Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring iGPU Memory Interference Response to L2 Cache Locking

The demand of parallel execution in real-time embedded applications has motivated the integration of GPUs as processing accelerators on SoCs (System-on-Chip) embedded architectures, often leading to CPU-iGPU architectures. In the safety-critical domain, it is paramount to ensure that the execution deadlines of critical tasks are not exceeded. To ease the analysis of this kind of tasks, we can make their worst-case execution time more predictable. One way to achieve this is by mitigating or controlling the memory interference generated by the concurrent execution of tasks through the application of a series of techniques (e.g., cache partitioning, bank partitioning, cache locking, bandwidth regulation). Originally, these were applied to CPUs, and more recently, to GPUs as well. In this work, we focus on the hardware-based L2 cache locking on iGPUs as memory interference mitigation mechanism. We are interested in evaluating its capacity for reducing the worst-case and the average-case execution time in different scenarios. Our measurement-based analysis has been carried out on the NVIDIA’s Jetson AGX Orin 64 GB MPSoC, making use of four representative benchmarks (data resetting, 2D convolution, 3D convolution and matrix upsampling).
Reynolds Stress Anisotropy in Self-Preserving Turbulent Shear Flows

It is theoretically shown that all turbulent shear flows governed by the boundary layer equations that exhibit self-preserving properties contain a layer, in which the Reynolds stress anisotropies are constant. Experimental data of several such flows are analysed, proving the existence of such a layer. The corresponding Reynolds stress anisotropies are inferred, deviating between the different flows. In particular, the shear stress anisotropy differs from the value in boundary layers according to the Bradshaw hypothesis, explaining the ``round-jet/plane-jet anomaly'' as well as the prediction of delayed re-attachment by models that are calibrated to boundary layer conditions. Requirements for the improvement of current RANS models are derived.
RISK FACTORS FOR EARLY DEVELOPMENT OF POLYNEUROPATHY AMONG TYPE II DIABETIC PATIENTS.

Mahfouz HM 1 , Hashem A.H. 2 and Yasser Hamed Mostafa 2 . 1. Hala Mohammed Mahfouz, Professor of internal medicine and endocrinology, Ain-Shams University. 2. Hassan Ahmed Hashem, Yasser Hamed Mostafa: Lecturers of neurology, AlAzhar faculty of medicine, Assiut branch. Manuscript Info Abstract . Manuscript History
Bag of Strategies Set New State-of-the-art for Algae Object Detectors

Deep learning-based detection of marine microalgae in natural waters can meet the need for rapid monitoring, facilitating researchers in marine and environmental sciences, while also paving the way for downstream cellular analysis tasks. We use a new training scheme for marine microalgae detection that consists of two phases: a teacher benchmark model phase and a student model learning phase. Using teacher model supervision to get better student model training results. Through a simple and fast image fusion method, we can obtain more realistic algae-generated images to extend the training set and eventually improve the convergence speed and performance of the model. Based on the algorithms of YOLOv5 and YOLOv6, we use the DHLC backbone network fusion method to fuse features from different levels of C3 modules and BepC3 modules together as the input of the PANet middle layer. We also use the module in BoTNet network to obtain stronger feature extraction capability by introducing self-attention mechanism in the yolo model. Since there are many small targets in marine microalgae images, we also extend the YOLOv6l model to the more powerful YOLOv6l-P6 model, which can get better detection results in the input image size of 1280. In addition, we also use time-test augmentation (TTA), weighted boxes fusion (WBF) and Single-class wighted boxes fusion (SinWBF) techniques to optimize the performance of each class. These strategies greatly improve the model detection performance and robustness under the conditions of small amount of marine microalgae microscopic image data. Finally our solution won the first place on the “Vision Meets Algae” Object Detection Challenge, and got 58.25 MAP.
A Bayesian Graph Neural Network for EEG Classification — A Win-Win on Performance and Interpretability

With the deepening of neuroscience research, data mining of brain signals is becoming an emerging topic. Among various brain signals, electroencephalography (EEG) has attracted more and more attention due to its advantages of non-invasiveness, portability, and low cost. EEG modeling and analysis play a vital role in human healthcare. Although many machine learning algorithms have been successfully applied to data mining of EEG signals, few of them achieve a win-win in classification performance and interpretability. In this paper, we propose a Bayesian graph neural network named BayesEEGNet. Considering an electrical impulse between two nodes in the brain as a Poisson process, the countless electrical impulses generated by the brain in a period are represented as an infinite number of connection probability graphs. After coupling and transforming these probability graphs, we interpret the brain’s electrical activity state as the brain’s perceptual state. Benefiting from the joint optimization of Bayesian modules and deep neural networks, our model shows superior classification performance in sleep stage classification and emotion recognition tasks. Meanwhile, our model is able to learn interpretable functional connectivity relationships between EEG channels without any prior knowledge.
A Rule-based Decision System for Financial Applications

