Miracle-dz/UniFAR-FTUs huggingface dataset:
Science Learning Motivation in Rural Schools

Motivation has been identified as a great factor influencing the effectiveness of the teaching and learning process. This study aimed to look at the level of science learning motivation of students in rural secondary schools. The study was administered using a questionnaire on 237 Grade 11 students of a rural secondary schools who took general science subject. The data obtained were analyzed using Statistical Package for the Social Science Version 21.0 (SPSS 21.0) software. Mean scores and t-test were used to find out the level of student motivation and gender differences towards science learning. Findings show that both groups of male and female students have a high level of motivation to learn science. The learning foal orientation scale showed the highest mean value of 3.97 while the self -efficacy scale had the lowest mean score value of 3.57. The findings also show that there are significant differences between genders for the level of science learning motivation. Besides, the finding also showed that there is a strong and positive relation between science learning motivation and science achievement.
Paddy Disease Classifier using Deep learning Techniques

Agriculture is an important sector for self-sustainability and plays a major role in a nation's economy and growth. Lack of timely identification of plant disease may result in huge loss in yield and in the economy. The objective of the research work is to support a large community of farmers particularly involved in paddy farming to understand and predict the disease affected to the crop. This research work demonstrates the robustness of classifying the paddy leaf disease using deep neural networks. Pre-processing techniques such as data augmentation and median filter have been applied to the dataset to avoid overfitting and to improve the model performance and accuracy. A model has been generated and analyzed its performance using deep learning approach. Also, the feature extracted, and preprocessed data set was fed to several models and analyzed their performance using various accuracy metrics.
Anomalous GIDL Effect With Back Bias in FinFET: Physical Insights and Compact Modeling

In this work, we report a detailed study and modeling of bulk current in the low forward bias region. In the forward bias region, the bulk current shows a gate-induced drain leakage (GIDL) such as strong modulation with gate voltage although from the band alignment one would not expect any tunneling current. We propose physics-based modeling of this effect and derive a compact model for circuit simulators as state-of-the-art compact models are not suitable to capture this effect. The proposed model has been validated with measurement from Intel’s bulk FinFET device.
Trajectory Simplification with Reinforcement Learning

Trajectory data is used in various applications including traffic analysis, logistics, and mobility services. It is usually collected continuously by sensors and accumulated at a server resulting in big volume. A common practice is to conduct trajectory simplification which is to drop some points of a trajectory when they are being collected (online mode) and/or after they are accumulated (batch mode). Existing algorithms usually involve some decision making tasks (e.g., deciding which point to drop), for which, some human-crafted rules are used. In this paper, we propose to learn a policy for the decision making tasks via reinforcement learning (RL) and develop trajectory simplification methods based on the learned policy. Compared with existing algorithms, our RL-based methods are data-driven and can adapt to different dynamics underlying the problem. We conduct extensive experiments to verify that our RL-based methods compute simplified trajectories with smaller errors while running comparably fast (and faster in the batch mode) compared with existing methods.
Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of Things

The measurement of the concentration of suspended sediment in a water body is a very important content in the observation of hydrological elements, and it is also one of the important parameters for calculating the sediment resuspension flux. In order to accurately predict the distribution of lake sediment, this paper uses satellite remote sensing data to invert the suspended sediment concentration. The key to the quantitative inversion is the atmospheric correction and the suspended sediment concentration inversion algorithm. In this paper, satellite remote sensing technology and Internet of Things technology are combined to establish a new type of lake suspended sediment concentration distribution model. First of all, this paper combines the results of satellite remote sensing inversion and the results of on-site water sample inspections of the Internet of Things to obtain the original hydrological data of suspended sediment in the lake. Secondly, this paper combines ADAM with deep learning technology to simulate the lake flow field and predict the dynamic process of suspended sediment pollution under different conditions. Finally, through experimental simulation and field sampling experiments, the validity of the lake suspended sediment concentration model established in this paper is verified. This model can provide assistance for relevant agencies to grasp the temporal and spatial distribution of suspended sediment concentration in regional lakes in a comprehensive and timely manner, and can obtain the overall characteristics of the study area and the impact of humanistic engineering construction.
Effect of Directional Movement on Dynamic Light Scattering

