Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrusion Detection System in IoT Smart city Environment using Tree-based Approach with Swarm-based Optimization for Multi-Step Cyber-Attack Dataset

With the popularity of IoT technologies, security has become a significant concern. IDS is a crucial component that analyses IoT network activity and categorizes it as either normal or anomalous due to susceptible attacks from hackers. An IDS safeguards a system's availability, integrity, and confidentiality. Because of resource constraints and algorithmic complexity, traditional ML IDS have drawbacks when applied to IoT networks. A lightweight IoT IDS that is optimal, inexpensive, and can minimize the loss function. So, to find the optimal model parameter values of the ML algorithm, hyperparameter tuning is performed by using populationbased swarm algorithms. The swarm-based algorithms control the learning process to extract the ML algorithms' relevant parameters and ultimately learn. A comparative study between the proposed hyperparameter tuning and default parameters predictions is made. The experimental results confirmed that the suggested strategy improves the security of the IoT environment. This approach is evaluated and determined to be promising on the Multi-Step Cyber-Attack (MSCA) benchmark IDS dataset. Additionally, the suggested method outperforms standard Tree-based ML parameters in accuracy while achieving a satisfactory trade-off between effectiveness and productivity.
Characteristics and Application of Intelligent Tutoring Systems: A Review

Recently, technology has developed rapidly, this has an effect on the integrated education system model with artificial intelligence techniques. Such educational models are known as Intelligent Tutoring Systems (ITSs). This paper aims to evaluate the characteristics of several ITS models that are applied to various fields of education. The original studies from 2009 to 2021 were extracted from the ACM Digitally Library, IEEEXplore, Scopus, Google Scholar, Science Direct, and Springer databases. From the search on the database, 26 papers were obtained which were used as the focus of evaluation based on the inclusion criteria as stated in the Kitchenham framework. From the results of the evaluation, the conclusion is that computer science is the largest field that applies ITS (50%). Meanwhile, artificial intelligence techniques are applied in ITS, the greatest frequency is NLP-based techniques (30.7%) and intelligent agent techniques (26.9%). These techniques are proven to be able to provide guidance and instructions according to user needs, evaluate processes learning for learners, defining new student models, and categorizing learners on ITS.
The early Danian hyperthermal event at Boltysh (Ukraine): Relation to Cretaceous-Paleogene boundary events

The Boltysh meteorite impact crater formed in the Ukrainian Shield on the margin of the Tethys Ocean a few thousand years before the Cretaceous-Paleogene boundary and was rapidly filled by a freshwater lake. Sediments filling the lake vary from early lacustrine turbidites and silts to ~300 m of fine silts, organic carbon–rich muds, oil shales, and lamenites that record early Danian terrestrial climate signals at high temporal resolution. Combined carbon isotope and palynological data show that the fine-grained organic carbon–rich lacustrine sediments preserve a uniquely complete and detailed negative carbon isotope excursion in an expanded section of several hundred meters. The position of the carbon isotope excursion in the early Danian stage of the Paleogene period, around 200 k.y. above the Cretaceous-Paleogene boundary, leads us to correlate it to the Dan-C2 carbon isotope excursion recorded in marine sediments of the same age. The more complete Boltysh carbon isotope excursion record indicates a δ 13 C shift of around -3‰, but also a more extended recovery period, strikingly similar in pattern to the highest fidelity carbon isotope excursion records available for the Toarcian and Paleocene-Eocene hyperthermal events. Changes in floral communities through the carbon isotope excursion recorded at Boltysh reflect changing biomes caused by rapidly warming climate, followed by recovery, indicating that this early Danian hyperthermal event had a similar duration to the Toarcian and Paleocene-Eocene events.
Intelligent Control of PMSM-Driven Electric Vehicles Using Fuzzy Logic and Genetic Algorithm

Transport electrification is a trend research topic and one of the biggest nowadays challenges. This paper discusses intelligent hybrid electric vehicle (HEV) control using direct torque control (DTC) based genetic algorithm (GA). An optimized estimator is used to control the electric vehicle (EV) using direct torque control technique. A proposed cost function was used along with DTC-SVM based genetic algorithm to provide a self-tuning for the three used vehicle regulators. By taking into account an international driving cycle and the external environment variables at play, an electric vehicle is put through realistic load circumstances. The EV responds well to the various control strategies that have been applied to it, according to the simulation findings, which are adequate. Also the use of evolutionary techniques has contributed significantly in minimizing torque and speed ripples during the tuning process. The obtained results using GA-DTC control approach have shown their superiority when compared to classical DTC and fuzzy logic based direct torque control as it will be seen throughout this paper.
Dark Web Traffic Detection Using Supervised Machine Learning

