Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Question Generation for Repeated Testing to Improve Student Learning Outcome

In recent years, educational resources have gradually been digitized, and digital education platforms have gradually become popular. We use AI to accurately assist people in performing daily tasks through a machine learning process. In education, we can use AI in many situations, such as predicting student's learning outcome and discovering student's learning strategies. However, most solutions have not yet utilized modern AI capabilities, such as natural language processing. This research aims to help teachers use machines to automatically generate short answer questions to reduce the time for teachers to write exam questions. In addition, the main reason we focus on short answers is that many studies prove that short answer exercises can enhance student's long-term memory, thereby improving their learning performance. We propose an automatic question generation (AQG) system that combines syntax-base and semantics-base, in order to prove that the system is highly available and improve student's learning performance, we conducted experiments with 41 students. The experimental results show that student’s learning performance has been significantly improved, which means that by repeatedly testing the machine question generation system, students can deepen their long-term memory of course knowledge.
The Sensitivity Analysis of Coil Misalignment for a 200-kW Dynamic Wireless Power Transfer System with an LCC-S and LCC-P Compensation

The dynamic wireless power transfer systems may reduce the battery size of electric vehicles while maintaining the travel range. Similar to the stationary wireless power transfer systems, the compensation networks are desired in dynamic wireless charging systems at the primary and secondary sides to reduce the reactive power requirement from the source and improve the efficiency. Consequently, it is essential to understand the behavior of the compensation networks, especially the sensitivity to misalignments. This paper presents a sensitivity analysis of LCC-S and LCC-P compensation networks for a 200-kW power transfer with variations in the coupling coefficient due to the electric vehicle travel and misalignments between the pads. The sensitivity study combines the electromagnetic finite element analysis and circuit analysis to determine the impact of misalignments on the wireless power transfer system characteristics. The theoretical analysis is verified by conducting circuit simulations at discrete points to verify the accuracy of the mathematical model.
Refresh Algorithm for Ensuring 100% Memory Availability in Gain-Cell Embedded DRAM Macros

Gain-cell embedded DRAM (GC-eDRAM) is a dense, low power option for embedded memory implementation, supporting low supply voltages; however, it suffers from limited data retention time (DRT) and requires periodic refresh operations, limiting its use only to applications that can tolerate temporary memory blockages. In this work we propose a memory architecture based on a novel refreshing algorithm that provides 100% memory availability for the user, resulting in no performance loss for any possible access pattern. This approach allows the memory to have a standard SRAM interface (“vanilla interface”), supporting direct replacement of the SRAM memory with a GC-eDRAM memory. The algorithm/architecture was implemented in a 65 nm CMOS technology resulting in more than 20% area reduction compared with standard SRAM solutions, for large memory implementations.
Dual-Polarized Wearable Antenna/Rectenna for Full-Duplex and MIMO Simultaneous Wireless Information and Power Transfer (SWIPT)

Owing to the mobility of a wearable antenna and the unpredictable body-centric communications environment, dual-polarization antennas are essential for both communications and energy harvesting. This paper presents a dual-polarized four-port textile antenna/rectenna for wearable simultaneous wireless information and power transfer (SWIPT) applications. The proposed antenna utilizes dual ports for both off-body communication and energy harvesting from horizontal and vertical polarizations. The antenna maintains a 100 MHz bandwidth with an $S_{11}$ under −10 dB around 2.4 GHz in the presence and absence of the human body, and at least 10 dB small-signal and large-signal isolation between all ports. The antenna maintains a 70–88% measured total efficiency and 8.4–9.6 dBi gain for various on-phantom positions across both communication ports. The measured mutual coupling is under −10 dB between co-polarized rectenna/antenna ports, and under −16 dB between orthogonally-polarized ports. A high RF to DC peak power conversion efficiency of over 70% (±5%) is achieved with a broadside harvesting pattern. Based on the proposed antenna’s performance, SWIPT microstrip antennas can be adopted for both full-duplex and MIMO applications, significantly reducing the complexity of future battery-free networks for both wearable and non-wearable applications.
Diabetic Retinopathy detection using Weighted Filters and Classification using CNN