Dynamic light scattering is a standard technique of nano-particle sizing in colloid systems. However, it is difficult to measure the particle size accurately in the flowing dispersion, which is inevitable in many applications, such as on-line measurement. In this paper, we present that the wave vector of the scattered light, which is averaged to a constant in traditional technique, varies with time when the particles undergoing not only the random motion but also the directional movement. This variation results in an additional term of sinc function to the traditional intensity autocorrelation function of the scattered light, thus affect the final particle size determination, especially for large velocity of the directional movement. The experimental results agree well with our derivation. We believe this will facilitate the extension of the technique of the dynamic light scattering to a wider range of application, especially of the on-line measurement in the flowing dispersion.
Vector Hysteresis Modeling Coupled with a Loop-based Magnetic Equivalent Circuit

This paper proposes a method to include the anisotropic magnetic hysteresis property of the soft-magnetic material in the magnetic equivalent circuit (MEC) modeling. The loop-based MEC formulation is improved to handle the non-linearity of the anisotropic magnetic hysteresis. Single MEC element of NO27 electrical steel is modeled in 2-D under both rotating and alternating magnetic fields to achieve an accurate iron loss estimation for different excitation frequencies. The developed model is coupled with both the single-valued BH curve (SVC) and the hysteresis BH loop (HL) of the non-linear magnetic material. The magnetic flux density is calculated as the output of the model and used for the comparison of both magnetic saturation calculations. The results show that an alternating magnetic field in a single direction with a peak value smaller than 300 A/m causes a discrepancy of more than 10% between HL and SVC, where the knee point of the SVC is located at 200 A/m. Although the SVC approximation gives realistic results under the deep magnetic saturation, it is not capable of estimating the iron loss accurately. The proposed model with the HL calculates the core loss density as 4 and 28 W/kg for 1000 A/m peak value with 50 and 200 Hz alternating magnetic field excitation, respectively.
Device Parameter-Based Analytical Modeling of Power Supply Induced Jitter in CMOS Inverters

This article presents an analytical approach to determine jitter for a CMOS inverter in the presence of power supply noise (PSN). The deviation in the transition edge of the output signal from its ideal timing is modeled accurately for each transition. A power series method is used to solve differential equations for different regions of transistors during output transition. The PSN has been expressed in Taylor series expression, aids to derive closed-form equation for time interval error (TIE). The obtained results from the proposed methodology closely match with electronic design automation (EDA) simulator results and verified on 40 nm Taiwan Semiconductor Manufacturing Company (TSMC) and 28 nm United Microelectronics Corporation (UMC) foundries, demonstrating accurate modeling of jitter.
CR-LPWAN: issues, solutions and research directions

Low Power Wide Area Network (LPWAN) is a network technology that emanated from the swift advancement of the Internet of Things (IoT) market. It is characterized by low cost, long-range communications, low power consumptions, and better area coverage. However, LPWAN is faced with several challenges such as scalability, security, coexistence, management, and adoption. Recently, Cognitive Radio-LPWAN (CR-LPWAN) has been introduced to address some of these challenges to preserve the benefits of LPWAN. Therefore, this paper surveys and analyses recent works on CR-LPWAN to identify the existing challenges, possible solutions and open issues as research directions. This paper specifically focused on relevant works that addressed issues in standardization, design, development, and architecture and identified research directions for improving CR-LPWAN. About twenty (20) relevant articles were explored, and the findings revealed the existence of several issues and proposed solutions in the CR-LPWAN realm. The findings also revealed CR-LPWAN as a promising wireless communication technology and with more research attention, CR-LPWAN can be improved significantly.
Efficient Shapelet Discovery for Time Series Classification (Extended Abstract)

Time-series shapelets are discriminative subsequences, recently found effective for time series classification (TSC). It is evident that the quality of shapelets is crucial to the accuracy of TSC. However, major research has focused on building accurate models from some shapelet candidates. To determine such candidates, existing studies are surprisingly simple, e.g., enumerating subsequences of some fixed lengths, or randomly selecting some subsequences as shapelet candidates. The major bulk of computation is then on building the model from the candidates. In this paper, we propose a novel efficient shapelet discovery method, called BSPCOVER, to discover a set of high-quality shapelet candidates for model building. We have conducted extensive experiments with well-known UCR time-series datasets and representative state-of-the-art methods. Results show that BSPCOVER speeds up the state-of-the-art methods by more than 70 times, and the accuracy is often comparable to or higher than existing works.
Performance Prediction of Virtual Machines via Transfer Learning of Bayesian Network

