Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Query Intention in Search Queries of Learners in Blended Learning Environments

In spite of the flexibility of learning, at any time, any place, and any pace, the student engagement decides success of the e-learning portals today. The ‘Search’ plays vital role in directing the learners to land in the correct web portal that can satisfy their needs and goals in the blended learning environments. The objective of this experimental study is to collect the user behavior of the online learners by using the web analytic tools ‘Google Analytics’ and the ‘Google Search Console’ and the faculty and web administrators can verify the fulfilment of the learner’s search intent. The datasets pertaining to the user behavior are collected from the tSearch Consolet data in the period Jan-Dec-2020 and analyzed using the metrics such as position, impressions, clicks. The metrics ‘Page views’ and ‘Total time on site’ are indicators of students’ intention to use the educational web site to achieve their academic goals like improving their grades in the final examination. The metrics such as Page views can predict the exam score using a linear relation. The multiple regression analysis done on the Google analytic metrics of the experimental data confirmed that these metrics can predict the exam score in the blended e-Learning portals and the results are statistically significant. Individually the learners’ performance has been improved with the increase in Pageviews reported by the web analytic reports. The click through data metrics helps the faculty to make improvement in the course website so as to ensure that the learners’ needs are successfully met by the online experimental portal.
Transformer-based NLoS detection in UWB localization systems

Ultra-Wide-Band (UWB) communication is recognized as one of the most promising technologies for indoor localization due to its ability to capture a high-resolution channel impulse response (CIR) at the receiver and penetrate walls and other obstacles. In UWB-based localization systems, a distance between the target object and anchor nodes is estimated by measuring the travel times of UWB signals. In indoor environments, numerous obstacles that block the Line-of-Sight (LoS) path between the target object and anchor nodes induce errors in the estimation of the time of signal flight. As a result, localization accuracy deteriorates. One of the techniques to address this issue is to discard range measurements of non-LoS (NLoS) anchors or to handle them differently when calculating the target position. In this paper, we apply Transformer deep learning model to detect NLoS channel conditions from the raw CIR data. The model has been trained and evaluated on a realistic dataset collected in an industrial environment. The results show performance improvement over the state-of-the-art convolutional neural network model.
Analysis of Coloboma Defected Eyes using Automated Pre-trained CNN Models

Coloboma is a defect of the iris of the eye. It takes place before the birth of the child. It can affect one or both eyes. If treated from the early stages, it can restore most of that person’s vision. To detect it in the early stages, we need the help of machine learning techniques and pre-trained CNN models so that the accurate and speedy results do not take the time it usually takes otherwise. VGG16, VGG19, and Inception V3 are three feature extraction models discussed in this article that provide crucial yet simple combination techniques that can lead to more accurate predictions. We compare the above extraction models with different classifiers for the highest accuracy. After examining the ROC and Confusion Matrix, we find the best suitable model and classifier pair. In this case, we get the largest area under the curve, 95.9%, with inception V3 and neural network classifier.
Quantized Prescribed Performance Control for Second-Order Nonlinear Systems

This paper designs an output tracking controller for a class of uncertain second-order nonlinear systems with input quantization to solve the prescribed performance control problem. The performance function restrains the convergence rate and precision of the output tracking error. The barrier function is used to confine this error. A simple input quantizer is specially designed for the controller. The resulting control strategy ensures that the prescribed output tracking performance is achieved and all the closed-loop signals are bounded. The control strategy is verified through the simulation result.
Vision based sidewalk recognition for walking assistive robot in outdoor environments

Robot assisting elderly people to walk in outdoor environments is important especially in aging societies. Robot outdoor navigation is really challenging due to the large variety of environments and situations. For safe robot navigation, the robots must recognize the sidewalks while avoiding pedestrians and obstacles. In this paper, we propose a deep learning method for sidewalk recognition. We trained two Deep Networks algorithms Yolo and YOLACT and compare the performance for sidewalk recognition. The trained Deep Neural Networks are implemented to control the walking assistive robot developed in our laboratory navigate in outdoor environments. The real robot implementation of trained Deep Networks shows a good performance.
Semi-Tensor Product based Multi-modal Fusion Method for Emotion Recognition

Emotion recognition has been an important research topic in the field of human-computer interaction. Multi-modal emotion recognition makes full use of the complementarity of different modalities, which has greater advantages than single-modal emotion recognition. Traditional methods have low prediction performance and limited dimension of tensor fusion caused by inadequate multi-modal information fusion. In this paper, we proposed the semi-tensor product and attention based low-rank multi-modal fusion network (STALMF) for emotion recognition. We first use the semi-tensor product to effectively combine acoustic and language features. The self-attention module is then used for modeling the temporal dependencies of the fused features. Finally, the low-rank multi-modal fusion module is adopted to adequately fuse the information between the fused feature and the individual feature. We conducted our proposed method on the IEMOCAP dataset. The proposed method achieves an averaged F1 score of 82.4% and accuracy of 83.0%, outperforming the comparative methods. Experimental results show that the proposed method can effectively fuse multi-modal information by introducing the semi-tensor product, self-attention mechanism and low-rank multi-modal fusion module.
A Rotor Target Recognition Method Based on Transfer Learning