This paper presents a study on the application of supervised machine learning algorithms for the purpose of distinguishing and categorizing Virtual Private Network (VPN) and The Onion Router (TOR) traffic on the dark web. The dark web, characterized by its anonymity and inaccessibility, has become a popular platform for illicit activities such as drug trafficking, money laundering, and cybercrime. While VPNs and TOR can be used for legitimate purposes such as privacy protection and bypassing internet censorship, they can also be exploited by cybercriminals. The CIC-Darknet2020 dataset, which includes a comprehensive collection of network traffic captures from the dark web incorporating traffic features from both VPN and TOR technologies, is used for this study. We employ classification algorithms such as Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree classifiers to construct our model. The performance of the model is evaluated using parameters such as execution time, accuracy, precision, F-measure, and recall, utilizing five-fold and ten-fold cross-validation and 66/34 and 80/20 percentage splits. Our results show that the Decision Tree (J48) classifier outperforms other classifiers, achieving 99.6% accuracy with an execution time of 15 seconds for ten-fold cross-validation. The findings of this study have implications for enhancing cybersecurity measures in identifying and mitigating threats associated with VPN and TOR traffic on the dark web.
Fuzzy Logic Based Perturb-and-Observe Algorithm for Minimizing Output Fluctuation of a PV System

Photovoltaic systems generate direct current (DC) power by absorbing sunlight through photovoltaic cells. However, under non-uniform irradiance, multiple peaks in the Maximum Power Point Tracking (MPPT) pose challenges for precisely tracking the global maximum power point. Traditional methods often focus on local maximum power points instead of the global ones. This research aims to enhance the Perturb and Observe (P&O) approach and model the PV module to accurately track the Maximum Power Point (MPP). The study investigates the performance of PV arrays using models with non-uniform characteristics. While traditional MPPT techniques like perturb and observe (P&O) can track the MPP, they struggle with non-uniform irradiance, leading to output power fluctuations and losses. Simulation results demonstrate that the P&O-based MPPT algorithm, tailored for maximum power production, adjusts the operational point effectively. Comparing the conventional MPPT output voltage 17.8V under non-uniform irradiance of conventional P&O MPPT output voltage and a non-conventional Fuzzy Logic 33.8V under non-uniform irradiance indicates that the Fuzzy MPPT achieves 52.66% of the output voltage obtained by the non-conventional MPPT comparing to conventional MPPT. These algorithms offer improved precision and flexibility, although their implementation may require higher processing power and computational capabilities. Therefore, the next focus will be on developing a fuzzy logic based MPPT and enhancing the photovoltaic system.
On the Performance of Legendre State-Space Models in Short-Term Time Series Forecasting

State-space models (SSMs) are a class of fundamental models in control theory. SSMs are well-known for their concise mathematical representations and capability of capturing the evolving dynamics of systems. They have also proven useful in time series modeling. Recent studies suggest that SSMs are able to conceptually generalize to classic machine learning models (CNNs, RNNs and RNN-variants) as well as provide theoretical justification for the design of novel sequence models. In this work, we investigate one type of SSM, the Legendre Memory Unit (LMU) and LMU’s parallelized variant (LMUFFT), and propose a stacking strategy leveraging the LMUFFT backbone and a Deep Adaptive Input Normalization (DAIN) scheme. Model performance is evaluated using short-term time series forecasting tasks formulated from real-world data. The proposed structure outperforms the traditional machine learning models in efficiency and prediction capability. Our results also suggest that this family of state-space models has potential in the realm of machine learning research. By shedding light on the benefits of SSMs in short-term time series forecasting, we hope to pique the interest of machine learning researchers in the use of SSMs and inspire further investigation into novel model designs based on these models.
Antecedent of Purchase Intention on Green Products in Indonesia

Economic and technological developments are increasing over time, this affects environmental conditions and there have been real problems such as air pollution and plastic waste. This study aims to examine the positive influence of green brand image, green awareness, green advertising, ecological knowledge, environmental attitude and altruism toward green purchase intention. This study has six variables with the target population with the criteria of being at least 18 years old, and consumers who have used environmentally friendly products and consumers who are in Indonesia. Data collection in this study using online questionnaire to 170 respondents and used purposive sampling as a sampling method. The data analysis method for this study uses PLS-SEM with the Smart-PLS application. The results show that green brand image has a positive effect on green purchase intention, green awareness has a positive effect on green purchase intention, ecological knowledge has a positive effect on green purchase intention, and alturism has a positive effect on green purchase intention and the results of this study also show that green advertising and environmental attitude do not have a positive influence on green purchase intentions
Experimental Investigation of Millimeter-Wave 3D Image Projection Using Dielectric Lens for Security Application

