Miracle-dz/UniFAR-FTUs huggingface dataset:
Framework to Analyze Malicious Behaviour in Cloud Environment using Machine Learning Techniques

The era of digitalization adds a lot to indorsing an IT infrastructure to meet business needs. Cloud Infrastructure played an important role by offloading the on-premises workloads to the datacenter which is easily accessible from anywhere through internet and reduce the complexity. It poses a major security concern while storing the data over internet. There are already a lot of security frameworks to protect the cloud environment, notwithstanding, there has been a lot of risk in recent years that has increased security threats and breached data at even higher levels. In this paper, we have proposed a framework to provide the security of cloud environment based on user's Behaviour. It counsels an improved method for detecting normal and malicious Behaviour and prevent the attempt which is coming as abnormal access. We used UNSW-NB15 dataset which contains more than 82,000 samples, 49 features, and 9 types of attacks. We have presented the viable analysis of performance of different machine learning techniques used for prediction of user's Behaviour and comparison of various algorithms i.e. Support Vector Method, Decision Tree, Naïve Bayes, Random Forest, KNN and Logistic regression, which shows the promising results in detecting the malicious behaviors.
Supervised Dimensionality Reduction of Hyperspectral Imagery Via Local and Global Sparse Representation

Despite the successful applications of unsupervised sparse dimensionality reduction (USDR) in pattern recognition, the USDR still suffers from two challenges for hyperspectral images (HSIs), which limit its discriminative performance: first, it cannot be applied for dimensionality reduction using both training samples and testing samples; second, it lacks the ability to integrate the spectral with spatial information for improving the discriminative performance of HSIs. In order to tackle the first challenge, we extend it to a supervised scenario, which can be applied for both training samples and testing samples, namely dimensionality reduction sparse representation (DRSR). Then, we propose a novel method called local and global DRSR (LGDRSR) to integrate the spectral information and spatial information of HSIs to further improve the discriminative performance of HSIs. The proposed LGDRSR computes the distance information between pixels of HSIs including the whole samples and their corresponding locations with a unified metric matrix. Experimental results show the proposed LGDRSR outperforms other state-of-the-art algorithms significantly.
Determination of Non-Proliferative and Proliferative Diabetic Retinopathy through Fundoscopy using Principal Component Analysis

Image processing can be one of numerous ways that can be utilized in recognizing illnesses particularly in the retina of the eye as it isn't effectively available for examination except the eye is to be dilated and can give a clearer image of it with the help of a lens. The characterization of existing models was constrained to determining Non-Proliferative Diabetic Retinopathy (NPDR) and excluding Proliferative Diabetic Retinopathy (PDR). In this research, a diabetic retinopathy determination system was created on Raspberry Pi 3 Model B by performing Pre-processing methods on images and through Principal Component Analysis and Support Vector Machine for classification on the OpenCV software. The system is also able to characterize the retina as normal, NPDR, or PDR. The system was tested and observed for the purpose of obtaining clear images and its accuracy through the Confusion Matrix. Combing Principal Component Analysis together with Support Vector Machine provides an accurate result of 86.67% with some errors due to having some minor similarities in the retina when processed such as camera flash must be mistaken as cotton wool spots, haemorrhages, and micro aneurysms. Providing a larger pool of database will effectively increase the accuracy rate of the system.
Hierarchical Random Forest Formation with Nonlinear Regression Model for Cardiovascular Diseases Prediction

Cardiovascular diseases (CVD) like stroke and heart attack have a higher risk to cause the life of human beings in heart diseases category. The challenge to predict cardiovascular diseases is high risky by using the hospitalized patients Intensive Care Unit (ICU) data. The recent technologies play a vital role in the medical industry to predict various health causes using various algorithms. Machine Learning (ML) technologies are one among them to predict the diseases using quality dataset and data analysis model. In this paper, we propose a model to find the similarity by using Hierarchical Random Forest Formation with Nonlinear Regression Model (HRFFNRM). By using this model, which produces 90.3% accurate prediction in cardiovascular diseases.
Low-Latency Low-Energy Memory-Cube Networks using Dual-Voltage Datapaths