Diabetic retinopathy is one of the primary sources of blindness in the developed society of over 50 percent. The early diabetic diagnosis of retinopathy may help prevent severe risks of vision loss. A great deal of the analysis already done focuses on the manual retrieval of properties. Still, this essay attempts to diagnose diabetes retinopathy automatically in their multiple groups to include a new approach focused on a deep-neuronal network of Gray Wolf optimization (GWO). The Gaussian space theory and generic data structures improve the accuracy and quantity of the dataset to overcome unbalanced dataset problems. Original and added data sets are used to train and validate the proposed model. The model precision of the convolutional neural network in this paper for an increased data collection is 0.9691, which is ideal compared to the original data and other procedures.
Design and Analysis of High-Stiffness Hyperredundant Manipulator With Sigma-Shaped Wire Path and Rolling Joints

High stiffness is desirable for wire-driven underactuated hyperredundant manipulators used in minimally invasive surgery. However, limited structural stiffness can cause lateral and angular deflections by the lateral force and moment applied to the distal end of the manipulator. This letter proposes a Σ-shaped wire path, which is a combination of parallel and convergent wire paths, for a hyperredundant manipulator with rolling joints. The Σ-shaped wire path applied to the manipulator has the ability to maintain the position and orientation of its distal end for an applied lateral force on the surgical instrument tip. The simple mechanics of deflection of the manipulator with the Σ-shaped wire path are described, and straight-pose experiments were conducted to verify the deflection characteristics of the Σ-shaped wire path. An arbitrary bending-pose experiment was also conducted to verify the stiffness performance. It was observed that the proposed method could enhance the stiffness of the manipulator in both straight and arbitrarily bent poses.
Identifying Rank Attacks and Alert Application in WSN

Routing protocol for low power and lossy networks (RPL) is a fundamental routing protocol of 6LoWPAN, a centre correspondence standard for the Internet of Things. RPL outplay other wireless sensor and ad hoc routing protocols in the aspect of service (QoS), device management, and energy-saving performance. The Rank definition in RPL addresses several issues, such as path optimization, loop avoidance, and power overhead management. RPL rank and version number attacks are two types of the most common forms of RPL attacks, may have crucial ramification for RPL networks. The research directed upon these attacks includes considerable vulnerabilities and efficiency issues. The rank attack on sensor networks is perhaps the utmost common, posing a challenge to network connectivity by falling data or disrupting routing routes. This work presents a rank attack detection system focusing on RPL. Considering many of such issues a method has been proposed using spatial correlation function (SCF) and Dijkstra's algorithm considering parameters like energy and throughput.
Camera and LiDAR Sensor Fusion for 3D Object Tracking in a Collision Avoidance System

Advances in autonomous sensor technology are the driving force behind vehicle manufacturers to reduce traffic accidents and fatalities. This process led to the development of advanced driver assistant systems (ADAS). However, vision-based methods often suffer from limited fields of view and difficulty to extract accurate range information which is critical for vehicle detection. Vehicle detection is one of the most important issues for ADAS. The authors show one among many other possible solutions, to improve detection, and that is to rely on several different sensors such as a camera and LiDAR sensors. This paper describes the implementation of a collision-avoidance system (CSA), and the needed time-to-collision (TTC) estimation, using constant velocity model and C++ programming language. The TTC calculation is based on data obtained by tightly coupled LiDAR and camera sensors. In the solution we integrated several key points detectors and, focused on descriptors extraction and matching. As a final step, we used so called, sensor fusion to integrate LiDAR points into camera images and detect object in camera images using deep learning approach. For evaluation, we run several combinations of detectors and descriptors, analyze differences between time to collision estimations and demonstrate the functionality of the best method into an efficient implementation.
Electrical Generation of Intranasal Irritating Chemosensation

Percutaneous electrical stimulation modifies brain and nerve excitability and also stimulates various sensory organs. Although it can be applied to sensory information displays, virtual reality, and human augmentation, the effect on human intranasal chemosensation, including olfaction, is not clearly understood. In this paper, we introduce galvanic olfactory stimulation, in which non-invasive electrical stimulation induces virtual intranasal chemosensation. For this purpose, we designed an electrode configuration using a finite element analysis simulation. We then conducted two psychophysical experiments comparing different stimulation configurations and asking subjects to describe their sensation experiences. The results demonstrate that galvanic olfactory stimulation using a pair of electrodes at the nasal bridge and the dorsal surface of the neck induces virtual irritating intranasal chemosensation. This observation suggests that, at the very least, the proposed galvanic olfactory stimulation method induces irritating intranasal chemosensation. This implies that an electric current stimulates the trigeminal nerve in the inner region of the nasal cavity. To the best of our knowledge, this is the first report to demonstrate virtual intranasal chemosensation using non-invasive electrical stimulation of the external surface of the nose.
Cryptanalysis of Internet of Health Things Encryption Scheme Based on Chaotic Maps