Decision rules have been widely applied in industrial applications such as finance, medicine, and biology, due to the critical requirement of interpretability. In order to make decision rules easier and more widely used in financial scenarios, an automatic intelligent rule system with rule learning and rule management capabilities is needed. However, the rule system for financial applications has distinctive challenges both in algorithms and systems. From the algorithm perspective, due to the characteristics of the financial data and scenarios, the rule learning algorithm faces the class-imbalanced issue, the scalability issue, and the diversity of optimization objectives. From the system perspective, a flexible rule learning and management framework is needed to adapt to fast-changing financial applications with heterogenous data, and engineering optimization is required to ensure the time and space efficiency of rule learning. In this work, we focus on developing a Rule-based Decision System (RDS) to deal with the algorithmic and systematic challenges mentioned above. RDS covers the full life cycle of the decision rules, including the rule learning module, rule management module, and rule deployment module. Moreover, the rule system offers an interactive interface to allow users to integrate the expert experiences into the decision rules and realize the human-in-the-loop. The RDS has been deployed on one of the world’s largest trading and money transfer platforms, serving hundreds of millions of users and transactions.
Abdominal MRI Synthesis using StyleGAN2-ADA

The lack of labelled medical data still poses as one of the biggest issues when creating Deep Learning models in the medical field. Modern data augmentation techniques like the generation of synthetic images have gained a special interest. In recent years there has been a significant improvement in GANs. StyleGAN2 achieves impressive results in the generation of natural images. StyleGAN2-ADA was created to respond to the lack of training data when training an image synthesis model, which is very frequent in the medical field. Some works used styleGAN to generate melanomas, breast cancer histological images, MR and CT images. In this work we apply, for the first time, a styleGAN2-ADA to a small dataset of abdominal MRI with 1.3k images. From the augmentation pipeline created by the authors of styleGAN2-ADA, we removed all augmentations except the geometric transformations and pixel blitting operations. We trained our network for 70 hours. Our generated dataset has a precision score of 59,33 % and a FID score of 18,14. We conclude that the styleGAN2-ADA is a viable solution to generate MRI using a small dataset.
HoneyKube: Designing and Deploying a Microservices-based Web Honeypot

Over the past few years, we have witnessed a radical change in the architectures and infrastructures of web applications. Traditional monolithic systems are nowadays getting replaced by microservices-based architectures, which have become the natural choice for web application development due to portability, scalability, and ease of deployment. At the same time, due to its popularity, this architecture is now the target of specific cyberattacks. In the past, honeypots have been demonstrated to be valuable tools for collecting real-world attack data and understanding the methods that attackers adopt. However, to the best of our knowledge, there are no existing honeypots based on microservices architectures, which introduce new and different characteristics in the infrastructure. In this paper, we propose HoneyKube, a novel honeypot design that employs the microservices architecture for a web application. To address the challenges introduced by the highly dynamic nature of this architecture, we design an effective and scalable monitoring system that builds on top of the well-known Kubernetes orchestrator. We deploy our honeypot and collect approximately 850 GB of network and system data through our experiments. We also evaluate the fingerprintability of HoneyKube using a state-of-the-art reconnaissance tool. We will release our data and source code to facilitate more research in this field.
Hybrid Multiple Access Scheme Employing NOMA and OMA Simultaneously Under Non-uniform User Distribution and Multiple Transmit Antennas

The fifth generation (5G) mobile communication systems provide increased functionality and performance, and can be implemented through orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA). Both methods present advantages and disadvantages under certain conditions. In this study, we analyze the performance of a hybrid multiple access scheme using NOMA and OMA simultaneously in a practical scenario under non-uniform user distribution and multiple transmit antennas through computer simulations to verify its effectiveness. The proposed hybrid multiple access scheme avoids the NOMA-specific performance deterioration caused due to small channel gain differences between users by applying OMA within the same bandwidth. Additionally, we implement resource allocation to account for both the channel gain and desired user traffic volume considering the diversified wireless service for Beyond 5G (B5G). We apply the Thomas cluster process as user distribution and maximum ratio transmission (MRT) as transmit diversity, and elucidate the effects of the non-uniformity of user distribution and an increase in the number of transmit antennas on the performance of the proposed hybrid multiple access scheme.
Domestic Security Clustering Algorithm Based on AP's Capability to Discriminate Location: A survey