Three-dimensional stack memory that provides both high-bandwidth access and large capacity is a promising technology for next-generation computer systems. While a large number of memory cubes increase the aggregate memory capacity, the communication latency and power consumption would become significant due to its low-radix large-diameter packet network. In this context, we propose a memory-cube network called Diagonal Memory Network (DMN). A diagonal network topology, its floor layout, and its lightweight router are designed for low-latency and low-voltage memory-read communication. Our evaluation results show that a DMN router decreases 31% of the hardware resources than a conventional virtual-channel router. The DMN router reduces 13% and 67% energy consumption to transit a packet along with the original datapath and bypassing datapath, respectively.
Tuning the Sensitivity of a Fiber-Optic Plasmonic Sensor: An Interplay Among Gold Thickness, Tapering Ratio and Surface Roughness

The performance of a multimode fiber optic surface plasmon resonance (FO-SPR) sensor has been studied, both theoretically and experimentally, as a function of three crucial parameters - metal layer thickness, tapering ratio (TR) and fiber surface roughness. A combined approach is adopted to investigate the effect of these three parameters on FO-SPR sensor performance instead of considering them individually. Intriguingly, the three parameters are found to be highly correlated and their combined effect significantly influences the FO-SPR sensitivity. It is observed that optimum gold thickness to attain maximal sensitivity varies for different tapering ratios. Moreover, increasing the tapering ratio does not always increase the sensitivity as claimed in previous studies, but instead depends on the fiber surface roughness generated during chemical etching. The optimized FO-SPR sensor exhibits a high sensitivity of 4714 nm RIU−1 with standard glycerol solution in the refractive index (RI) range of 1.372-1.388. Furthermore, the optimized FO-SPR sensor has been employed for label-free detection of bovine serum albumin (BSA) and parathion pesticide. The system is able to detect BSA concentration as low as 1.5 pM and the parathion pesticide is detected at 0.1 ppb levels.
Tracking problem in robotic teleoperation systems without force and velocity measurements in the presence of time delay

In this paper, a bilateral teleoperation system in the presence of time-varying time-delay is examined. Force feedback increases system performance as well as transparency, which in turn increases user dominance over the system. However, the force sensor is not always available for various reasons like space constraints, cost and weight. Thus to overcome the problem of lack of Force, the state observer algorithm is used to estimate the speed and external force of the master and slave robots. The estimated signals are used in the four-channel architecture controller design and then the analysis of the tracking of the bilateral Teleoperation system is proved. The simulation results indicate the position and force tracking as well as the transparency of the system.
Adaptive Neural Command Filtered Control for Pneumatic Active Suspension With Prescribed Performance and Input Saturation

In this paper, an adaptive neural command filtered backstepping scheme is proposed for the pneumatic active suspension with the vertical displacement constraint of sprung mass and actuator saturation. A quarter car model with a pneumatic spring is first fabricated on the basis of thermodynamic theory to describe the dynamic characteristics. To overcome the lumped unknown nonlinearities and enhance the requirement of modeling precision, the radial basis function neural networks (RBFNNs) are proposed to approximate unknown continuous functions caused by the uncertain body mass and other factors of pneumatic spring. To solve the explosion of complexity problem in the traditional backstepping designs, a proposed command filter control is applied by using the Levant differentiators which approach the derivative of the virtual control signals. Nussbaum gain technique is then incorporated into the controller to avoid the problem of the completely unknown control gain and control directions of a pneumatic actuator. In addition, the prescribed performance function (PPF) is suggested to guarantee that the tracking error of the sprung mass displacement does not violate the constraint boundaries. Based on the command filtered backstepping control with PPF, the Lyapunov theorem is then applied to indicate the system stability analysis. Finally, the comparative simulation examples for the pneumatic suspension are given to verify the effectiveness and reliability of the proposed control.
Two Secure and Efficient Lightweight Data Aggregation Schemes for Smart Grid

