Miracle-dz/UniFAR-FTUs huggingface dataset:
Medicine Recommendation for Pharmacists at Drug Stores based on Data Analysis of Conditional Knowledge

The purpose of this research was to support pharmacists in giving information to patients with medication recommender systems using the Rule-based and Gradient boosting tree methods. The cases of the system consist of the patients taking medication independently without doctors' diagnosis with household medicines to relieve basic symptoms and the symptoms of patients that should receive real treatment at the hospital. In addition, technology solutions such as machine learning have been valuable for application in health care. Accordingly, this research starts with getting a patient's information such as symptoms, chronic diseases, drug allergies, and medicine a pharmacist asks some general questions for understanding the patient's health condition and constructing databases by processing the medication knowledge. Rules were extracted from pharmacy guidelines for filtering the Hospital Cases that are out of the scope of the drugstore and the patient should receive medical care from a specialist. Then the system would analyze the data for recommendations the medicine and display it on the user interface. Lastly, we tested the performance of the system by evaluating with accuracy, precision, recall, and F1 score.
Analysis, Design and Implementation of a Web- Based Online Auction System

This paper presents a study on the security challenges faced by online auction systems and proposes strategies to address them. The research methodology involved a literature review of existing studies. The main findings of the study suggest that the most common security threats faced by online auction systems include hacking, phishing, and fraud, which can have serious consequences for both buyers and sellers. To address these threats, the paper proposes a number of strategies, including the use of encryption, two-factor authentication, and secure payment systems. The study concludes that improved security measures in online auction systems can help to increase trust and confidence among users, which in turn can lead to greater participation and revenue for the industry. The implications of the study for the online auction industry and potential directions for future research are also discussed.
Critical Buckling Load of Magnetostrictive Sensors Under Compression

Magnetostrictive materials are smartmaterials that have a wide range of applications for actuation and detection. They have been widely used in the industry for the development of variousdevices, such as actuators, sensors, transducers, speakers, vibration dampers,and fast relays. This special class of materials shows an inherent couplingbetween magnetic field and deformation. Structures made of magnetostrictivematerials are capable of simultaneously changing their dimensions, and elasticand/or electromagnetic properties in the presence of a magnetic field. The objective of this work is to model andnumerically simulate the instability phenomenon of magnetostrictive sensorssubject to compressive loadings. The mechanical modeling of the consideredsensor is done using a one-dimensional elastic magnetostrictive beam undercompression subjected to a magnetic field. The formulation of the problem iscarried out using the Euler-Bernoulli beam theory and taking into account thecoupling of the mechanical and magnetic effects via the behavior relations ofmagnetostrictive materials. The equilibrium equation of the sensor was obtainedby applying the Lagrangian formalism. The buckling loads and the critical loadof the compressed beam were calculated analytically and numerically using anapproach based on the Finite Element Method (FEM). Different boundaryconditions were considered.
Orchestration of Containerized Applications in the Cloud Continuum

Cloud Service orchestration is a hot topic addressed by both the academia and the business world. The increasing diversification of the Cloud offer, coupled with the strong need of flexible and scalable applications demanded by customers, is calling up for efficient and agile mechanisms that streamline the devops practices. The novel opportunity of running software applications or just application portions on resources located on the Edge of the network poses an additional challenge to the already tough problem of flexibly and seamlessly provisioning services. Orchestrators must deal with a continuum of heterogeneous computing resources, contributed by both the Edge and the Cloud, that promises several benefits yet at a higher management cost. In this paper, we introduce a continuum-enabled orchestrator and discuss an application provisioning paradigm that helps the users to easily configure and deploy composite applications. Finally, we showcase the potential of the orchestrator in a simple yet practical smart-city scenario.
SecBox: A Lightweight Container-based Sandbox for Dynamic Malware Analysis

Cybersecurity solutions based on machine learning (ML) and behavioral fingerprinting have demonstrated their suitability when detecting heterogeneous malware. However, most solutions are black boxes missing explainable and visual capabilities needed to analyze relevant metrics and malicious behaviors to be collected. In this demonstration, SecBox, a dynamic malware analysis platform with integrated data collection and visualization for malware execution, is presented. To provide a lightweight sandboxing approach, the architecture relies on Linux containers for isolation. The sandboxing and data analysis components of the SecBox architecture are deployed in a test bed to show the analysis of two malware families. In the presented scenario, the Monti ransomware and CoinMiner, a Monero-based cryptojacker are analyzed after obtaining them from a public database.
Rheumatoid Arthritis Predictor Using ML Techniques and Explainable AI

