Miracle-dz/UniFAR-FTUs huggingface dataset:
Metis: An Integrated Morphing Engine CPU to Protect Against Side Channel Attacks

Power consumption and electromagnetic emissions analyses are well established attack avenues for secret values extraction in a large range of embedded devices. Countermeasures against these attacks are approached at different levels, from modified logic styles, to changes in the software implementations. In this work, we propose a microarchitectural modification to a compact RISC-V SoC, the OpenTitan open source silicon root of trust, providing a <italic>code morphing</italic> countermeasure against power and electromagnetic emissions side channel attacks. Our approach allows the countermeasure to be applied transparently, without the need for any software modification to the cryptographic primitive running on OpenTitan. Our microarchitecture integration of a morphing engine also allows us to provide transparent protection to memory operations. We validate our approach through measurements on an actual FPGA prototype on a Xilinx Artix-7. Our integrated morphing engine increases the FPGA resource consumption by less than 8%, plus the resources required by an RNG of choice, with respect to the original OpenTitan SoC. Our design shows a side channel attack resistance improvement of at least <inline-formula> <tex-math notation="LaTeX">$250\times $ </tex-math></inline-formula> in the Measurements-To-Disclose metric with respect to the unprotected design. We benchmark the performance of our proposed architecture on all the ISO/IEC standard symmetric block ciphers, including, among the other AES, reducing the execution time overhead by <inline-formula> <tex-math notation="LaTeX">$21\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$141\times $ </tex-math></inline-formula> with respect to a continuously morphing software solution.
Gaussian Process Regression for Small-Signal Modelling of GaN HEMTs

This paper explores and develops a biasdependent small-signal model for Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) using a non-parametric probability based Gaussian Process Regression (GPR) approach. The characterization data for HEMT is available in S-parameters. It is observed that the determination of optimal hyper-parameters for each S-parameter is crucial in this technique. Therefore, the developed modeling technique employs 10-fold cross-validation loss model to find the optimal parameters. The model incorporates Bayesian optimization alongside with cross-validation error for the best outcome. As a first step, the model is developed using two-third of the data (training set) and subsequently one-third of the remaining data (testing set) is used to capture the predictive ability of the model. The model is tested for both training and testing sets in terms of the mean square error and fitting curves of measured and simulated outputs. Excellent performance is achieved for the entire frequency range. Lastly, the model is also compared with other leading models based on Artificial Neural Network and Support Vector Regression to demonstrate the effectiveness of the proposed GPR based model.
An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis

The analysis of imaging data derived from micro-Fourier transform infrared (μFTIR) microscopy is a powerful tool allowing the analysis of microplastics enriched on membrane filters. In this study we present an automated approach to reduce the time demand currently needed for data analyses. We developed a novel analysis pipeline, based on the OPUS© Software by Bruker, followed by image analysis with Python and Simple ITK image processing modules. By using this newly developed pipeline it was possible to analyse datasets from focal plane array (FPA) μFTIR mapping of samples containing up to 1.8 million single spectra. All spectra were compared against a database of different synthetic and natural polymers by various routines followed by benchmark tests with focus on accuracy and quality. The spectral correlation was optimized for high quality data generation, which allowed image analysis. Based on these results an image analysis approach was developed, providing information on particle numbers and sizes for each polymer detected. It was possible to collect all data with relative ease even for complex sample matrices. This approach significantly decreases the time demand for the interpretation of complex FTIR-imaging data and significantly increases the data quality.
Multilingual Text Classification from Twitter during Emergencies

Social media such as Twitter are a valuable source of information due to their diffusion among citizens and to their speed in sharing data worldwide. However, it is challenging to automatically extract information from such data, given the huge amount of useless content. We propose a multilingual tool that automatically categorizes tweets according to their information content. To achieve real-time classification while supporting any language, we apply a deep learning classifier, using multilingual word embeddings. This allows our solution to be trained on one language and to apply it to any other language via zero-shot inference achieving acceptable performance loss.
Double U-Nets for Image Segmentation by Integrating the Region and Boundary Information