Aiming to overcome the limitations of traditional manual search for identifiable annotation features that require professional domain knowledge and difficult to extract high-quality features, the VGG16 model-based transfer learning method is used to automatically extract and identify rotor-type targets in this paper. Firstly, based on the radar echo model of the rotor targets, the echo signals of five typical single-rotor or dual-rotor targets are obtained, and then five typical rotor targets data sets, that is AH-64, K-50, K-MAX, V-22 and WZ-9, are established. Finally, the precise identification of the rotor targets is achieved by fine-tuning the VGG16 model. The simulation results validate that the proposed method can improve the recognition rate of rotor target to nearly 99.76%.
Estimation of Hip, Knee, and Ankle Joint Moment Using a Single IMU Sensor on Foot Via Deep Learning

Analyzing human joint moments is an essential step for evaluating walking functions. However, collecting joint moments is mostly limited to the lab environment because it requires force plates to measure overground reaction forces, motion capture cameras to collect joint kinematics, and computational modeling software to calculate joint moments. Although this method allows accurate measurement of joint moments, estimating joint moments outside the lab in daily living, especially in various walking conditions such as ramp and stairs, is challenging due to the requirement of multiple numbers of wearable sensors and expensive insole pressure sensors. A large number of sensors are not only cumbersome for installation and calibration but also limit natural movements. There is a need for an accurate joint moment estimation method using affordable and minimum number of sensors. This paper proposes a novel machine learning algorithm that can estimate the hip, knee, and ankle joint moments in the sagittal plane using an IMU sensor on the foot in treadmill, level-ground, stairs, and ramp walking conditions. The proposed novel DL-Kinetics-FM-Net consists of an end-to-end trained model, a fusion module (FM) to improve joint moments estimation, and a novel technique of integrating two loss functions more eﬃciently than the conventional loss design. Through comprehensive evaluation, we have demonstrated the effectiveness of different proposed components of our model. Specifically, DL-Kinetics-FM-Net results in a decreasing NRMSE by 7.10 − 23.16% compared to the state-of-the-art deep learning algorithm for joint moment estimation. This is the first study that estimated hip, knee, and ankle joint moment in multiple walking condition using a single IMU sensor on foot via deep learning.
Detecting Flooding, Impersonation and Injection Attacks on AWID Dataset using ML based Methods

The widespread use of Wi-Fi networks has also made them a preferred target to a wide range of security assaults. Assailants are employing more advanced techniques to launch attacks, which is changing the nature of the attacks. Using the publicly accessible Aegean Wi-Fi Intrusion Dataset, the authors describe a machine learning (ML) based Wireless Intrusion Detection System (WIDS) for identifying flooding, impersonation, and injection attacks in Wi-Fi networks (AWID). The benefit of ML-based IDS is that they can decipher complicated patterns from the data, allowing them to distinguish between patterns of legitimate traffic and malicious traffic. On the AWID dataset, the authors contrast and compare the results of Logistic Regression (LR), AdaBoost, Naive Bayes (NB), Long Short-Term Memory (LSTM), Decision Tree (DT), and Random Forest (RF). The authors have used data preparation techniques on the AWID dataset’s null values. The trials showed that RF and DT outperformed other ML approaches for the detection of flooding, impersonation, and injection attacks in terms of accuracy, precision, recall, and F-measure. Our proposed methods outperform the others by a wide margin, as demonstrated by a comparison with contemporary methods that have been employed in the literature.
Multi-functional properties of CaCu3Ti4O12 thin films

In this work, electric transport properties of CaCu3Ti4O12 (CCTO) thin films were investigated for resistive switching, rectifying and gas sensor applications. Single phase CCTO thin films were produced by polymeric precursor method (PPM) on different substrates and their electrical properties were studied. Films produced on LNO/Si substrates have symmetrical non-ohmic current-voltage characteristics, while films deposited on Pt/Si substrates have a highly asymmetrical non-ohmic behavior which is related to a metal-semiconductor junction formed at the CCTO/Pt interface. In addition, results confirm that CCTO has a resistive switching response which is enhanced by Schottky contacts. Sensor response tests revealed that CCTO films are sensitive to oxygen gas and exhibit n-type conductivity. These results demonstrate the versatility of CCTO thin film prepared by the PPM method for gas atmosphere or bias dependent resistance applications.
An Agile Model-Based Test Strategy for Assured PNT Implementation