Rheumatoid Arthritis (RA) is a chronic autoimmune disease that occurs in multiple organs and joints in the body. It is characterized by inflammation in the lining of joints, known as the synovium, which leads to pain, stiffness, and eventually, loss of function. RA can also cause fatigue, fever, and weight loss, and in severe cases, it can lead to permanent joint damage and disability. Diagnosis is essential during the early stages of affection, but the current process is costly and inefficient, disadvantaging those with limited finances. To address this, a study was conducted using five different machine learning (ML)models (Convolutional Neural Networks (CNN), K-Nearest Neighbor (KNN), Xg-boost (XB), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM)) to find the most efficient way to diagnose RA. The study compares the accuracy of these algorithms and determines the most effective one for predicting RA in a patient. The process involves two main steps: image processing and algorithm-based prediction. During the image processing phase, the uploaded image undergoes optimization techniques to remove false negatives and enhance the image quality for a more ideal input to the following step. The image is processed using the most effective ML model in the second step, which results in 98% prediction accuracy, a significant improvement over and above the state-of-the-art literature.
An Efficient Secure VANET Communication Using Multi Authenticate Homomorphic Signature Algorithm

Vehicular Ad-hoc Networks (VANETs) is a wireless network that provides communication between vehicles and roadside infrastructure. It has been gaining high attention in the field mainly by researchers recently. It can provide various services, ranging from security-related early warning systems, enhanced navigation mechanisms, and information and entertainment applications. In VANET systems, some confidential data and contact information leakage can cause a severe loss of property. After that, it is a need for a higher level of security VANET system. Therefore, in VANET is the urgent priority need for security of privacy protection protocol. The proposed method Elliptic Homomorphic Signature (EHS) the critical requirement is to allow VANETs privacy protection protocol to ensure its authenticity for the use of trademarks. When the user signs data, the signature is generated using the secret key, which is kept secure by the signer, and it encrypts the data. No one can create any signature items other than legitimate users. Destination to receive data using the public key confirms has not been modified in transit. The attacker can neither find the correct signature nor steal the data. The simulation results, when used in VANET, confirmed the level of effectiveness and security.
Event-Flow Correlation for Anomaly Detection in HTTP/3 Web Traffic

The new HTTP/3 protocol for web traffic was recently released, superseding the widely used HTTP/2. It now supports exclusively encrypted transmissions and brings a lot of other changes. Many of these changes promote user privacy but hinder security monitoring of network traffic. In the past, the direct correlation of events and flows generated by the HTTP/2 web traffic enriched the encrypted network flows with the content from the web server’s event log. In this paper, we modify the event-flow correlation method for the HTTP/3 protocol. Then, we deploy and evaluate the correlation method on a real traffic dataset. We compare the results of the correlation with the results for HTTP/2 and discuss the differences in the correlation of the new protocol compared to the original one. The results show that event-flow correlation can still enrich HTTP/3 network flows, albeit it introduces new challenges to cope with.
Multi-Pass Automotive Synthetic Aperture Radar Image Fusion

In this work we demonstrate an efficient technique for the registration and compositing of multiple automotive synthetic aperture radar (SAR) images as a step towards distributed SAR imaging. Through the use of an efficient sub-pixel Fourier-based iterative correlation and refinement technique, sub-sections of the overall SAR images are aligned. Because only small sub-regions of the full SAR images are aligned at a time, this technique inherently compensates for accelerations experienced when sampling the synthetic aperture, by assuming a quasi-constant velocity over small sub-regions of the image. Because the image fusion is performed after image formation, this technique lends itself well to a distributed architecture where the image formation is performed on-vehicle and transferred to other vehicles or a centralized cloud processor where images from other vehicles can be aggregated. Finally, because the registration of sub-regions of the SAR images are performed, the registration alignment vector can be used as a odometry provider to improve vehicle odometry estimates; if a metrology grade SAR map is used as reference, a globally reference position estimate could be performed.
An Adaptive Moving Target Defense Approach for Software-Defined Networking Protection

The Software-Defined Networking (SDN) paradigm has been increasingly adopted in computer network infrastructures in the last decade. However, since it is a relatively recent approach, few security measures have been taken into account to protect it. Recently, Moving Target Defense (MTD) techniques has emerged as an effective technique to protect SDN infrastructures. However, MTD-based strategies can seriously degrade network performance owing to their operational reliance on delay tactics. Thus, in an attempt to provide feasible protection against scanning attacks without degrading network services, especially in terms of Quality of Service (QoS), we introduce the MTD Adaptive Delay System (MADS) solution. We extensively evaluated and compared MADS with the state-of-the-art in MTD-based defenses for SDN protection. Our results shows that MADS causes lower network degradation concerning latency, Bad TCP packets, and throughput.
Achieving Observability at Scale through Federated Learning