Due to its excellent localization precision, worldwide application, and lack of supplementary equipment, a WIFI-based indoor localization method has garnered considerable interest. However, as the interior environment varies dynamically, the environment influences access point (AP) connectivity in various locations to varying degrees. The conventional algorithm for fingerprint localisation cannot account for irregular received signal strength indication (RSSI) in diverse places. This work presents an algorithm for localisation of interior protection depending on the capability of position discriminating access points (SLABLDA), which is resilient in complicated, abrupt interior settings. The AP location diversity models for aptitude assessment is supplied during the offline detection phase of the process in order to identify the prejudice based on locality capacity of AP in a variety of indoor complicated situations. During the online part of the process, a strategy for adjusting the gap between fingerprints using the lowest variation in RSSI is described in order to limit the The atmosphere's temporal dynamics are causing RSSI disturbance. Tests were done have shown that the technique that was proposed is capable of successfully filtering the effects of anomalous RSSI in complex and abrupt interior environments, which results in an increase in the localization accuracy in comparison to the algorithm that is currently used for fingerprint localization.
Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems

In Bayesian inverse problems, the posterior distribution is used to quantify uncertainty about the reconstructed solution. In fully Bayesian approaches in which prior parameters are assigned hyperpriors, Markov chain Monte Carlo algorithms often are used to draw samples from the posterior distribution. However, implementations of such algorithms can be computationally expensive. We present a computationally efficient scheme for sampling high-dimensional Gaussian distributions in ill-posed Bayesian linear inverse problems. Our approach uses Metropolis--Hastings independence sampling with a proposal distribution based on a low-rank approximation of the prior-preconditioned Hessian. We show the dependence of the acceptance rate on the number of eigenvalues retained and discuss conditions under which the acceptance rate is high. We demonstrate our proposed sampler by using it with Metropolis--Hastings-within-Gibbs sampling in numerical experiments in image deblurring, computerized tomography, and NMR relaxometry.
A Novel High-Interaction Honeypot Network for Internet of Vehicles

Along with the evolution of communication technologies, cybersecurity has evolved, and so have its new directions and demands. There is a wide range of tools to detect, analyse, or protect systems from malicious activity. Yet, as new technologies are emerging and maturing, the need for particular domain solutions arises. This paper proposes a methodology for a honeypot network organisation mimicking vital autonomous vehicle sensors inside the Internet of Vehicles (IoV) infrastructure, along with attack propagation patterns analysis based on the logs collected from the honeypots. The discovery of sequential patterns is based on Markov Chain models applied in the honey-farm data. Further, these trained models are applied with graph-based algorithms to discover the interaction patterns between honeypots targeting the discovery of segments that were attacked in series. The intelligence produced from the analysis is used to rank and estimate the relative importance of the honeypots in their framework. The results of our study allowed us to identify common attacks on the IoV system, detect the geolocation of each attacker, and specify the usage of each honeypot node from the attacker’s perspective.
EV charging site day-ahead load prediction in a synthetic environment for RL based grid-informed charging

Ensuring grid health in the face of increasing demand for power is an emerging challenge especially due to transportation electrification. A free market approach to influencing electric vehicle (EV) load through grid-informed hourly dynamic pricing is introduced in this work. The setting of charging price is done by a reinforcement learning (RL) agent that learns the complicated dynamics by interacting with a synthetic environment. This synthetic environment is a combination of distribution feeder simulation, EV charger user behavior dynamics, and EV charging simulation. A key module in this synthetic environment involves obtaining the day-ahead charging profile of EV charging stations based on real-world past data. The day-ahead prediction is also useful in other traditional optimizations related to EV charge scheduling. The proposed approach involves using EV charging data from two different past time horizons - one to determine the shape of the daily profile and the other to determine a scaling value to capture actual energy consumption. Real-world charging data over many years from the ACN charging network has been used to demonstrate the ability to predict the day-ahead profile with only charge session data. Both Python and MATLAB have been used for data cleaning, processing, analysis, and prediction.
Distributed State Estimation for Multi-Area Data Reconciliation

Data reconciliation is an essential tool in data processing in various industries. It helps to improve accuracy of decision-making algorithms by reducing the influence of random errors in measurements. In this paper, we consider large-scale data reconciliation problems in which multiple areas communicate over a network to obtain an optimal solution of the centralized problem. Our proposed approach accounts for the boundaries between different areas avoiding a mismatch and sub-optimality as well as reduces computational and communication complexities. The proposed distributed data reconciliation method is compared to a centralized reference in different scenarios.
Deep Learning Approaches for Cabbage Disease Classification

