Miracle-dz/UniFAR-FTUs huggingface dataset:
A Convolutional Neural Network based Model with Improved Activation Function and Optimizer for Effective Intrusion Detection and Classification

Technological developments in today’s world have tied our financial, social and other facets of life to the Internet, expanding into our transportation, home appliances and more device with rising IoT technologies. Additionally, the dramatic growth in number of cyber-attacks have presented our sensitive information on Internet with critical security risks. To fix this problem, the Intrusion Detection System acts as a realistic method to identify cyber-attacks when they are underway or prior to them. Leveraging Deep Learning methods, we can utilize the most sophisticated multi-functional architectures available right now to identify and classify intrusions with maximum precision. Our paper proposes a Convolutional Neural Network model with mish activation function and Ranger optimizer that reaches a higher degree of precision compared to previous Deep Learning models and traditional CNN models that utilize ReLU activation function and Adam optimizer. In this study, we have used the relatively novel dataset CIC-IDS-2018 for testing which comprises six different varieties of attacks. Our model reaches an unprecedented accuracy of 98.9% that is the highest in multiclass classification with this dataset.
On the Development of Computer Thinking in the Process of Modern Teaching

In 2018, the Ministry of Education released the Action Plan for Education Informatization 2.0, which puts forward new requirements for modern education and teaching. Under the influence of the computer Internet, modern teaching has not only changed the way of teaching and learning, but also promoted the improvement of educational concept. In the face of the current new trend, how to cultivate students' computer thinking ability in the teaching of computer courses has gradually become a hot topic in the field of computer education at home and abroad. This paper mainly describes the concept of computer thinking, characteristics and research status at home and abroad, at the same time, comprehensive description of the specific significance of the development of computer thinking, as well as how to cultivate students' computer thinking, finally do a whole summary of computer thinking, and add their own thinking. Under the new situation, we should advocate the development of computer thinking, keep up with the pace of The Times, and attach importance to the cultivation of students' computer thinking, so that students can better deal with the problems in life by the course learning.
Computer-assisted Remedial Instruction Structured to reduce Dyslexia in children

the study is a remedial instruction strategy to improve reading ability of children with developmental dyslexia, strategy belongs to Structured Literacy category which align resources, knowledge, programs and standard explicit practices in a systematic and progressive way, the proposal include Testing Effect and some practices from PhAB/DI and WIST. The study report the first part of the project, a test to detect Developmental Dyslexia in order to detect children who do fit the strategy, every test consist in a set of tests and its relationships, the purpose is to use the results as first input of the software system and a continuous support the strategy on two axis, axis one: will help in the monitoring and sequential tracking of lessons and tests, in order to maintain systematic control. Axis two: will keep a statistical model of development for every children, this model is based on classical reading models (Dual-Route model of Coltheart and the theory Double Deficit of Wolf) and data from the tracking of lessons and tests, model will mirror the development model of reading process and will make recommendation to tutors about any observation of development of reading process. The methodology give a brief and useful account of participant’s profiles. The findings of this study are very similar to previous research studies on remedial training, show smooth tendencies but do not show clear relationships between variables, the early evaluation of the software system support was considered as a solid support of strategy and the continuous evaluations along with the strategy in a diagnostic-intervention approach was considered as a viable and functional strategy.
Clickbait in Social Media: Detection and Analysis of the Bait

Taking advantage of visual-centric social media's rising popularity, content creators have started using enticing images to lure users into clicking on bothersome clickbaits, in place of previously used text-based baits. In addition, the development of a single model to detect clickbait on multiple image-centric social media platforms is largely an unexplored problem. Therefore, we introduce a novel model that can detect visual clickbaits on both Instagram and Twitter posts. The proposed model consists of a stacking classifier framework composed of six base models (K-Nearest Neighbors, Support Vector Machine, XGBoost, Naive Bayes, Logistic Regression, and Multilayer Perceptron) and a meta-classifier (Random Forest). The developed classifier achieved an accuracy of 88.5% for Instagram posts and 85% for Twitter posts, which is an improvement over previous separate state-of-the-art models for both platforms. Additionally, the stated classifier does not use meta-features (e.g., the number of likes or followers) for classification, which helps to detect potential clickbaits right away, enhancing its applicability in real-time clickbait detection use cases. Furthermore, based on our analysis, we have drawn essential conclusions about the telling characteristics of clickbaits.
Lane Change Intention Classification of Surrounding Vehicles Utilizing Open Set Recognition