This paper presents an agile Model-Based Testing (aMBT) strategy to guide the rapid acquisition and implemen-tation of modernized Positioning, Navigation, and Timing (PNT) capabilities. Next-generation PNT services need to be secure and assured to support of Multi-Domain Operations (MDO). With aMBT the systems that use PNT can become more responsive to mission requirements. Positioning, Navigation, and Timing (PNT) services are deliv-ered by System of Systems (SoSs) that support Multi-Domain Operations. Because, PNT services span many constituent systems and components, a holistic strategy is needed to select technology options to support end-to-end capabilities by integrating system resources. To implement Assured PNT services, a strategy is needed to determine how to ensure that PNT data is available during military operations.
A Streamlined Start-Up Procedure for Grid-Connected Photovoltaic System with DC Power Optimizers

In traditional grid-tied photovoltaic (PV) systems, the power output drops significantly when partial shadowing occurs between different PV modules in a string, as the PV string current is limited to the value delivered by the weakest module. This issue is mitigated by coupling a dc-dc converters (micro-converter) to every PV module, each being operated at the maximum power point (MPP). In this scenario, without communication links between the distributed micro-converter and the grid-tied inverter, a system start-up procedure must be carefully designed to avoid over-voltages occurring at the micro-converters/inverter interface, eventually leading to system breakdown or failure. In this paper, the control algorithm of each converter is enhanced to provide a seamless start-up operation, so that PV units can safely start transferring power to the inverter and the grid. Improving from previous works, the proposed control technique is simple and removes the need for current sensors at the output of each micro-converter and at the inverter dc-link, providing an economical advantage. Simulation results demonstrate the successful system start-up behavior, whilst confirming the benefits of the proposed control technique.
Microsoft Malware Prediction Using LightGBM Model

The development of Windows malware detection technology and the evolution of malware are complementary and inseparable. Traditional malware detection methods mainly include signature-based detection, heuristic-based detection, and dynamic behaviour-based detection. Thanks to the development of computer hardware and artificial intelligence technology, machine learning and deep learning have achieved many remarkable research results in many fields, and researchers in network security are also paying more and more attention to machine learning. In our paper, we do feature engineering and use LightGBM as our model to process data. To evaluate our method’s performance, we use Auc-Roc as the metric. The higher the Auc-Roc Score and accuracy are, the better performance the model will gain. LightGBM model owns the highest Auc-Roc score 0.684, which is 0.046 and 0.007 higher than Catboost and Xgboost respectively.
Ship Recognition Algorithm Based on ResNet in SAR Images

In the application fields of ocean target recognition in remote sensing images, the target classification of the marine ships based on synthetic aperture radar (SAR) figures remains a significant challenge. The traditional ship target recognition algorithms rely on manually selected features, and these features need to be designed on many experimental bases and professional domain knowledge, which leads to the poor robustness of the algorithm and poor recognition results. In this paper, in order to solve the problem of ship recognition in the SAR image without using manually selected features, a method based on the ResNet is proposed. First, a data augmentation module has been used to expand the experimental dataset. Then, the ResNet is used to recognize the ship in the SAR figures. Ultimately, the experiments based on the ship SAR dataset are carried out, and the suggested recognition method is verified to be of great effectiveness and applicability.
Convolutional Neural Networks for MRI-Based Brain Tumor Classification

Recently, the rate at which people die from cancer disease is very alarming. The major reason is because the cancer cells were not detected at the early stages. It may be difficult for medical professionals to evaluate the results of medical imaging techniques such as magnetic resonance imaging (MRI) because cancer cells may not be adequately represented for detection. This is why deep learning techniques are needed for earlier detection of cancer cells so as to reduce the death rate from such.In this paper, we have applied the convolutional neural network on some MRI image dataset downloaded from Kaggle. The model was trained to be able to detect tumors in brain MRI images. The performance of the model was evaluated and it achieved 97.8% score for the accuracy, 98.5% specificity, 96.2% recall, 98.5% F1-score and 97.3% precision. A graph was drawn to compare the training loss to the validation loss, as well as the training accuracy to the validation accuracy.
Advanced Deep learning-based Controller for Robust EV Speed Management