Cabbage diseases such as black rot, downy mildew, and white rust are frequent and have a negative impact on yield. However, existing research lacks an accurate and rapid detector of cabbage diseases to assure healthy cabbage production. In this research, the transfer learning approach has been employed for many state-of-the-art CNN architectures, such as VGG16, VGG19, mobilnetv2, and InceptionV3, to determine the most optimal solution for this problem. A dataset of around 1500 images from three different classes is employed to train and validate the models. Among the multiple CNN models evaluated, vgg16 produced 95.55% test accuracy, which is far superior to other similar experiments conducted recently.
Analysis of Indoor Localization Techniques

Location-based applications and services have gained growing business interest in recent years. Systems for indoor localization relay on various localization techniques, which can vary significantly when it comes to the technology, accuracy, cost, robustness, etc. Unlikely outdoor localization, the indoor localization requires a higher precision and can be more challenging due to the signal dispersion and obstacle reflections. Currently, there are numerous solutions proposed in this field of research. In this paper, we provide the survey of the existing technologies and techniques for indoor localization systems.
Low Complexity HARQ Algorithm for a Polar Code Decoder

A low complexity Hybrid Automatic Repeat reQuest (HARQ) scheme for a polar code decoder is presented which provides a coding gain at the expense of information throughput at very low SNRs. Unlike complex incremental redundancy HARQ schemes or those which use a CRC to identify a decoding failure, only two nodes within the polar tree are monitored to predict a decoding failure. The performance of this low complexity algorithm is compared with an ideal Genie algorithm that has access to the transmitted symbols to identify a decoding failure. It is shown that the frame error rate (FER) of the HARQ algorithm follows the Genie algorithm from low to high signal-to-noise ratios (SNRs) and ensures the information throughput reverts back to 100% at high SNRs. Simulation results are presented for the rate 1/2 (N = 8, K = 4) and rate 5/8 (N = 16, K = 10) polar codes from the 5G standard over an additive white gaussian noise (AWGN).
Intelligence Quotient Issues Solved By Simulations: Development And Consequences

Throughout the entire first half of the Consists of a minimum century, several computational models of aptitude test issues were put out; butproducts were obtained century, we have witnessed an increase in the number of personal computers that can perform well on there hasn't been a comprehensive analysis of all connect to one another and what their true accomplishments are. There is also a lack of knowledge on what machine, whether they are helpful in assessing AI systems, if they are really difficult challenges, and whether they are helpful in comprehending. By giving a thorough from the 1960s to the present and their relationships, focusing f the models, how general or specific these models are, the Related methods they use in each case, their correlation with job behaviour, and their review of these issues. These experiments and the simulators that attempted them demonstrate that AI still lacks universal methods for addressing several issues concurrently. However, refocusing on these issues and taking a closer look at what intelligence tests may give for AI may help create new links as well as inspire new sorts of problem reservoirs.
LightMIRM: Light Meta-learned Invariant Risk Minimization for Trustworthy Loan Default Prediction

Machine learning models are increasingly applied to loan default prediction to reduce the labor cost of financial institutions and the waiting time of lenders. We find that existing loan default prediction models remain lack minimax fairness, i.e., encountering significant performance drops on underrepresented subpopulations. The main cause of this trustworthy issue is pursuing Empirical Risk Minimization over the whole population, which will overlook the underrepresented subpopulations. To tackle this issue, we split the training data into subpopulations (a.k.a. environments) and conduct Invariant Risk Minimization (IRM) to learn the optimal prediction model across environments. A technical challenge is the computation cost of directly using existing IRM methods suitable for loan default prediction, such as meta-IRM, which quadratically increases as the number of environments. To reduce the complexity in training, we propose a light meta-IRM method which reduces time complexity to be linear through environment sampling and loss replaying strategies. We apply the light meta-IRM to train a representative loan default prediction model and conduct both online and offline evaluations on a large auto loan platform. Extensive experiment results validate the advantage of the proposed light meta-IRM w.r. t. the overall accuracy, minimax fairness, and training cost.
Taylor Galerkin Pressure Correction (TGPC) Finite Element Method for Incompressible Newtonian Cable-Coating Flows

Received May. 23, 2018. Accepted for publication Jun. 28, 2018 DOI: http://dx.doi. org/10.31642/JoKMC/2018/050203 AbstractBased on Taylor Galerkin /pressure-correction (TGPC) finite element method, this work is concerned with numerical study for incompressible Newtonian cable coating flows. The fluid motion is described by using the Naiver-Stoke equations, which include two essential differential equations. One of them is the equation for conservation of mass and the other one is the equation of conservation of momentum equations. Moreover, this study shows the free surface location methodology to determine the free surface position, and the boundary conditions. The Phan-Thien (dh/dt) scheme is applied to calculate the change in the freesurface position. A number of computational investigations have been achieved to see the effect of different factors on the processing of coating. This includes investigating the influence of variation in surface tension on the shear rate and strain-rate stabilisation approach.