This paper proposes a classification algorithm utilizing an open set recognition concept to conservatively detect lane change intention of surrounding vehicles. Conservatively predicting the lane change intention of the surrounding vehicles is needed to improve adaptive cruise control (ACC) performance and avoid possible accidents. However, existing machine learning can make incorrect decisions due to information not included in the training data set or confused data even with probability. To cope with this problem, we present a classification algorithm using a multi-class support vector machine applying an open set recognition concept to detect the surrounding vehicles’ lane change intentions. Feature vectors are constructed from lateral information obtained by a Kalman filter using only radar and in-vehicle sensors. The open set recognition concept is adapted using Meta-Recognition based on binary classifiers scores. Furthermore, we analyze lateral information where an object vehicle changes lanes. From experimental results, we observe that the proposed system conservatively deals with wrong decisions and detects and cancels detecting the closest in-path vehicle (CIPV) earlier with average times of 1.4 sec and 0.4 sec compared with a commercial radar system, respectively.
A Novel Triple-band Meander Monopole Antenna Design for Military Applications

A triple meandered line monopole antenna is designed and analyzed for military applications which works above X-band region. The proposed antenna is designed to work at security applications which comes under X-BAND Region. Proposed antenna works in the frequencies of 8GHz, 10.2GHz, 11.6GHz, 14.23GHz which are used high band military applications(which comes for uplink X band region). The Proposed antenna designed analyzed using commercially quipped tool CST Microwave studio in which FR4 material has chosen as the substrate material. The results show good argument that the antenna is best suitable for the X band military applications
Single Switch ZVS Transformerless Resonant High Step-up Converter

In this paper, a single switch transformerless resonant high step-up non-isolated DC-DC converter is proposed. In this converter, soft-switching condition is provided for all semiconductor elements without any axillary switch. Due to high voltage gain and soft-switching of all semiconductor elements without using any axillary switch or coupled inductors, the proposed converter has a simple structure, low volume, weight and cost. In addition, there is a common ground between the input, output and the main switch, which simplifies the control. In this paper, the presented converter operational principle and steady-state analysis are discussed in details. To confirm the performed analysis, a 100-watt prototype is implemented in the laboratory.
Conducted EMI Investigation of a SiC-Based Multiplexing Converter for EV/PHEV

This paper investigates the conducted electromagnetic interference (EMI) characteristics of an on-board multiplexing converter that utilizes paralleled silicon carbide (SiC) devices to achieve high efficiency and high-power density for the application in electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). The multiplexing converter can operate as a three-phase interleaved DC/DC converter with a peak output power of 60 kW or an integrated two-stage non-isolated on-board charger (OBC) with an output power of 6.6 kW. The novelty of this paper is that it reveals that in the DC/DC mode the series resonance between the input negative lead parasitic inductance and the high frequency transmission line effects of the input power inductor will increase the conducted EMI levels, which can be mitigated by optimizing the winding methods of the power inductor or by adding a small capacitor, and reveals that in the non-isolated OBC mode the shielded cable on the battery-side will increase the grid-side EMI in the low frequency ranges, which can be suppressed by integrated installation of EMI filters without affecting the system power density. Firstly, parasitic parameters of the components of the multiplexing converter, such as the power inductors, the SiC MOSFETs and the shielded cables, are extracted and validated with impedance measurements, and the corresponding equivalent circuit models are established. Then, the conducted EMI simulation models of the multiplexing converter in different working modes are established. The influence of different interference sources and different parasitic parameters on the system EMI characteristics are analyzed, and the effective EMI suppression measures are given without affecting the system power density. Finally, the conducted EMI characteristics of the multiplexing converter and the effectiveness of the proposed EMI suppression measures are tested and verified through experiments.
Design and Implementation of Web Honeypot Detection System Based on Search Engine

In these years, with the development of search engines, it is more convenient for people to get information via the Internet. However, despite the benefit, search engines bring new challenges to network security. More and more hackers search for vulnerable websites and computers with the help of search engines and make large-scale attacks. This is quite a challenge to traditional cybersecurity concepts. Honeypot is a technology that is widely used in capturing and analyzing hackers' behaviors. By building virtual objectives with vulnerabilities, honeypots attract hackers and analyze the interaction process with hackers. But traditional honeypot can't attract hackers well and just wait for attackers. We design and implement a new Web honeypot detection system based on search engines, which can not only attract hackers but also can renew the content automotive to synchronize with the new attack technologies. This new kind of honeypot makes up for the shortcomings of traditional honeypots and has great advantages in finding zero-day attacks.
Structural and Modal Analysis of Cross Core Piezomagnetic Resistance Sensor