It is a challenging issue in smart grid to provide flexible and efficient data aggregation and at the same time maintain data integrity and data privacy. To address this issue, we firstly propose a lightweight $t$ -times homomorphic encryption scheme, which can reduce the computational cost of smart devices further and resist quantum attacks. Based on it, we propose two efficient data aggregation schemes for two application scenarios. The first scheme is suitable for small network, such as home network and small-office network. The second scheme is appropriate for medium-size network, such as a part of smart city. More specifically, we explore how the number of smart devices and the number of home area networks affect the overhead of communication and computation. The experiment results show that the proposed schemes are efficient. Moreover, the proposed schemes not only meet security requirements of integrity, confidentiality, authenticity, resistance to intermediate attack, resistance to replay attack, forward and backward security and resistance to quantum attack, but also reduce the overhead of communication and computation.
Nanomechanical properties evaluation of bioactive glass coatings on titanium alloy substrate

Bioinert metallic implants such as titanium and titanium alloys could be coated with bioactive materials with good adhesion to metal and which could be also bonded to the bone. In order to predict the mechanical behavior of coatings during the implant insertion, mechanical properties of the surface and through the thickness of deposited bioglass coatings are necessary to be studied. We report an investigation of the mechanical characteristics of bioglass coatings using the nanoindentation and nanoscratch techniques. The coating topography with residual imprints was analyzed by in situ imaging method. The dependence of the friction coefficient on load in constant and ramped load tests as well as on the scratch speed is compared. Elastic modulus and hardness results after indentation on the coating surface with higher loads showed more consistent results with less pile up around imprints. The nanohardness and reduced elastic modulus of the coating is in the expected boundaries of theoretical values for glass and alloy with a moderate distribution through the coating thickness. Nanoscratch testing showed that the surface deform plastically with no visible debris or cracks.
Development of a Hybrid Machine Learning Model for Apple (Malus domestica) Health Detection and Disease Classification

Apple is one of the top agricultural products produced each year. Even with apple one of the most cultivated crops, its demand is also increasing. As a result, this crop which is typically grown in temperate climate regions is now being cultivated in tropical climate regions. A factor affecting the production of apples each year is the pest and disease infestation. This study aims to create a machine learning model that can detect and classify the three most common 3 types of apple disease: apple scab (Venturia inaequalis), black rot (Botryosphaeria obtusa), and cedar apple rust (Gymnosporangium juniperi-virginianae). Color and texture feature extractions were performed to 400 single-apple leaf images followed by feature selection reducing the number of predictors to R, V, and b*. A comparison among K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) was done to determine which is the best performing model that detects whether an apple leaf is healthy or infested. The GPR model with ARDSquared kernel function was determined to be the best performing model with training accuracy of 88.44%, testing accuracy of 82.50%, and $\text{AUC} =0.9256$. On the other hand, Decision Tree and Support Vector Machine (SVM) were compared to get the better performing model for classification disease classification. The quadratic SVM model was determined to be the better performing model obtaining 77.1% training accuracy, 83.3% testing accuracy, and $\text{AUC}=0.86$.
An Early Detection of Asthma Using BOMLA Detector