Many encryption algorithms are designed to decrease the probability of cyberattacks by assuring data security as well as system and participant authentication. However, in the development of computer resources cryptanalytical techniques have been developed and performed competitively in information security with good results. In this paper, we reported security flaws in the recently offered encryption technique based on a chaotic map for Internet of Health Things (IoHT) security. The scheme was based on a new chaotic map, modified Mandelbrot set, and conditional shift algorithm asserting the encryption algorithm is secure. We have performed some cryptographic attacks to retrieve the key from the understudy cryptosystem. The key was retrieved in little computation by using a chosen-plaintext attack and one known plaintext ciphertext pair. The minimum execution time of performed attacks indicates the vulnerability of the diffusion-based encryption algorithm. To enhance the security of the understudy cryptographic algorithm, we have also suggested certain improvements.
Test Bed Development for Evaluating Extremely Fast Charging Stations

To evaluate the cyber-physical security of Extremely Fast Charging Stations and investigate its potential impact on the distribution systems, a hardware-in-the-loop-based testbed has been developed, which consists of a grid simulator, 350kW commercial EVSE unit, custom electric vehicle simulator, and RTDS. This paper describes the architecture of the test bed, testing methodology, EV simulator design, communication between EVSEs and EVs, and HIL simulation model. The full-scale test bed can serve as a multifunctional platform to facilitate various engineering studies and advanced research in a real-time manner with respect to XFC's operations, controls, and functionalities. RTDS simulation results show the potential impact of compromised 350kW charging stations on the microgrid considering grid-connected and islanded modes.
A Drowsiness Reduction Strategy Utilizing Visual Stimulation With Different Colors of Light: An fNIRS Study

Drowsiness during driving is a severe problem that must be addressed to improve road safety. Numerous counter-measures have been proposed to resolve this issue like adaptive environmental settings (temperature, sound, and light). The objective of this study was to accurately predict the effects of exposure to different colors of light on human drowsiness by using functional near-infrared spectroscopy and other physical measurements (heart rate and eye closure). We targeted two regions of the brain (visual and pre-frontal cortices). Twenty-three healthy subjects were investigated to evaluate all variables related to the awakening state, and twenty-one healthy subjects were also examined in the drowsy state evaluation. Eventually, the ten most suitable subjects were exposed to red, green, and blue lights under drowsy conditions, according to the experimental paradigm. Dim light was maintained in the experimental premises before and after colored light exposure to limit the results to those produced only in response to the desired stimuli. Eye closure, heart rate, and changes in oxy and deoxy hemoglobin concentrations were measured to characterize the condition (awake/drowsy) of the subject. A support vector machine classifier was used to identify the classification accuracy of awake and drowsy states. In conclusion, exposure to blue light triggered the activation of oxy hemoglobin in targeted brain regions; however, deoxy hemoglobin was not significantly affected by exposure to any of the colored lights. Noticeably, our study revealed that blue light exposure is more effective at reducing drowsiness than exposure to red and green lights.
Interactive AR App for Real-Time Analysis of Resistive Circuits

An augmented reality app for real-time analysis of direct current in resistive circuits is presented. The app allows the manipulation of circuit elements and computes the values of voltage and current intensity using the loop method and applying the Kirchhoff’s voltage law. The app can be used in theoretical classes and laboratories. The contributions of this paper are two-fold. First, the app has higher levels of interactivity than other apps in the same domain since it allows defining the configuration and the parameters of the circuit. Second, the app performs more complex computations than similar apps in real-time.
Internet Based Remote Laboratory Architecture for 3-Phase Induction Motor Control System Experiment

To impede the spread of the COVID-19 virus, governments around the world enacted several rules such as social distancing, local lockdown, and travel restriction in accordance with WHO suggestions. These rules have caused many things such as limitation of social interaction and increased limit of public places, including campuses, schools, and other educational institution. This condition leads to the difficulties of educational activities since most classes are either canceled or moved to an online platform. Instead of classes, most of the workshops, conferences, and other activities which are important to students have been canceled or postponed. To alleviate the pandemic impact in education especially for college students this work proposed a system for conduction remote practicum safely and effectively to support the education and experience of these students. Research or practicum is important to preserve student's hands-on experience so that the student's readiness to handle the post-campus job is adequate. In this research, the proposed system architecture enables internet-based remote access of the laboratory equipment via an API with the MQTT communication protocol. The experiment is tested under a three-phase induction motor. The results show that the proposed system has the potential ability to be implemented further in the practicum activity. The average transmission time of 0,388 seconds without the Live Stream feature and 0,679 seconds with the Live Stream feature. Also, the system reliability of the proposed system could reach 98,76 percent.
Methods for the Identification and Analysis of Human Errors in Current Military Aviation