We describe a novel approach to achieving observability at scale based on federated learning and our experiences and lessons learned with implementing it in a real-life commercial production environment. With the system described in this paper, the combination of Edge technologies with Artificial Intelligence allows one to predict up to six hours in advance that a component of a distributed system is about to fail. This permits the proactive execution of actions to either prevent the failure altogether or mitigate its impact on other services. Moving from incidents (unplanned work) to Predictive Maintenance, and thus planned work removes the risk of outages for service providers and administrators and increases the operations of large-scale distributed systems. By defining a set of key performance indicators, we are able to quantify the benefits of our approach.
Adaptive Group Testing on Networks With Community Structure: The Stochastic Block Model

Group testing was conceived during World War II to identify soldiers infected with syphilis using as few tests as possible, and it has attracted renewed interest during the COVID-19 pandemic. A long-standing assumption in the probabilistic variant of the group testing problem is that individuals are infected by the disease independently. However, this assumption rarely holds in practice, as diseases often spread through interactions between individuals and therefore cause infections to be correlated. Inspired by characteristics of COVID-19 and other infectious diseases, we introduce an infection model over networks which generalizes the traditional i.i. d. model from probabilistic group testing. Under this model, we ask whether knowledge of the network structure can be leveraged to perform group testing more efficiently, focusing specifically on community-structured graphs drawn from the stochastic block model. We prove that a simple community-aware algorithm outperforms the baseline binary splitting algorithm when the model parameters are conducive to “strong community structure. ” Moreover, our novel lower bounds imply that the community-aware algorithm is order-optimal in certain parameter regimes. We extend our bounds to the noisy setting and support our results with numerical experiments.
Design of Double Closed-Loop Boost Converter Controller to Reduce Transient Voltage Dip for Sudden Load Connection

The paper presents a double closed-loop boost converter controller. For regulating output voltage of the boost converter a capacitor, a power switch, and a diode are used. The traditional buck-boost converter has a simple design that achieves high efficiency. Simulink is used to simulate a double closed-loop controller in this manuscript. A double loop is made up of an inner and an outer loop. The outer loop supplies the controller with boost output voltage, while the inner loop supplies the controller with inductor current. The sliding mode controller is regulated by inner loop, whereas the PI controller is regulated by its outer loop. The suggested system is capable of high-voltage photovoltaic applications by using inductors and low-voltage semiconductor devices. The dual closed-loop controller contributes to the achievement of a stable DC output voltage. The converter is capable of producing a broad range of noninverting output voltages. The proposed converter surpasses traditional models in terms of efficiency, voltage gain, ripple content, and dynamic responsiveness. This closed-loop system is durable and dependable, with constant transients and output voltage.
Effects of Data Including Visual Presentation and Rest Time on Classification of Motor Imagery of Using Brain-Computer Interface Competition Datasets

Herein, we investigated the effects of using time segments, including visual presentation, motor imagery, and rest time, as training data in a brain-computer interface (BCI) competition. Using BCI Competition IV 2a and 2b, many researchers have attempted to create more robust classifiers with higher classification accuracy. Some studies have also used visual presentation time and rest time as training data. However, the use of training data outside of motor imagery makes comparisons of performance across models difficult, and may lead to models that are overfitted to the experimental environment. In addition, it is possible that brain activity other than motor imagery is involved in visual presentation. Hence, to examine the effects of the selection of training data, we compared several classifiers, including linear discriminant analysis (LDA), support vector machine, and convolutional neural networks (CNN), trained with data including visual presentation time and rest time, with data only during motor imagery. The results showed an improvement in performance when BCI Competition IV 2a and 2b data included visual presentation information in the training data. For the greatest improvement among participants, training data with visual presentation improved the accuracy by 13.44 % and 10.14 % in BCI Competition IV 2a (participant 9) for LDA and CNN, respectively; and by 8.38 % and 16.68 % in BCI Competition IV 2b (participant 3) for LDA and CNN, respectively. Training data that includes visual presentation information improves model performance, therefore, we recommend using only motor imagery time to train the model.
Carbon Nanotube Field Effect Transistors: An Aspect of Low Power and High Frequency Applications of CNTFETs

