industries, and emphasizes ethical considerations in handling sensitive data by external entities. In conclusion, the paper summarizes the significant strides achievable with LLMs for data masking, with implications for diverse sectors and analytics providers. Future research directions, especially fine-tuning LLMs for enhanced performance in varied analytic scenarios, are suggested. This study sets the stage for a harmonious coexistence of customer data protection and utility in the intricate ecosystem of data analytics services, facilitated by the advanced capabilities of LLM technology.
Structuring Hybrid Model Using Bilateral and Guided Filters for Image Denoising

Image denoising is a foundational challenge within the realm of image processing. This study introduces an innovative technique for image denoising, leveraging a hybrid filtering method. The hybrid filter represents a fusion of two well-established filters, namely the bilateral filter and the guided filter. By amalgamating the merits of these filters, the proposed approach adeptly retains image edges while simultaneously mitigating noise. During the hybrid filtering procedure, a guided filter is employed to generate a guiding image that aids in edge preservation while smoothing the image. Subsequently, a non-linear filter known as the bilateral filter is applied to each pixel, its operation based on the weighted average of neighboring pixel intensity values, calculated through a Gaussian distribution. Experimental outcomes demonstrate that the proposed hybrid filtering method surpasses other state-of-the-art denoising techniques concerning peak signal-to-noise ratio (PSNR). The PSNR values achieved through the proposed method consistently range from 31 to 33 dB across various noise types present in the image. Consequently, the resulting images exhibit markedly reduced noise levels while sustaining their clarity. In summary, the proposed hybrid filtering method proves to be an effective strategy for image denoising, capitalizing on the strengths of both the bilateral and guided filters. This approach offers a pragmatic solution for real-world applications within the domain of image processing.
Enhanced Image De-Hazing Using Novel Hybrid Filtering and Perceptual Fog Density Improvement

Image de-hazing processes improve visual clarity and image quality, particularly in areas where sight is limited. This study proposes a novel hybrid methodology with the goal of outperforming the limitations of existing de-hazing strategies. We propose a filtering methodology in this study that combines the advantages of Guided Image Filtering (GIF), Weighted Guided Image Filtering (WGIF), and Globally Guided Image Filtering (WGIF) into a single strategy. The proposed solution is a multi-stage framework in which the three de-hazing techniques are utilized consecutively. GIF is first applied to the foggy image. Following that, WGIF is used, followed by GGIF. Finally, we have included a bilateral filter into the hybrid filter to increase the visual quality of the photos even further. Furthermore, the quality of de-hazed photographs is evaluated using a metric known as Perceptual Fog Density (PFD). For evaluating the performance of the proposed methodology, experiments are done over various hazy images of varying haze levels. When comparing the results obtained from the proposed novel strategy to standard de-hazing procedures, it becomes evident that the proposed approach offers notable advantages in terms of both Perceptual Fog Density (PFD) and visual quality.
Energy Efficiency Optimization of Hexapod Robots Based on Central Pattern Generator Control

Hexapod robots typically work in rugged and steeply inclined complex environments, imposing strict requirements on the robot's stability, robustness, and energy consumption in such unstructured surroundings. In light of this, this paper proposes a motion control method for hexapod robots based on the Central Pattern Generator (CPG), which generates stable coupled oscillatory signals automatically to coordinate the control of various joints of the hexapod robot. This eliminates the need for the controller to iteratively compute control commands based on pre-set patterns, thus exhibiting robust resistance to disturbances. Furthermore, the influence of various parameters within the CPG network on robot energy consumption is investigated, and the genetic algorithm is employed for parameter tuning to optimize the energy consumption of the hexapod robot. Finally, the effectveness of the proposed method is validated through simulation experiments.
Future Prospects of Paper Batteries: A Review

Ultrathin, flexible and secure energy storage devices, are seeing increased attention as a means to meet the varied form and function requirements of today's products. New studies show that carbon nanotubes may have the best chance of adopting the adaptable batteries that might make our devices even smaller. The paper battery may provide enough power for next-gen electronics. The size and mass of the battery become a problem beyond a specific point. Paper battery would be the most effective approach to resolve this issue. This manuscript discusses several issues with conventional batteries and explores how paper batteries may be utilized to solve them. It also discusses several potential applications where paper batteries can be utilized to enhance their performance. Studying the benefits, drawbacks, future uses, and limits of Paper Batteries is the primary goal of this research. The article also intends to provide light on the design and manufacturing techniques of Paper Batteries.
Innovative Object Detection Utilizing an Improved Version of the YOLO5 Algorithm