Bayesian Network (BN) can quantify the uncertain relationships among the multiple Virtual Machine (VM) related features and then predict the VM performance accurately. However, when the settings of hardware/software or the loads running on the VMs change over time, the VM-related features might be different, which will lead to the modification of VM performance prediction results. Thus, we resort to the transfer learning method and then propose a novel BN updating model, called BN_Transfer. BN_Transfer revises the weights of the updated instances constantly, and then combine the Maximum Likelihood Estimation and the hill-climbing methods to modify the parameters and structures of BN accordingly. The experiments conducted on the Alibaba published datasets and the benchmark running results on our simulated platform have shown that the BN_ Transfer can update the BN effectively as well as predict the performance of VM accurately.
Image Enhancement of Optical Coherence Tomography using Deep Learning

Optical coherence tomography (OCT) images are widely used in the clinical diagnosis of diseases because they can obtain high-resolution images in real-time. However, due to the noise interference generated during the signal acquisition, there will be pixel jitters in the OCT images. Aiming at the pixel imaging jitter problem caused by signal transmission interference, an improved UNET network framework in deep learning is proposed to construct an OCT image correction model. This model forms a mapping from the input image X to the output image Y by taking advantage of the deep network structure of UNET. Through 200 iteration training, the loss value is reduced to the lowest level in this model to realize OCT image correction. Finally, the validity of the proposed method was proved by calculating the similarity of corrected images.
Evaluating the Maximum Directional Kinematic Capability of a Redundant Manipulator Based on Allowable Velocity and Force

In this study, a new concept of allowable velocity and force is proposed to precisely evaluate the maximum directional kinematic capability of a redundant manipulator. For a general redundant manipulator, an optimization problem is formulated to determine the maximum achievable velocity and force projected along the base direction at any target position in the workspace. This provides quantitative information on allowable (i.e., maximum directional) velocity and force to be precisely visualized in 2D and 3D complicated shapes, which conventional manipulability ellipsoid cannot provide. As application examples, allowable velocity and force are evaluated for a distributed actuation mechanism (DAM)-based three-link planar manipulator, 3RRR planar parallel manipulator, and the UR5 robot (a spatial manipulator with 6 degrees of freedom). The simulation and experimental results validate that the proposed method can precisely determine allowable velocity and force, thereby contributing to planning the optimal operation for a given task.
StO2 Stress Detection Based on Fewer Wavelengths Through Linear Prediction Algorithm

In recent years, tissue oxygen saturation (StO2) based on hyperspectral imaging (HSI) technology has been deeply studied in the field of affective computing. HSI StO2 is a kind of non-contact physiological signal, which can reveal people's potential emotions. However, at this stage, the number of bands to generate StO2 is too many to achieve real-time emotion detection. Therefore, based on the publicly available HSI stress database, we used a Linear Prediction (LP) algorithm to select only 8 characteristic bands from the original 106 bands to generate StO2 and performed the task of identifying psychological stress and physical stress. The experimental results showed that the recognition rate of StO2 generated based on the selected 8 bands in stress detection is very close to (even higher) that of the original 106 bands, and reaches 84.44% when using the Bayes classifier.
Analysis of Lifetime-Limiting Defects in Cast-Mono Silicon Using Injection-Dependent Lifetime Spectroscopy Methods

Seed-assisted cast-mono silicon for photovoltaic (PV) application is a relevant alternative to the conventional Czochralski (Cz) in terms of cost and carbon footprint. Despite the remarkable PV conversion efficiencies reached when combining this material with novel cell concepts, cast-mono industrial deployment is infringed by the propagation of dislocation clusters during crystallization, inducing variations in final wafers quality. Indeed, some wafers feature dislocation-rich regions whose recombination activity is not fully understood. This article aims at providing insights into the carrier recombination in such regions. We focus on n-type cast mono grown from a compensated feedstock in order to obtain samples with the same crystallographic properties but different electron concentration (<italic>n</italic><sub>0</sub>) values. The bulk carrier lifetime in the dislocation-rich region could be approximated by the combination of two discrete defects for which the symmetry factor <italic>k</italic> could be estimated first by exploiting the relationship between the defect lifetime gradient with 1/<italic>n</italic><sub>0</sub>. Both defects energy positions (<italic>E<sub>T</sub></italic>) could be obtained, thanks to the defect parameter surface solution method. We analyzed the variations of the first defect Shockley–Read–Hall (SRH) parameters (<italic>E<sub>T</sub></italic>, <italic>k</italic>, and hole lifetime constant <italic>τ<sub>p</sub></italic><sub>0</sub>) with <italic>n</italic><sub>0</sub> and for temperatures ranging from 30 to 75 °C. A temperature-assisted transition of the first defect from one state to another was highlighted and was found to be enhanced by low <italic>n</italic><sub>0</sub> values. For each state, the SRH parameters were extracted.
LHist: Towards Learning Multi-dimensional Histogram for Massive Spatial Data