Millimeter-wave (mmWave) imaging is an emerging technology that has recently gained a lot of attention due to its potential applications in various fields, including security. MmWave imaging enables the detection of objects that are typically difficult to observe using traditional imaging techniques. A reconstructed target image can be obtained by analyzing the recorded amplitude and phase of the scattering behavior from illuminated objects with a Fast-Fourier Transform (FFT) based scheme using a dielectric lens. Experiments have been performed to show the effectiveness of the proposed systems for not only the single object but also for multiple targets with different distances. In agreement with theory, the experiments reveal that the object can be identified and reconstructed, yielding promising results.
Design and Simulation of a Multiband Slotted Microstrip Patch Antenna for Wireless Applications

In this work, a multiband slotted microstrip patch antenna has been proposed and studied. FR-4 (lossy) has been chosen as a substrate material because it has a dielectric permittivity of 4.3. The proposed antenna can be utilized for several wireless applications such as Wi-Fi, Zigbee, Wi-Max, radio LAN, radars, satellite communications, etc., considering the L, S, and C frequency bands. The proposed antenna is designed and simulated in CST software. The antenna covers the frequency range of 2–10 GHz. The return loss, VSWR, 2D radiation pattern, and maximum gain vs. frequency plots are provided. The simulation results show the proposed antenna is a good fit for the aforementioned wireless applications. Moreover, this paper also studied the optimized antenna design. Particle Swarm Optimization (PSO) and Genetic Algorithms have been applied, and the optimized results show better performance.
Long Range High Resolution Imaging Radar with Digital Code Modulation (DCM) and Sparse Array

Automotive radars are an integral part of Advanced Driver Assistance Systems (ADAS) as well as Autonomous Driving Systems (ADS). The use cases of such systems require the ability to accurately detect targets (including stationary targets) at few hundred meters with very high angular resolution. Recently, radars built using waveforms based on Digital Code Modulation (DCM) have been introduced in the industry. These radars provide High Contrast Resolution (HCR) as well as better immunity to interference. In addition, the ability to integrate large number of channels on a single chip allows high angular resolution compared to conventional radars in the automotive space. In this paper, we demonstrate the use of sparse array on a DCM radar to further increase the angular resolution. We demonstrate a radar system with angular resolution 0.5° (azimuth) by 0.7° (elevation) using 12 transmitters and 8 receivers.
Serverless Computing: Architectural Paradigms, Challenges, and Future Directions in Cloud Technology

The concept of serverless computing has emerged as a significant paradigm shift in cloud technology, fundamentally transforming the processes involved in application development and deployment. This research paper offers a complete examination of serverless computing, focusing on its architectural principles, identifying the primary issues it poses, and exploring the potential future developments in this fast-progressing domain. The article begins by providing a comprehensive explanation of the basic ideas behind serverless computing. It places particular emphasis on its event-driven nature, scalability, and the paradigm change from infrastructure management to code-centric development. This study examines the architectural paradigms of serverless computing, specifically focusing on Function as a Service (FaaS) and Backend as a Service (BaaS). It does a comparative analysis of these paradigms, considering their respective use cases and benefits. This study not only discusses the existing issues in serverless computing but also presents potential future possibilities in the field. The paper investigates current developments in the field, including the utilization of serverless orchestration, the integration of hybrid cloud systems, and the advancement of tools for serverless development. Moreover, this study explores the possible ramifications of serverless technology on edge computing, Internet of Things (IoT), and machine learning applications. This research paper is a great resource for practitioners, academics, and organizations in the field of cloud technology that are interested in leveraging the advantages of serverless computing. This study amalgamates existing information, discerns areas of research that need further investigation, and provides valuable perspectives on the dynamic development of serverless technology. Consequently, it facilitates a more knowledgeable and inventive approach to the field of cloud computing.
A New Topology of PV-Linked Grid-Connected Modular Multi-level Converter Based on Current-fed Quadruple-Active Bridge Converter

: This paper proposes a new topology of modular multi-level converter (MMC) based on current-fed quadruple active bridge (CFQAB) converter for grid integration of photovoltaic (PV) systems. The MMC tpology includes multiple cascaded converter modules in each phase of a three phase system. Each individual module is further formed by a PV-linked CFQAB dc-dc converter to integrate the PV outputs and supply it to a high-voltage dc (HVDC) bus and further to a cascaded inverter to converter to the ac voltage for distribution grid. The proposed topology features a wide input voltage range, lower current ripple and smaller size due to use of CFQAB converter as the building block which makes it an ideal choice for PV applications. The operation principle of the MMC, converter modules and the control techniques have been presented in details. A scale-down prototype of the topology is implemented to validate the operation of the proposed topology and the control systems.
MIT Open Access Articles Attendee-Sourcing: Exploring The Design Space of Community-Informed Conference Scheduling