One of the most well-liked fields now is object detection. As a basic job in computational vision, object identification has garnered a lot of attention as deep learning technology has advanced. Due to their tiny size and poor quality, remote sensing pictures provide a significant problem when it comes to object recognition. Based on the YOLOv5 target identification technique, which has been shown to be one of the most representative and successful algorithms for this job, a remote sensing image detection (RSI-YOLO) strategy is provided in this research. We can improve the features of the neural network as a whole by using channel attention and spatial attention. The network's basic PANet structure has been changed to a weighted bidirectional feature pyramid so that feature fusion at different scales can be done better and with more information. A thin layer is also added to help recognize objects, and the loss function is changed to make the network model better. Results from four picture datasets, such as DOTA and NWPU-VHR 10, show that RSI-YOLO is better at detecting things than the original YOLO. The fact that the suggested RSI-YOLO approach is better at detecting objects than other standard object identification methods proves that the changes in the YOLOv5 algorithm work.
Implementation of Machine Learning Models to Classify Security Incidents in Industrial Systems

This research is focused on classifying security data in industrial control systems (ICS) using machine learning models. Currently, ICS mainly focus on the technical and industrial operation of technological infrastructures, neglecting their security. This practice is dangerous as it affects various critical sectors of society. Due to the scarce information and difficult access to security incident data in industrial systems, this study employed web scraping to create a data set called “SI ICS UPS 2023” with 2914 records of non-null security incidents in text format. For the labeling phase, regular expressions were applied to standardize the data set and propose two main classes of interest in this study. Data cleaning and processing stages were implemented, followed by the training of four machine learning models from scratch. The best-performing model in terms of the area under the curve (AUC) was the Random Forest with a score of 0.76 and an accuracy of 71.20%. These results demonstrate the efficiency of automating processes for the collection and classification of cyber incident data in industrial environments using techniques like web scraping and the utilization of machine learning models.
Metamorphic Malware Evolution: The Potential and Peril of Large Language Models

Code metamorphism refers to a computer programming exercise wherein the program modifies its own code (partial or entire) consistently and automatically while retaining its core functionality. This technique is often used for online performance optimization and automated crash recovery in certain mission-critical applications. However, the technique has been misappropriated by malware creators to bypass signature-based detection measures instituted by anti-malware engines. However, current code mutation engines used by threat actors offer only a limited degree of mutation, which is frequently detectable via static code analysis. The advent of large language models (LLMs), such as ChatGPT 4.0 and Google Bart may lead to a significant evolution in this landscape. These models have demonstrated a level of algorithm comprehension and code synthesis capability that closely resembles human abilities. This advancement has sparked concerns among experts that such models could be exploited by threat actors to generate sophisticated metamorphic malware. This paper explores the potential of several prominent LLMs for software code mutation that may be used to reconstruct (with mutation) existing malware code bases or create new forms of embedded mutation engines for next-gen metamorphic malwares. In this work, we introduce a framework for creating self-testing program mutation engines based on LLM/Transformer-based models. The proposed framework serves as an essential tool in testing next-gen metamorphic malware detection engines.
Experimenting Datasets and Machine Learning Techniques for Enhancing Cyberbullying Detection

Social media has become an essential aspect of everyday life for most individuals worldwide and continues to expand. In social media, short text communication has become more common, and the ease of accessibility has also contributed to the increase in cyberbullying cases. Numerous research studies have been conducted by researchers in cyberbullying detection using different datasets and machine learning methods for cyberbullying prediction. However, different data characteristics may influence the predictive model performance, and there is a need to examine how the impact is. Therefore, this research aims to compare different datasets, including merging existing ones with different machine learning techniques, shedding light on their relative effectiveness and contributing to a deeper understanding and better decision-making of dataset usage and techniques deployment. Our research finding provides scientific evidence that combining various cyberbullying datasets to create balanced data and Bi-LSTM and BERT improve prediction model performance.
Detection and Classification of IoT Malware from MQTT Dataset

As technology has improved, the usage of IoT devices has increased significantly in recent years. Due to their abundance, variety, and heterogeneity, IoT devices are vulnerable to cyber threats. IoT infrastructure faces serious security risks from a range of threats because it lacks a standard that supports all smart devices and an integrated security mechanism. The possibility for practically all internet attacks, including malware, viruses, and other threats, to be launched through IoT infrastructure can result in some losses. In the paper, different machine learning algorithms are proposed to detect and classify IoT malware from the MQTT dataset, a standard protocol for IoT messaging. The different machine learning algorithms such as Random Forest, CNN, Gradient Boost, Light GBM, and Naive Bayes are proposed and compared based on their accuracy, F1 score, and confusion matrix. Random Forest outperforms the other algorithms, provides better results, and effective in classifying the malware.
Soft Computing Methods for Predicting Component Development System Through Software Reusability