In order to ensure the safety and reliability of the cross core piezomagnetic resistance sensor, the finite element static analysis on the main structure and shell of the sensor was carried out by ANSYS Workbench software, and the stress and deformation distribution of each structure under different external force conditions were studied. The modal analysis of the main structure of the sensor was carried out to study the dynamic characteristics. The natural frequencies and corresponding vibration modes of the first six modes were obtained. The results show that under the given conditions, the sensor structure met the strength requirement, and the stress was concentrated in the groove of its shell. With the increase of external force, the maximum equivalent stress and total deformation of the main structure and shell of the sensor increased linearly, and the deformation of the shell changed rapidly; The calculated natural frequencies of the sensor under different orderscan provide a theoretical basis for its structural dynamic design.
Battery Energy Storage (BES) for Mitigation of Short-Term Power Fluctuations in Large-Scale Solar PV Plant Due to Cloud Movement

The installation of large-scale solar PV plants has been increasing rapidly worldwide, and its integration presents the electricity grid with new technical challenges, as a solar panel power plant fluctuates rapidly with the movement of clouds in the sky. One of the possible and widely adopted solutions to mitigate the large fluctuation in power generation is integrating large-size battery energy storage (BES) with the solar PV plant. In this paper, an MW size fundamental-switched voltage source converter (VSC) is used in the power conditioning system (PCS) of battery energy storage (BES) for the integration of a large-scale PV plant with the grid. The BES is used to make PV plant power dispatchable by mitigating the short-term power fluctuations due to clouds' movement. For the solar PV plant power generation smoothening, a simple moving average (SMA) algorithm with a battery state of charge (SOC) control (SMA-SOC algorithm) is used in BES. In the Matlab/Simulink environment, a 10 MW solar PV plant with 5MW/2.5 MWh BES configuration is realized and then implemented in an OPAL-RT real-time simulator to validate the concept.
Shipborne Vessel Monitoring System using Automatic Identification System Packet Encapsulation and LoRa Packet Propagation

This paper presents a shipborne vessel monitoring system, where position reports of the shipborne vessel are transmitted with the use of LoRa technology, and is received by a receiver module, where the position data is parsed and displayed for the end-user's consumption. This work provides an alternative ship-tracking system to the existing Automatic Identification System (AIS), where the packet encapsulation used for AIS devices will also be used for the LoRa-based Vessel Monitoring System.
Electromagnetic Response of SiO₂@Fe₃O₄ Core–Shell Nanostructures in the THz Regime

Magnetite (Fe3O4) nanoparticles (NPs) of diameter 100 nm and nano-hollow spheres (NHSs) of diameter 100, 185, 250, 350, and 725 nm have been synthesized by a facile one-step template-free solvothermal technique. Silica (SiO2) coating on their surface has been carried out following the Stöber method. X-ray diffraction and high-resolution transmission electron microscope images confirmed the phase and morphology of the nanostructures (NSs). Furthermore, we have investigated the terahertz (THz) wave absorption properties of SiO2@Fe3O4 shell/shell NSs in the frequency range of 0.9–2.0 THz. Detailed morphology and size-dependent THz absorption study proves that these particles can be very useful as electromagnetic shielding devices.
Investigation of Modern Ransomware Key Generation Methods: A Review

For years and the world of cyber is suffering from the most pernicious malware infection known as Ransomware which sat on top of the costliest malware attacks. Ransomware is considered to be a malicious software that attacks individuals as well as organizations for monetary purposes, it utilizes different types of encryption ways to encrypt important and sensitive files of the victims, extorting them to pay a ransom in order to decrypt the infected files. The heart action of the ransomware is encrypting the files of the victim with an encryption key that will make the victim unable to get the original files unless he has the decryption key, which won't happen unless he pays the ransom to the attacker. As the core function of the ransomware to encrypt the victim's files, it will use keys, taking into consideration the cryptosystems that the ransomware uses in its attack. Without a strong, secure, and hard to retrieve encryption keys, the ransomware will be a mere flimsy attack [1] leaving by that the attacker without getting their desired ransom from the victim. In this review paper we will discuss the ransomware encryption keys research area by going through the previous work of the researchers who focused their study on the ransomware encryption keys, from the point of how the ransomware authors get their keys, where they generate them, how they manage those keys in order to keep them safe and away from the victim.
Spatiotemporal Residual Network for Dynamic Scene Recognition based on ResNeXt

Dynamic scene recognition is a fundamental task in computer vision, focusing on the method of classifying videos by analyzing dynamic changes of different scenes. Modeling the spatiotemporal information of dynamic scenes in videos is the main challenge of the task. To solve this problem, this paper proposed a spatiotemporal residual network model based on ResNeXt. Our model uses the 2D deep convolutional network for spatial information extraction, and the residual units of ResNeXt are transformed to spacetime to increase the network’s temporal receptive field making it possible to extract temporal features in videos. In addition, to improve the generalization ability and prevent overfitting, we extend the global pooling strategy from spatial to temporal. On both of the static and moving subsets of the YUP++ dataset, the classification accuracies are improved compared with state-of-the-art methods, which indicates that our optimized method could make better use of spatiotemporal information for dynamic scene recognition.
Time-Gated and Multi-Junction SPADs in Standard 65 nm CMOS Technology