Asthma is a chronic and airway-induced disease, causing the incidence of bronchus inflammation, breathlessness, wheezing, is drastically becoming life-threatening. Even in the worst cases, it may destroy the quality to lead. Therefore, early detection of asthma is urgently needed, and machine learning can help identify asthma accurately. In this paper, a novel machine learning framework, namely BOMLA (Bayesian Optimisation-based Machine Learning framework for Asthma) detector has been proposed to detect asthma. Ten classifiers have been utilized in the BOMLA detector, where Support Vector Classifier (SVC), Random Forest (RF), Gradient Boosting Classifier (GBC), eXtreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) are state-of-the-art classifiers. In contrast, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QLDA), Naive Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN) are conventional popular classifiers. ADASYN algorithm has also been employed in the BOMLA detector to eradicate the issues created due to the imbalanced dataset. It has even been attempted to delineate how the ADASYN algorithm affects the classification performance. The highest accuracy (ACC) and Matthews’s correlation coefficient (MCC) for an Asthma dataset provide 94.35% and 88.97%, respectively, using BOMLA detector when SVC is adapted, and it has been increased to 96.52% and 93.04%, respectively, when ensemble technique is adapted. The one-way analysis of variance (ANOVA) has also been performed in the 10-fold cross-validation to measure the statistical significance. A decision support system has been built as a potential application of the proposed system to visualize the probable outcome of the patient. Finally, it is expected that the BOMLA detector will help patients in their early diagnosis of asthma.
Characterization of Seamless CdTe Photon Counting X-Ray Detector

Spectrally selective X-ray imaging provides improved material and tissue discrimination in comparison with the state-of-the-art dual energy technologies that are commonly used in medical, industrial, and security applications. Cadmium telluride (CdTe)- and cadmium zinc telluride (CdZnTe)-based line scanners and small size two-dimensional X-ray sensors are emerging to the market, but the need for large-scale panels is axiomatic. In this study, a seamless CdTe tile was developed that enables the implementation of large-sized, energy selective X-ray detector panels. The developed tile consists of a $64\times64$ pixel array (with $150~\mu \text{m}$ pitch) with a necessary substrate, ASIC, and CdTe crystal. The performance of the constructed seamless tile was characterized by focusing on spectral resolution and stability. In addition, a simple pixel trimming method that automates the equalization of each energy selective pixel was developed and analyzed. The obtained results suggest that the proposed concept of seamless (tileable) detector structures is a feasible approach to scale up panel sizes. The seamless tile shows comparable spectral resolution and stability performance with commercial CdTe sensors. The effect of tile to tile variation, the realization of a large-scale panel, as well as the charge sharing performance were left out of the scope and are to be studied in the next phase.
Estimation of spatial distribution and symmetry of textile materials using numerical classification

CDAPT, ISSN 2701-939X Peer reviewed article 2020, Vol. 1, No. 2, pp. 180-185 DOI 10.25367/cdatp.2020. 1.p180-185 Received: 18 December 2020 Accepted: 24 December 2020 Available online: 25 December 2020 Numerical classification of textile materials, here aramid, viscose, and PAN/WV, is proposed using lacunarity analysis of monochromatic digital representations of optical microscopic images. The method is sensitive to the spatial distribution of fibers and, equivalently, to the empty spaces between them. This means that lacunarity is able to quantitatively express a given level of spatial in-plane symmetries of single-face fabrics.
Profiling Cell Surface Sialylation and Desialylation Dynamics of Immune Cells

Sialic acids (SAs) are a diverse family of naturally occurring 2-keto-3-deoxynononic acids that are involved in a wide range of biological processes, including early fetal development, cellular recognition, and utilization by microbes. While it is clear that cell surface SAs are highly involved in the immune system, the sialylation status of individual immune cells and functions are still unknown. In this study, I combined the newly developed LC-MS/MS methods with flow cytometry and confocal microscopy to systematically study the sialylation and desialylation dynamics of macrophages at different conditions. First, I developed an accurate LC-MS/MS method to quantify free SA in human plasma with isotope-labeled standard calibration and 3,4-diaminotoluene derivatization. This method is capable to distinguish SA analogus in complex biological samples, which paves the path for dynamic SAs research. Menwhile, another LC-MS/MS method with direct SAs quantification was developed for high throughtput analysis. This method does not require complicated sample preparation and can quantify SA at 2 ng/mL. Next, I performed globally profiling of sialylation status of Raw 264.7 macrophages by flow cytometry, confocal microscopy, and LC-MS/MS. Both flow cytometry and confocal microscopy showed the predominat of α-2,3 linked SAs on the cell surface, and
Impedance Transparency and Performance Metrics of HBT-Based N-Path Mixers for mmWave Applications