Constructing a good conference schedule for a large multi-track conference needs to take into account the preferences and constraints of organizers, authors, and attendees. Creating a schedule which has fewer conﬂicts for authors and attendees, and thematically coherent sessions is a challenging task. Cobi introduced an alternative approach to conference scheduling by engaging the community to play an active role in the planning process. The current Cobi pipeline consists of committee-sourcing and author-sourcing to plan a conference schedule. We further explore the design space of community-sourcing by introducing attendee-sourcing – a process that collects input from conference attendees and encodes them as preferences and constraints for creating sessions and schedule. For CHI 2014, a large multi-track conference in human-computer interaction with more than 3,000 attendees and 1,000 authors, we collected attendees’ preferences by making available all the accepted papers at the conference on a paper recommendation tool we built called Confer , for a period of 45 days before announcing the conference program (sessions and schedule). We compare the preferences marked on Confer with the preferences collected from Cobi’s author-sourcing approach. We show that attendee-sourcing can provide insights beyond what can be discovered by author-sourcing. For CHI 2014, the results show value in the method and attendees’ participation. It produces data that provides more alternatives in scheduling and complements data collected from other methods for creating coherent sessions and reducing conﬂicts.
MEXPLORER 1.0.0-rc.0 – A mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory

The open-source software MEXPLORER 1.0. 0-rc.0 1 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
An Improved YOLOv4 Lightweight Traffic Sign Detection Algorithm

—A new lightweight traffic sign detection algorithm based on YOLOv4 has been proposed to address the time-consuming issue of existing algorithms when training more parameters. The algorithm involves several key improvements to YOLOv4's backbone network and feature pyramid. Specifically, MobileNetv3 is used as the backbone feature extraction network, replacing standard convolution with depthwise separable convolution to reduce the number of parameters and computation in both the backbone network and feature pyramid. Furthermore, two SPP modules are added to the feature pyramid part to improve the detection accuracy of the model. The MobileNetv3 network is also improved by dropping the modules after layer 17 and performing PW (PointWise) convolution operations on feature layers to convert their dimensions and connect them with the detection layer. Finally, the a priori frame is initialized by applying the K-means++ algorithm to further enhance the detection accuracy of the algorithm. Experimental results on the CCTSDB dataset indicate that the proposed algorithm achieves a 1.7% increase in mAP (mean average precision), reduces the parameter amount by 197M, and increases detection speed by 25% compared to YOLOv4. These results demonstrate that the improved lightweight algorithm performs exceptionally well in traffic sign detection.
An Empirical Investigation Of Digital Learning Via Mobile Phones In Higher Education Institutes

The phenomenon of ubiquitous computing is often considered as one of the latest transformational educational paradigms, which foster an environment of anywhere, anytime learning. The present study explores learning through implementation of mobile computing devices, such as mobile/smartphones, in higher education sector of developing countries. Therefore, in this paper higher education quality indicators proposed by Kwan and Ng (1999) have been adopted for use in the e-learning context of Higher Education Institutes (in Pakistan), mainly for investigating preference of students’ on mobile phones and face to face against each indicator. In recent times, change has been observed in approaching e-learning especially through mobile phones due to proliferation of wireless electronic devices. The significance of technology does not imply that merely technology plays an imperative role in education process. However, teaching and learning strategies have also been altered majorly due to technological oriented learning setting. A quantitative survey from students was conducted in a blended learning model. Results revealed that students prefer to receive education in e-learning setting from mobile phones rather than face to face only (i.e. traditional learning). Furthermore, in teaching and learning process, technology does not only provide transparency of data but also enhances the efficacy and efficiency of students through universally available tools.
Economic Behavior of Information Acquisition: Impact on Peer Grading in MOOCs

A critical issue in operating massive open online courses (MOOCs) is the scalability of providing feedback. Because it is not feasible for instructors to grade a large number of students’ assignments, MOOCs employ peer grading systems. This study investigates the eﬃcacy of that practice when student graders are rational economic agents. We characterize grading as a process of (a) acquiring information to assess an assignment’s quality and (b) reporting a score. This process entails a trade-oﬀ between the cost of acquiring information and the beneﬁts of accurate grading. Since the true quality is not observable, any measure of inaccuracy must reference the actions of other graders, which motivates student graders to behave strategically. We present the unique equilibrium information level and reporting strategy of a homogeneous group of student graders and then examine the outcome of peer grading. We show how both the peer grading structure and the nature of MOOC courses aﬀect peer grading accuracy, and we identify conditions under which the process fails. There is a systematic grading bias toward the mean, which discourages students from learning. To improve current practice, we introduce a scale-shift grading scheme, theoretically examine how it can improve grading accuracy and adjust grading bias, and discuss how it can be practically implemented.