SPADs (Single-Photon Avalanche Diodes) are important detectors for a wide range of applications including positron emission tomography, Raman spectroscopy, light detection and ranging, and quantum key distribution. For some applications, custom image sensor technologies are used, but at a higher cost and lower performance imagers when compared to implementation in a standard planar CMOS technology. In this paper, we explore time-gating and multi-junction techniques to improve the SPAD’s performance in smaller standard planar CMOS processes to take advantage of their potential for monolithic integration with other advanced, mixed-signal circuitry for simple, low-cost, high-performance imaging solutions. A passively quenched, unbuffered, triple-junction SPAD structure was designed in a standard 65 nm CMOS process from TSMC. The characterization of the SPAD junctions in this process is the first in literature and proves useful for SPAD designers aiming for advanced CMOS technology nodes. The time-gated (TG) pixel design used the top shallow junction. The potential for improved photon detection efficiency and wavelength distinction through a multi-junction design was investigated. Our testing demonstrated that the proposed implementation of the triple-junction SPAD in this technology node is not suitable for wavelength distinction. The TG design achieved a fill-factor of 28.6%, and at an excess voltage of 300 mV, it achieved a peak photon detection efficiency of ~2.1% at 440 nm, <1% afterpulsing probability for hold-off times >22 ns, and <200 ps timing jitter.
Impact Study of a Microgrid with Battery Energy Storage System (BESS) and Hybrid Distributed Energy Resources using MATLAB Simulink and T-test Analysis

Power grid's most common problem is the unpredictable occurrence of the fault on its system. External and internal faults in power system network can result to severe instability and reduces the reliability of the electrical system. For this paper, the researchers tried to mimic the situation wherein the microgrid is disconnected to the grid or islanded while having a renewable energy as its source. Testing the reliability of the system by injecting a three-phase fault and single line to ground fault will give a better analysis of the stability of the system. This opens the opportunity for engineers to innovate on how to improve the performance and efficiency of renewable energy for consumers. With solar power and wind power becoming the more commonly used renewable energy source in most places, this paper will tackle if it can further enhance their reliability in places with independent electrical grid. The researchers use MATLAB/Simulink to model a microgrid consisting of a Photovoltaic (PV) for solar power and Wind Energy Conversion System (WECS) for the wind power. Looking to improve the system, a Battery Energy Storage System (BESS) is installed together with PV and WECS, forming a Hybrid Distributed Energy Resource. Simulation different test cases wherein various scenarios occur. For this research, three phase fault and single-line-to-ground fault was simulated. This paper will show if a battery will truly help the performance of the microgrid system, how much will it contribute, and what type of scenario where the BESS will be most useful by using the method of t-test analysis. T-test yielded a t-stat value of −6.65 and 17.55 for SLG fault load real power and load reactive power, respectively, falls on the reject region, which is less than the negative value of the two critical two tail and more than the positive value of the two critical two tail. It indicates that there is a significant difference between systems with battery and systems without battery.
Recognition of Digits on License Plate by RAISR with Changing Contrast Ratio

We have researched unreadable digits on license plates of cars could be read by machine learning classifiers trained with CG images. Digits on license plates can be evidences for climes if they can be read and CG does not require huge numbers of real images for training. We reached a conclusion that some digits could be read with high accuracy when training images were effectively extended. We also found that test images with emphasized edges were effectively enhanced by image resolution enhancement. Therefore, in this paper, we evaluated how much edge emphasis by changing contrast ratio influences the resolution enhancement. The results show that enhancement of contrast ratio increases classification accuracy of unreadable digits with the resolution enhancement. On the other hand, the contrast ratio enhancement does not work for images with too low contrast ratio. Also, it decreases the classification accuracy on the contrary when the contrast ratio of original images is enough high. We evaluated which contrast ratio suits edge emphasis.
Vibration-based Structural Health Monitoring System for Bridges using ADXL345 Accelerometer with MATLAB Standalone Application

The study designed a Vibration-based Structural Health Monitoring (VBSHM) using ADXL345 accelerometer with a wireless sensor network using radio frequency module to detect crack and monitor status of bridge in order to prevent catastrophic failures. A microcontroller ATmega2560 Arduino controls the activities of the system and a real time clock module was added for time synchronization. MATLAB standalone application was built for analyzation and data recording. The researchers used three sensor nodes as the primary device of the system and base station for data monitoring. To test the functionality and reliability of the system, the researchers used three miniature bridges. At 100 hertz the bridges were tested under two factors, the intensity of crack and strength of vibration. A two-way ANOVA was used to analyze the effect of the factors on the readings of the system. The output shows intensities of crack and strength of vibration has a significant effect on the readings.