The article is focused on the comparison of capabilities and principles of selected methods for the identification and analysis of human errors in contemporary military aviation. Common features, differences and interesting facts are examined of selected methods used in current, due to societal changes ever evolving, practice in investigating causes of aviation accidents. The purpose of the article is to call attention to useful logical links and principles in investigating the causes of human errors in military aviation safety in the Czech Republic as well as in other countries.
A Review on Smart Agriculture using IoT

Smart Agriculture is an emerging concept that refers to managing agriculture using IoT Technologies to extend the standard and quantity of products. Over the past few years, IoT has grown into one of the leading 21st century technology. The Internet of Things (IoT) explicate the network of physical objects integrated in sensors, software and other technology to connect and exchange data online with other systems and devices. This paper presents a review on smart agriculture using IoT by using various Sensors and cloud computing to store the information.
CoPIM: A Concurrency-aware PIM Workload Offloading Architecture for Graph Applications

Processing-in-Memory (PIM) is considered a promising solution to improve the performance of graph-computing applications by minimizing the data movement between the host and memory. Which workload to offload and how to offload it to PIM logic determine whether the PIM architecture is well utilized. Offloading too much or too little workload from the host processor to the PIM side could hurt overall performance. On the other hand, the offloading granularity needs to be representative without losing generality. In this paper, we present CoPIM, a novel PIM workload offloading architecture that can dynamically determine which portion of the graph workload can benefit more from PIM-side computation. CoPIM focuses on the loop code blocks of graph applications and evaluates the necessity of offloading based on a concurrent memory access model. We also provide detailed architectural designs to support the offloading. In this way, CoPIM reduces the size of offloading instructions and also improves the overall performance with less energy consumption. The experimental results show that compared with other state-of-the-art PIM workload offloading frameworks, CoPIM achieves a speedup by the geometric mean of 19.5% and 11.4% than PEI and GraphPIM, respectively. On the other hand, CoPIM also reduces the un-core energy consumption by 6.8% and 6.5% on average over PEI and GraphPIM, respectively.
A Novel Convolutional Neural Network Model for Musical Instruments’ Classification: A Deep Signal Processing Approach

Stress management is a challenge in this modern world, and several methods are being practiced such as diet, exercise, sleep, meditation and relaxation. One of the relaxation methods is the Music Therapy, where one listens to Vocal music, Instrumental music, Hindustani classical music, and Western music etc. In instrument music, various types of instruments are used and these instruments vary from country to country, which produce different sounds. Based on the sounds, humans recognize these instruments with certain efficacy that varies with the expertise. In this paper, we propose the classification of 14 categories of musical instruments, namely, bells, cello, clarinet, crotales, double Bass, flute, piano, saxophone, trombone, trumpet, vibraphone, viola, violin, and xylophone etc. based on the sound signal. A total of 8,365 spectrograms, across 14 classes, are used. We have deployed five pre-trained CNN models such as Efficientnet, Googlenet, ResNet, Squeezenet, and Mobilenet for transfer learning. Since the classification accuracies of these pre-trained models are not up to the expectations, hence, a custom designed architecture is proposed, which outperforms the pre-trained models giving 99.81 % classification accuracy. Hyper-parameters are varied during the experimentation and the results are compared with the state-of-the-art methods. The work is helpful for the practicing psychiatrists, in knowing what types of sounds manage, which kinds of stresses.
Comparative Study of Major Algorithms for Pest Detection in Maize Crop

Early-stage control of maize plant pest is a big issue in precision agriculture. Farmers can prevent pest only if they identified as early as possible and can avoid economic losses also. This type of process can minimize the usage of pesticides and can bring healthy crop. Here algorithm features are takes place for comparative study to identify the training time, training data, merits, demerits and accuracy. In this study, we found Faster R-CNN is good in accuracy but time delay was high. Faster R-CNN convolutional neural network is desirable to develop user friendly system for the farmers that can automatically identify pest. The object detection method can be further improved by adopting the proposed model. We proposed new algorithm to replace the drawback and to provide best result in both accuracy and time delay. IoU method supports to find the prediction object from the real object, hence the pest can identify from the crop. The pseudocode can use to develop the real time system to bring out the process in effective and also efficient manner. Some of the samples have initialize to check the performance metrics of the proposed algorithm.