An essential part of software engineering is the use of soft computing techniques. The fuzzy logic system, the neural network model, and the genetic algorithm all fall under this category. Fuzzy logic and neural network techniques are commonly used in assessments of software attributes such as reusability, maintainability, and understandability. The term “software reuse” refers to the method of developing new programmes by reusing existing building blocks. These are evaluated with the help of soft computing techniques like neural networks and fuzzy logic. There are a number of membership functions used in MATLAB, which include the Triangular (trimf), Trapezoidal (trapmf), and Gaussian (guassmf) functions. It is possible to predict software reusability by utilizing the membership function even above. These software criteria are the foundation around which the approach is built for the purpose of locating and assessing reusable components. The reusability of software is positioned to have a prosperous future and is now a noteworthy area of research. Because of this endeavor, software developers and researchers will be able to select the most optimal component in terms of its reusability. This, in turn, will contribute to the overall improvement of the software system's performance and efficiency.
A High Efficiency SIW-Integrated Dielectric Resonator Antenna Array for Millimeter-Wave Applications

This paper presents a low-loss and low-cost hybrid feed network based on substrate integrated waveguide (SIW) for millimeter wave applications. To further enhance the radiation efficiency, dielectric resonators are chosen as the radiators. Combined series and corporate feeds are deployed to excite the dielectric resonator antenna array, which consists of 8 × 8 rectangular dielectric resonator (RDR) elements. To realize circular-polarized radiation, the chamfered RDR are utilized to generate perturbations. The proposed antenna array is implemented between 36.8-38.6 GHz, achieving 3-dB axial ratio bandwidths of 2.9% from 37.5 GHz to 38.6GHz. Additionally, the array obtained a high realized peak gain and radiation efficiency of 20.5 dBic and 87.4%, respectively.
Soft Set Based Clustering and Its Comparison on Categorical Data

Categorical data clustering is problematic since it is difficult or complex to determine how comparable the data is. Several methods, most recently centroid-based strategies, have been developed to reduce the complexity of the similarity of categorical data. These methods nevertheless result in lengthy processing durations. Another method, soft set-based clustering (SSC), based on the probability function of multivariate multinomial distributions, is suggested in this article. Soft sets are used to represent the data, and each soft set has a probability for each object. The joint cluster distribution function determines the probability for each object after the multivariate multinomial distribution function. The connected cluster would receive the highest likelihood. Benchmark data sets from UCI machine learning are used to compare the performance of the approach to the baseline techniques. The outcomes demonstrate that the suggested strategy performed better in purity, rank index, and calculation time.
Control of the Energy Conversion in Radial Microactuators Using Conductive Polymers

Conductive polymers (CPs) represent new controllable and low cost materials for microactuation. During the electrochemical doping process, polymer changes its volume, when a voltage is applied. Different combinations have been studied and among them the combinations with the new CPs, like P(VDF-TrFE-CFE) Terploymer (PVTC) placed between polyaniline (PANI) electrodes, present higher conductivity, when there are used for radial microactuation. The cycle life of $10^{6}$ order justifies tuning the actuator performances. The operating structures were reproduced at molecular level with HFSS program (by Ansys). The tensile stress was of ca. $0.8-2.6 \mathrm{MPa}$, at operating voltages $\pm(1 \ldots 4) \mathrm{V}$. An energy conversion efficiency of about 30-50 % was obtained, but with peaks even higher, for a radial strain of up to 80%, depending on the actuator dimensions and doping level. Dopants like metals from nitrates or chlorides $\left(\mathrm{Au}^{+}, \mathrm{Cu}^{2+}\right.$ cations) or soaking acids $\left(\mathrm{HNO}_{3}\right)$ in PEDOT:PSS or BaZr0.2 Ti0.8 O3 in Terpolymer have been considered for increasing the electrical conductivity in the polymer matrix. The radial, respectively tangential strain of the $C P$ radial actuator has been determined and the simulation data have been used to calculate the energy conversion efficiency of the CP actuator. The method generates characteristic parameters of the optimal structure to be synthesized, for the highest energy conversion rate, considering also new dopants and CPs in combination.
Video Captioning using Spatio-temporal Graphs: An Encoder-Decoder Approach

To understand the content in the video, describing it in the form of natural language is also one of the essential requirements. A video consists of frames showing different objects and actions. These objects can change in each frame, and there is a time-dependent sequence between objects. Objects in the sequence of frames have a temporal relationship. Actions in a frame are the interactions between objects, which can be easily understood using spatial relationships. When spatial and temporal aspects combine, it is the Spatio-temporal relationships between objects. Analyzing this relationship is done using a Spatio-temporal graph, which integrates both visual and motion features of detected objects. An integrated Spatio-temporal graph describes the video in a natural sentence. The generated sentence is evaluated using Metrics like BLEU@4, METEOR, and CIDEr.