This paper reviews, analyzes and proposes a new method for high frequency applications of carbon nanotube field effect Transistors (CNTFETs) with validation based of the simulation results for high frequency applications, which are promising technology for High-performance electronic devices. CNTFETs face several challenges due to their nanometer dimensions, including issues with leakage current, power consumption regulation, switching speed, and short channel effects like drain-induced barrier lowering (DIBL) and sub-threshold swing (SS). In this respect Low power CNTFET devices are of particular interest and will result in an enhanced and efficient battery life in modern portable smartphones and devices. With the increasing demand for high-speed and high-performance electronic devices, CNTFET technology is emerging as a promising field of research, especially in the context of 5G technology. Here, various methods, approaches, and strategies have been discussed to address the issues related to the performance of the device. By analyzing and discussing these new approaches published in the literature, researchers can identify the most effective strategies for improving the performance of electronic devices, including CNTFETs. Some of the new approaches that have been discussed in the literature including the use of novel materials for CNTFET Fabrication, such as graphene and other 2D materials, as well as the development of new device architectures that can reduce power consumption and improve switching speed. Other strategies include optimizing the gain, doping concentration and channel length of CNTFETs with respect to the operating frequency along with a dedicated emphasis on exploring the nascent techniques to reduce leakage current and minimize short-channel effects. A general conclusion is also presented that is based on comparison of contemporary technologies. Researchers in the field of electronic solid state devices, especially those working on CNTFET circuit design and fabrication, could benefit from these findings. Index Terms— Carbon Nanotubes, field-effect transistor.
Color Selection of Multimedia Arts Students at LPU-MNL in Building a Brand Identity: A Quantitative Study

This research study aims to determine how future graphic designers specifically; junior & senior year Multimedia Arts students of Lyceum of the Philippines University Manila (LPU-MNL) utilize colors in strategizing and designing a brand's visual identity. The purpose of this study is to gain an in-depth understanding on the usage of colors by future graphic designers and to analyze their thought process within their design strategy. Utilizing the census method, the researchers conducted survey questionnaires that made use of the Likert scale which were disseminated online. Through collecting the data, descriptive research analysis and an inferential statistical test has been employed quantitatively to scrutinize sixty-three (63) respondents and their design strategy for color selection in building a visual identity of a brand. This study determined which colors are effective for a brand identity according to graphic design students, as well as the implications of the findings being consistent with other studies related to colors and their attributions in categories such as corporate brand, personal brand, product brand, and service brand. Furthermore, the color selection of graphic design students at LPU-MNL in building a brand identity has demonstrated a significant relationship between color attributions (color psychology) and their design strategy in color selection.
ML Threat Detection with KDD Cup Data

Artificial Intelligence (AI) has rapidly emerged as a transformative technology, with applications in numerous fields including security. With the increasing frequency and sophistication of security threats, such as cyber-attacks and malware, the need for effective methods to detect and prevent these threats is critical. Machine learning (ML), a subfield of AI, has been applied in this context, as its ability to analyze large amounts of data and identify patterns can be particularly useful in detecting security threats. In this empirical study, we examine the use of machine learning techniques for security threat detection, with the goal of advancing the understanding of the most effective approaches and techniques.
SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks

We propose using federated learning (FL) in loiv Earth orbit (LEO) satellite networks for the Internet of Remote Things (IoRTs) to enable adaptive learning in massively networked devices while reducing costly traffic in satellite communication (SatCom). In this resource-constrained space setting, FL techniques in LEO satellite-based learning can improve system energy efficiency and save time. However, FL raises security and risk concerns, as local model updates can be used to infer device information by a hostile federated aggregator server in space. To address this, we propose using homomorphic-based encryption and decryption security techniques for federated aggregators and IoRTs. We evaluate the secure learning performance of our proposed framework using simulations on advanced datasets and aggregation approach. The results shoiv that compared to the benchmark scheme, the proposed secured computing networks improve communication overhead and latency performance.
Improving LVRT for DFIG used in WPCS under voltage dip

This work presents the crowbar active circuit protection method in case of voltage dips on the grid affecting the DFIG based wind power conversion system (WPCS). The sudden voltage drop produces high values of stator and rotor currents which also leads to an increase of the DC bus voltage. Without protection, the rotor side converter (RSC) can be destroyed and the DFIG can be damaged. With the proposed method, the currents will be limited to prescribed values at the onset and at the disappearance of the fault, which will allow to keep the DFIG connected to the grid during the voltage dip and to recover its normal operation after the fault disappearance.