The significant increase in electric vehicle demand (EV) has highlighted the importance of environment-conscious and erudite architecture for speed control methods. The speed controller is an essential component of an EV for addressing issues such as managing battery energy and adjusting to driving conditions. Because the function of an EV system is time-variant due to parametric uncertainties and road conditions, regulating such mechanisms is a complicated task that traditional predetermined monitoring techniques cannot successfully achieve. To discuss the control issues associated with the EV, in this study advanced Deep Learning (DL) controllers are used to robustly manage the speed of the EVs. In this study, a threshold speed is given to two different controllers as Neural Network (NN) controller and the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller and the performance of the controller is analyzed. For this process, a mathematical model of a complete EV is designed and the speed control is done in that particular model. Further, in order to determine the most efficient controller the error metrics such as ISE (Integral Square Error), IAE (Integral Absolute Error), and ITAE (Integral Time Absolute Error) values are calculated. Comparing the performance values and the error values of both the DL-based controllers, it is found that the ANFIS controller exhibits efficient performance and less error value compared to the NN controller.
Machine Learning Approaches for Electronic Design Automation in IC Design Flow

Due to the vast amount of data collected and the very high level of complexity in VLSI design and manufacturing, the implementation using machine learning can be used in physical design has increased significantly. ML can be used to increase the abstraction level that is obtained from complex simulations based on physics models and provide results that represent a significant level of quality. Computer science techniques such as pattern matching and machine learning can reduce the design time of VLSI circuits by working with large datasets.
A novel campus fire monitoring system based on YOLOv5

To protect lives and equipment, a robust, reliable, and expandable fire monitoring system is critical to detect and react in a campus fire situation. Thus, this paper proposes a YOLOv5-based campus fire monitoring system directly deployable on existing terminals, computers, and other IoT terminals. The proposed campus fire monitoring system effectively monitoring fires in various scenarios on campus all day long. Our deep learning model has been trained using fire images and fire scene videos. The experimental evaluations on different end devices demonstrate that our system achieves an appealing performance in campus fire monitoring.
Privacy Preserving Task Allocation with Multi-objectives in Edge Computing Enhanced Mobile Crowdsensing

Mobile crowdsensing (MCS) uses participants' computing resources to collect and analyze data and it has been applied in several areas to bring the convenience to people's lives. In MCS, the minimization of travel distance with location privacy is a common objective but should not be the only one practically. Different from the single objective of travel distance minimization, in this paper we formulate a multi-objective optimization model based on bit flipping mechanism, i.e., travel distance minimization and sensing quality score maximization, which is more suitable for a practical scenario. In order to solve the large-scale optimization problem, a Multi-Objective Simulated Annealing approach (MOSA) is utilized to derive a Pareto solution for decision makers. Extensive simulation results illustrate the feasibility and effectiveness of the proposed scheme.
Extendable B-tree on Multi-channel Nonvolatile Memory Devices

B-tree is used to be a popular index structure in a variety of application scenarios such as file systems and databases. In a B-tree, each node is allocated to a fixed number of sectors of storage space, and once a node exhausts its allocated space, it overflows and has to be split. When this happens, the data items in the overflown node have to be migrated to the newly split node, which might incur severe performance overheads. The nodes of the B-tree are limited by the capacity upper bound to guarantee the worst-case time to search for a key in the node. However, nonvolatile memories (NVMs) are relatively slower on write operations, which makes the unsorted B-tree a preferred choice over the default B-tree. Observing the different performances and searching a key in a sorted node and unsorted node, we present how to adaptively extend the leaf nodes according to the key distributions to optimize the holistic latency to search a key in a B-tree. The main objective is to maximally postpone the timing of node overflows and improve the overall performance of the B-tree. The proposal in this study is verified through analytical and experimental studies, as the results are promising.
Sampling Rate Impact on Heart Rate Variability

We set a research question to find the impact of the sampling rate in the electrocardiograms on the calculation of the heart rate variability. Our evaluation method analyzes the heart rate variability on the MITDB electrocardiogram benchmark database originally sampled to 360 Hz. Then the electrocardiogram records are resampled to 125 Hz, and a new set of heart rate variability parameters are calculated and compared to those calculated on 360 Hz. The analysis performed on time-domain heart rate variability parameters showed that the impact of sampling frequency was large for NN50 and pNN50 parameters (up to a max value of 50%, and mean of 10%), and smaller for SDNN and RMSSD (up to a max value of 10%, and an average of 2%). In conclusion, heart rate variability needs a higher sampling frequency to reveal more precise results.
Poster: Corrective Real-Time Feedback for Smartwatch Devices using Quaternion Manipulation

This poster presents the preliminary work of corrective real-time feedback for free-weight exercises. We position the proposed system as an app that can run on any smartwatch. The application’s primary focus is to alert the user when their movement deviates from established guidelines. This preliminary study explores the feasibility of manipulating wrist motion with unit-norm quaternions to identify improper form and associated challenges in detecting repetitions. We posit that this technology has the potential to provide injury-preventing feedback.