The existing CNN-based segmentation methods use the object regions alone as the labels to train their networks, and the potentially useful boundaries annotated by radiologists are not used directly during the training. Thus, we proposed a framework of double U-Nets to integrate object regions and boundaries for more accurate segmentation. The proposed network consisted of a down-sampling path followed by two symmetric up-sampling paths. The down-sampling path learned the low-level features of regions and boundaries, and two up-sampling paths learned the high-level features of regions and boundaries, respectively. The outputs from the down-sampling path were concatenated with the corresponding ones from two up-sampling paths by skip connections. The outputs of double U-Nets were the predicted probability images of object regions and boundaries, and they were integrated to calculate the dice loss with the corresponding labels. The proposed double U-Nets were evaluated on two datasets: 247 radiographs for the segmentation of lungs, hearts, and clavicles, and 284 radiographs for the segmentation of pelvises. Compared with the baseline U-Net, our double U-Nets improved the mean dices and reduced the 90% Hausdorff distances for the “difficult” objects (lower lungs, clavicles, and pelvises), and the integration of “difficult” object regions and boundaries can improve the segmentation results compared with the use of object regions alone. However, for the “easy” objects (entire lungs and hearts) or “very difficult” objects (pelvises in lateral and implanted images), the integration did not improve the segmentation performance.
Empirical Analysis for Crime Prediction and Forecasting Using Machine Learning and Deep Learning Techniques

Crime and violation are the threat to justice and meant to be controlled. Accurate crime prediction and future forecasting trends can assist to enhance metropolitan safety computationally. The limited ability of humans to process complex information from big data hinders the early and accurate prediction and forecasting of crime. The accurate estimation of the crime rate, types and hot spots from past patterns creates many computational challenges and opportunities. Despite considerable research efforts, yet there is a need to have a better predictive algorithm, which direct police patrols toward criminal activities. Previous studies are lacking to achieve crime forecasting and prediction accuracy based on learning models. Therefore, this study applied different machine learning algorithms, namely, the logistic regression, support vector machine (SVM), Naïve Bayes, k-nearest neighbors (KNN), decision tree, multilayer perceptron (MLP), random forest, and eXtreme Gradient Boosting (XGBoost), and time series analysis by long-short term memory (LSTM) and autoregressive integrated moving average (ARIMA) model to better fit the crime data. The performance of LSTM for time series analysis was reasonably adequate in order of magnitude of root mean square error (RMSE) and mean absolute error (MAE), on both data sets. Exploratory data analysis predicts more than 35 crime types and suggests a yearly decline in Chicago crime rate, and a slight increase in Los Angeles crime rate; with fewer crimes occurred in February as compared to other months. The overall crime rate in Chicago will continue to increase moderately in the future, with a probable decline in future years. The Los Angeles crime rate and crimes sharply declined, as suggested by the ARIMA model. Moreover, crime forecasting results were further identified in the main regions for both cities. Overall, these results provide early identification of crime, hot spots with higher crime rate, and future trends with improved predictive accuracy than with other methods and are useful for directing police practice and strategies.
Dynamic Modeling and Feedback Linearization Control of a 3-D Overhead Gantry Crane System

The overhead crane systems are important heavy-duty machines that are widely used, especially in construction and industry sectors to transport and lift heavy loads. This paper introduces the mathematical modeling of a 3-D overhead crane. The Euler-Lagrange equation is utilized to extract the dynamic equations of the crane system. The obtained nonlinear crane model has been linearized by applying the feedback linearization technique. Then, a PID controller has been developed and implemented to the obtained linearized crane model. The parameters of the designed PID controller have been tuned by implementing the Differential Evolution (DE) algorithm which significantly improved the performance of the PID controller. The simulation results have been performed using MATLAB/SIMULINK platform. The obtained simulation results clearly showed the impact of applying the Differential Evolution (DE) technique to tune the PID controller gains which provided better performance compared to the manually tuned PID controller gains.
Comparison of two types of bioreactors for synthesis of bacterial nanocellulose tubes as potential medical prostheses including artificial blood vessels

BACKGROUND Bacterial nanocellulose (BNC) has a promising prospect in applications of artificial blood vessels, esophagus and urethra. Several bioreactors have been utilized for synthesis of the BNC tubes, however little knowledge is known about differences in biosynthesis processes and tube properties. The current work for the first time compared two types of bioreactors, double-silicone-tube (D-BNC) and single-silicone-tube (S-BNC) bioreactors, to evaluate differences in preparation and properties of BNC tubes by using fructose or glucose of 50 g/L and 100 g/L, respectively. RESULTS Two bioreactors showed different biosynthesis processes in BNC tubes production. D-BNC bioreactor provided higher yields and better mechanical performance of tubes because of more oxygen supply. Higher fructose concentration resulted in more production of D-BNC, but sugar types and concentrations played little effects on S-BNC yields. Fructose granted stronger mechanical properties, but smaller water permeability. Both types of tubes prepared with 100 g/L fructose were composed of porous nano-fibrillar network but showed different cross-sectional morphologies and fiber distributions. CONCLUSION This study revealed the biosynthesis processes of BNC tubes in two bioreactors and their correlation with properties. The results provide significant knowledge to regulate BNC tubes with particular structures and properties for biomedical prostheses including artificial blood vessels.
Multistep Transitions From Microstrip and GCPW Lines to SIW in 5G 26 GHz Band