MOS N-path mixer-first receivers are capable of providing instantly-reconfigurable RF impedance and bandwidth while achieving moderate noise figure (NF) and high linearity, but their frequency tuning range is limited by both LO (local oscillator) generation and the RF port input capacitance. State-of-the-art mm-wave MOS N-path receivers often compromise performance to cover the mm-wave range, but this paper presents the theory and design considerations for a new topology of N-path mixer which makes use of high fT HBTs and breaks this trade-off between performance and tuning range. Here, we borrow from a previously derived LTI model for MOS-based N-path mixers to derive an analogous model for the HBT-based counterpart which provides a meaningful comparison between the performance of the two topologies. We show that the HBT-based implementation is capable of operation beyond the frequency limits of MOS-based implementations while maintaining comparable NF and linearity without consuming exorbitant power. Measurements done on a proof-of-concept chip in GlobalFoundries BiCMOS8HP are consistent with our models and simulations. By organizing process parameters and user-selected design variables into dimensionless ratios, we provide expressions for key performance metrics which enable the designer to make informed decisions about trade-offs and optimizations for both LO generation and the mixer core.
Power Extraction From an Ultra-Low Input Voltage Source in a Battery-less Thermoelectric Harvester

This paper presents a thermoelectic energy harvesting technique by sensing the temperature gradient between the human body and the ambient condition. With a temperature difference of 2-3 °C, the voltage available at the output of the thermoelectric harvester is merely 85 mV. To make it usable to CMOS circuits, this voltage needs to be boosted using a DC-DC boost converter. The key objective of this paper is to replace a motion-activated off-chip mechanical switch that is conventionally used in the boost converter with an integrated NMOS switch for starting-up the voltage conversion process. During start-up operation, the fully integrated control circuit produces a suitable level-converted ON-OFF signal with a swing of nearly 0–300 mV for the NMOS switch from the input voltage of 85 mV. This is achieved by using a start-up assisted ring oscillator topology that produces oscillations at a very low voltage. Subsequently, a series of clock booster stages, switch capacitor-based voltage booster stages and level shifter circuit are used to increase the voltage swing of the gate-drive signal for periodic switching of the NMOS switch. The proposed solution implemented in 180 nm CMOS process is capable of boosting the very low harvested voltage to nearly 1 V without requiring any external power supply such as battery or an off-chip mechanical switch.
Promoting the Teaching Reform of New Economics by Experimental Economics

In recent years, with the rapid development of science and technology and the innovation of educational and teaching concepts, simply opening economic theory courses will result in a tedious teaching mode and insufficient teacher-student interaction. As experimental economics has made more and more important achievements in academic research, its role in teaching has also begun to receive the attention of university teachers. Through case analysis, this paper explores how to integrate the advanced experimental economic methods into the traditional Microeconomics and Game Theory teaching in order to promote economic teaching reform and meet the new needs of teachers and students in the new era for economic professional education and learning. It is found that the innovative classroom combining experimental economics enhances the interaction between teachers and students, brings more subject inspiration, and avoids the dilemma of "knowledge uselessness" caused by abstract theoretical knowledge divorcing from practice compared with the traditional indoctrination classroom. Therefore, the conclusion of this paper provides a feasible plan for promoting the reform of new economics teaching in the new era.
Towards a virtual companion system to give support during confinement

We present the analysis and design of a Virtual Companion System (VCS) that helps college level students to manage some psychological problems due to confinement. VCS takes advantage of physiological techniques such as Meditation, Cognitive Behavior Therapy Mindfulness or Poetry therapy, etc., which have scientific evidence about their effectiveness to improve the well-being of persons.