Data synopsis is widely adopted to speed-up query processing over large spatial databases. As one of the most popular spatial data synopses, multi-dimensional histograms (MH) have been studied and adopted by modern DBMS and analytical systems for decades. However, existing MH construction techniques highly rely on expert knowledge and statistical assumptions, making them hard to achieve consistently satisfactory performance across different datasets. Inspired by the emerging learned index techniques where the widely used index structures like B-tree can be further improved by integrating simple machine learning models, in this paper, we propose a learned data synopsis technique named Learned Multi-dimensional Histogram (LHist). Compared with the traditional data synopsis techniques, LHist is fully data-driven, easy-to-implement, and has the potential to achieve better storage-accuracy trade-off. On the typical task of range COUNT query estimation, the extensive experimental studies on large-scale real-world datasets and synthetic benchmarks reveal that LHist can outperform the existing synopsis structures in terms of storage cost, query processing efficiency, and estimation accuracy.
Investigation of the Impact of Hot-Carrier-Induced Interface State Generation on Carrier Mobility in nMOSFET

A comprehensive investigation on the hot-carrier-induced interface state generation and its impact on carrier mobility in nMOSFET is performed. <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> compact modeling and charge pumping (CP) characterization are used as independent ways to evaluate the interface state density as a function of hot-carrier-induced aging. From the two techniques, similar power-law time exponents of the interface state density kinetics are obtained. Assisted by the quasi-spectroscopic (temperature-resolved) CP measurement, the extracted interface state density is further correlated with the <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> modeling results: an universal mobility degradation normalization parameter <inline-formula> <tex-math notation="LaTeX">${N} _{\text {it,ref}} = {\sim }\, {4.1} \times {10}^{{11}}$ </tex-math></inline-formula>/cm<sup>2</sup> is reported, irrespective of the effective oxide thickness (EOT), stress temperature, or the relative degradation of the device under test (DUT). Supported by the fundamental principles deployed in the derivation and the broad range of experimental conditions considered for its validation, the reported normalization parameter could serve as a modeling constant in the commonly used <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> compact models to correlate the mobility degradation with the interface state density induced by hot carrier stress.
Automatic Detection of Vehicle Congestion by Using Roadside Unit

The presence of Roadside Units (RSUs) helps network loads to be expanded to the other nodes that have already been far away from frequent node exposure. We proposed in this work utilizing the mobile node to operate as a roadside unit and operate data packet routing such as roadside units does. The main problem of utilizing the huge number of roadside units is the spending of huge time for data provision by a reduction in performance. Also, in this paper, we attempt using the various number of mobile nodes such as roadside units that are different from traditional roadside units such as the past is fixed and the second is active as a movement previous. The proposed method was executing by utilizing ad hoc on demand distance vector (AODV) routing is a path protocol for mobile ad-hoc networks (MANETs) and other wireless ad-hoc networks, this protocol designed for usage of ad-hoc mobile networks. Also, it's an active protocol, the routes are made only when they are needed, common routing tables, one entry single destination, and supplement numbers in conformity with determine whether routing information is up-to-date and to forestall routing loops. In this paper, mobile vehicles move randomly on highways, so in the event of a collision is too high, it is assumed that the vehicle will stop, and the collision site will be subject to accommodate more than one vehicle. Where vehicles are driven at high speed. Due to the driver's ignorance of the accident area, they can enter it, and thus the problem is magnified. The results achieved shows that numerous mobile nodes as a roadside unit may enhance the communication according to the computation of the average time delay and the link duration of the connection and reconnecting each node. Therefore, the results may reduce the delay time and maintain the connection for a longer period, as shown in the fourth simulation model.
Analysis and Compact Modeling of Fast Detrapping From Bandgap-Engineered Tunneling Oxide in 3-D NAND Flash Memories

We present the comprehensive analysis and the compact modeling methodology of fast electron detrapping from the bandgap engineered tunneling oxide (BE-TOX) in 3-D NAND flash memories. To this end, we develop a physics-based detrapping model considering the detailed electron dynamics in the BE-TOX and various emission paths of the detrapping mechanism. By the aid of the carrier density rates from the accurately calibrated model, we find that the major path of the charge loss can be direct tunneling (DT) or trap-to-band tunneling (TBT) depending on the temperature and the nitride layer position in the BE-TOX. In addition, we compare the model result and the fitted stretched exponential function. From those comparisons, we correlate several indicators (time constant and curve shape) of the retention characteristics with the major paths of the charge loss. Finally, our model facilitates the quantitative separation of the detrapping mechanism into different paths.