Substrate integrated waveguides (SIW) are a topic of interest for many researchers, due to their power handling capabilities and compatibility with planar processing techniques. These advantages make this technology an attractive candidate for fifth generation of cellular networks applications (5G). Thus, transitions from more common planar technologies to SIW have been designed to obtain a good performance. Several techniques have been used in these transitions, from the use of via holes as probes to the inclusion of lumped elements. We propose the use of cascaded linear tapers as a multistep transition in order to obtain a better performance, optimizing the designs for the 5G $n258$ band. As result, several transition configurations are optimized, measured and compared to simulations. The design process is performed using an heuristic approach, by just increasing the number of iterations. The optimization of sixteen transition topologies (four different configurations, with four different numbers of steps), is carried out by electromagnetic simulation in CST Microwave Studio. A comparison between the response of the different designs, and the number of iterations used to obtain them, is also presented. Simulations with an in-house full-wave solver are performed to validate the transitions. All the transitions have been manufactured in a low-cost single-layer printed circuit board technology on Rogers 4003C. A microstrip-to-SIW 4-step configuration, tested as a back-to-back prototype, exhibits an insertion loss between 2.0 dB and 2.2 dB and a return loss better than 20 dB dB, from 24.25 GHz to 26.5 GHz, including the effect of the end-launch connectors. Another GCPW-to-SIW 3-step configuration, tested also as a back-to-back prototype, experimentally shows from 24 GHz to 26.5 GHz a minimum return loss of 13 dB, and an insertion loss between 2.6 dB and 2.9 dB. The overall performance of the sixteen configurations validates the usefulness of the proposed design process.
Task Parallelism-Aware Deep Neural Network Scheduling on Multiple Hybrid Memory Cube-Based Processing-in-Memory

Processing-in-memory (PIM) comprises computational logic in the memory domain. It is the most promising solution to alleviate the memory bandwidth problem in deep neural network (DNN) processing. The hybrid memory cube (HMC), a 3D stacked memory structure, can efficiently implement the PIM architecture by maximizing the existing legacy hardware. To accelerate DNN inference, multiple HMCs can be connected, and data-independent tasks can be assigned to processing elements (PEs) within each HMC. However, owing to the packet-switched network structure, inter-HMC interconnects exhibit variable and unpredictable latencies depending on the data transmission path and link contention. A well-designed task schedule using context switching can effectively hide communication latency and improve PE utilization. Nevertheless, as the number of HMC increases, the variability of a wide range of inter-HMC communication latencies causes frequent context switching, degrading overall performance. This paper proposes a DNN task scheduling that can effectively utilize task parallelism by reducing the communication latency variance owing to HMC interconnect characteristics. Task partitions are generated to exploit parallelism while providing inter-HMC traffic within the sustainable link bandwidth. Task-to-HMC mapping is performed to hide the average communication latency of intermediate DNN processing results. A task schedule is generated using retiming to accelerate DNN inference while maximizing resource utilization. The effectiveness of the proposed method was verified through simulations using various realistic DNN applications performed on a ZSim x86-64 simulator. The simulations revealed that DNN processing with the proposed scheduling improved the DNN processing speed by reducing the processing time by 18.19% over conventional methods where each HMC operated independently.
Optimized Deep Stacked Long Short-Term Memory Network for Long-Term Load Forecasting

Long-term load forecasting (LTLF) is an essential process for strategical planning of the future needed extension in the power systems of any country. Besides, deep learning has become the heart of the machine learning paradigm, which is wildly used nowadays in many fields, and it also has become the current revolution in Artificial Intelligence (AI). In this paper, an optimized deep learning model based on Stacked Long Short-Term Memory Network (SLSTMN) is proposed. The architecture of the model is optimized to get the best configuration using Genetic Algorithm (GA). In addition, the hyperparameters of the model network are optimized using many deep learning techniques. During the optimization process, hundreds of model configurations are tested. The accuracy of this model is compared with many deep learning models and is compared against the related work in the field of LTLF. The dataset of the South Australia State (SA) power system is used to test the compared models. This data includes maximum daily load, daily maximum temperature, daily minimum temperature, weekday, the month, and holidays for 12 years from 2005 to 2016. SLSTMN achieves excellent accuracy and the lowest error value (almost 1%) when compared with other models on the same dataset and with related work models on different datasets.
Intention Recognition of Elbow Joint based on sEMG Using Adaptive Fuzzy Neural Network

In this paper, the adaptive fuzzy neural network (AFNN) based on the surface electromyography (sEMG) for estimating the elbow joint angle is established and investigated from the perspective of rapidity and accuracy. In addition, back propagation neural network (BPNN) and artificial neural network of radial basis function (RBFNN), as the classical method for data forecasting, have been applied to estimate the elbow joint angle for comparing with AFNN. Ultimately, the experimental simulation and result analysis demonstrate that the rapidity and accuracy of AFNN is superior to BPNN and RBFNN.
Studies on fault influence of Yindong HVDC transmission system in Shandong Power Grid Based on PSCAD

According to the actual parameters, the PSCAD simulation model of Yindong HVDC transmission system is established. This paper uses appropriate electromagnetic transient simulation method to establish the inverter side AC equivalent (Shandong power grid) model, establishes different types of faults in Shandong power grid, including unbalanced fault (grounding fault) and symmetrical fault (three-phase short circuit fault). The influence of Shandong power AC system fault on Yindong HVDC transmission system is studied and analyzed, so as to improve the stable operation of Yindong HVDC transmission system For some guidance.
Numerical analysis of the influence of loading center angle on the determination of rock tensile strength

In the Brazilian split test, under the loading condition of arc loading, there is no strict regulation on the range of the loading center angle of the weakly cemented soft rock sample, which has a significant impact on the results of the sample measurement. According to theoretical analysis, as the loading center angle increases, the tensile strength of the rock shows a decreasing trend. Using FLAC3D numerical analysis software, the arc loading Brazilian splitting numerical simulation test was carried out on different loading center angles, and the influence of loading angle on the measurement of the stress distribution and tensile strength of the disc specimen axis was studied, indicating that appropriately increasing the loading angle can be effective It reduces the stress concentration near the loading point of the Brazilian disc. If the loading angle is too large, it will cause fracturing failure and test failure. According to the Griffith strength criterion, the maximum equivalent stress is located at the center point of the end face of the disc specimen, and the initiation point of the disc split is judged to be the center point of the end face of the disc specimen. As the loading angle increases, the magnitude of the reduction of the equivalent stress at the center point of the end face of the disc also decreases. Finally, through analysis, the reasonable loading angle value for weakly cemented soft rock should between 20 and 25°.
Arabic Sentiment Analysis of Eateries’ Reviews: Qassim region Case study

Social media plays an essential role in daily life. It allows people to express their thoughts and feelings about available products on e-commerce websites, which is often called an opinion or review. The aim is to see the author’s mood or the speaker’s attitude, which can be positive or negative about a product. This expression by the people is called sentiment. Sentiment analysis is a machine learning method in which the machine learns and analyzes some textual data’s sentiment and emotions (for example, reviews about movies or products). In this research, a real-world data set was collected from customers in the Qassim region using a Microsoft Form survey, containing approximately 1,785 Arabic reviews related to the Qassim region, Saudi Arabia. The overall aim is to find people’s opinions regarding different eateries (cafes and restaurants) using sentiment analysis of the customers’ reviews written in the Arabic language. The experiments are run using 10-fold cross-validation. The performance of the algorithms was measured using accuracy, recall, and F-measure. Different supervised machine learning classifiers were applied. Support Vector Machine, Logistic regression, and Random Forest achieved the best results. Furthermore, we analyzed the differences between all results in this study.
GPU-Accelerated Computations for Supersonic Flow Modeling on Hybrid Grids

With its strong floating-point operation capability and high memory bandwidth in data parallelism, the graphics processing unit (GPU) has been widely used in general-purpose computing. GPU-based computations have been extensively applied in the field of computational fluid dynamics (CFD). This paper aims to design an extremely efficient double-precision GPU-accelerated parallel algorithm for supersonic flow computations on hybrid grids. Compute unified device architecture (CUDA) is used as a general-purpose parallel computing platform and programming model to perform parallel computing codes on GPUs. The cell-centered finite volume method based on unstructured grids is used in the spatial discretization of governing equations, whereas the three-stage explicit Runge-Kutta scheme with second-order accuracy is used for temporal discretization. The turbulence is solved by using the $K-\omega$ SST two-equation model. Three test cases are studied to validate the computational accuracy of the proposed algorithm. The numerical results agree well with the experiment data, thereby suggesting that the GPU-accelerated parallel algorithm has good accuracy.