Reviewerly/exHarmony huggingface dataset:
Exploring Performance Degradation in Virtual Machines Sharing a Cloud Server

Cloud computing has become a leading technology for IT infrastructure, with many companies migrating their services to cloud servers in recent years. As cloud services continue to expand, the issue of cloud monitoring has become increasingly important. One important metric to monitor is CPU steal time, which measures the amount of time a virtual CPU waits for the actual CPU. In this study, we focus on the impact of CPU steal time on virtual machine performance and the potential problems that can arise. We implement our work using an OpenStack-based cloud environment and investigate intrusive and non-intrusive monitoring methods. Our analysis provides insights into the importance of CPU steal time monitoring and its impact on cloud performance.
State-Sensitive Black-Box Web Application Scanning for Cross-Site Scripting Vulnerability Detection

Black-box web application scanning has been a popular technique to detect Cross-Site Scripting (XSS) vulnerabilities without prior knowledge of the application. However, several limitations lead to low efficiency of current black-box scanners, including (1) the scanners waste time by repetitively visiting similar states, such as similar HTML forms of two different products, and (2) using a First-In-First-Out (FIFO) fuzzing order for the collected forms has led to low efficiency in detecting XSS vulnerabilities, as different forms have different potential possibilities of XSS vulnerability. In this paper, we present a state-sensitive black-box web application scanning method, including a filtering method for excluding similar states and a heuristic ranking method for optimizing the fuzzing order of forms. The filtering method excludes similar states by comparing readily available characteristic information that does not require visiting the states. The ranking method sorts forms based on the number of injection points since it is commonly observed that forms with a greater number of injection points have a higher probability of containing XSS vulnerabilities. To demonstrate the effectiveness of our scanning method, we implement it in our black-box web scanner and conduct experimental evaluations on eight real-world web applications within a limited scanning time. Experimental results demonstrate that the filtering method improves the code coverage about 17% on average and the ranking method helps detect 53 more XSS vulnerabilities. The combination of the filtering and ranking methods helps detect 81 more XSS vulnerabilities.
Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm

Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications.
Facial Emotion Recognition Feature Extraction: A Survey

Facial emotion recognition is a process based on facial expression to automatically recognize individual emotion expression. Automatic recognition refers to creating computer systems that are able to simulate human natural ability of detection, analysis, and determination of emotion by facial expression. Human natural recognition uses various points of observation to make decision or conclusion on emotion expressed by the present person in front. Facial features efficiently extracted aid in improving the classifier performance and application efficiency. Many feature extraction methods based on shape, texture, and other local features are proposed in the literature, and this chapter will review them. This chapter will survey some recent and formal feature expression methods from video and image products and classify them according to their efficiency and application.
HLS‐based swarm intelligence driven optimized hardware IP core for linear regression‐based machine learning

Abstract Linear Regression (LR), as one of the essential Machine Learning (ML) models, incurs massive data crunching during the training phase based on many data points. Considering the computationally intensive nature in the LR models, an optimized dedicated hardware IP core design can be very effective. This paper proposes the following novelties: (a) an optimized hardware IP core design of linear regression‐based machine learning model using high‐level synthesis (HLS). More specifically, independent application specific datapath architectures of hardware IP for computing optimal bias and intercepts and cost function in LR‐ML are presented here; (b) an optimized hardware IP core design of LR based ML model by deducing dependency graph from its corresponding mathematical foundation; (c) register transfer level (RTL) design, using HLS, of the optimized LR based ML hardware IP core for computing cost function; (d) linear regression IP core design using multi‐layered tree‐height transformation (THT) and swarm intelligence based architectural exploration for optimized HLS design.
An end-to-end DNN-HMM based system with duration modeling for robust earthquake detection

Developing a reliable and robust automatic earthquake detection system is quite a challenging and highly necessary task as two conditions can make this task a difficult one. First, earthquake detection systems may perform more poorly if they are employed in a region that is different from the one where the training database corresponds to. Systems trained with local databases are assumed to perform better. Nevertheless, these databases are usually limited. Second, the performance of such systems worsens when the SNR of the seismogram signals decreases. This paper proposes an end-to-end DNN-HMM based scheme to address these limitations, i.e. it does not require previous phase-picking, backed by engineered features and combined with duration modeling of states and seismic events. The proposed engine requires 10- or 15-times fewer parameters than state-of-the-art methods and therefore needs a smaller training database. Modeling duration can improve the noise robustness of the detection system significantly, particularly with limited training data; having a negligible increase in the number of training parameters. The system described here provides a F1-score 101% higher on average than schemes published elsewhere with Iquique and North Chile databases. It provides a reduction in F1-score equal to 10% when the average SNR is reduced by approximately 18 dB. This reduction in F1-score is at least half of the one observed with the state-of-the-art schemes in the same testing conditions. With respect to the detection of small earthquakes at short epicenter-station distances, the averaged precision provided by the DNN-HMM system with duration modeling is at least 5% higher than other systems.
Federated learning based on asynchronous and adjusted client training

Machine learning in small-scale unmanned aerial vehicle (UAV) deployment fails to accomplish complex missions of swarm intelligence. Federated learning (FL) tackles it by being able to handle large-scale learning, while keeping data private. The client data distribution involved in FL is often non-independent and identically distributed (Non-IID) and the amount of data is unbalanced. This may seriously affect the model’s convergence speed, and may even lead to nonconvergence. To tackle the above problems, we propose a new asynchronous FL scheme that combines adjusting client training. The scheme determines the weight of aggregation based on the staleness of client model parameters and the amount of data owned by the client, and dynamically adjusts the local training of each client using average training loss. Experiments on public datasets verify the effectiveness of our method, which can alleviate the impact of data Non-IID and data imbalance in asynchronous FL and accelerate the training of FL.
A Gaussian multi-scale mixture model-based outlier-robust Kalman filter

Abstract In many practical applications, the unknown non-stationary heavy-tailed distributed process and measurement noises (HTPMNs) are prone to occur due to inaccurate noise prior and stochastic outliers. To achieve the state estimation under this situation, a new outlier-robust Kalman filter based on Gaussian multi-scale mixture model (GMSMM-ORKF) is proposed in this paper. First, a new GMSMM is designed to model the one-step prediction and measurement likelihood probability density functions. Then, the hierarchical prior presentation on the mixture probability vectors and scale parameters are built. Furthermore, employing the variational Bayesian (VB) inference, a GMSMM-ORKF is derived. Finally, a classical target tracking model and real navigation experiment are utilized to demonstrate the effectiveness of the proposed filter.
Bi-EvoGAN: Bi-level Evolutionary Approach for Generative Adversarial Networks

Generative Adversarial Networks (GANs) are an adversarial approach to generative modeling using deep learning methods, which have become one of the most relevant topics in the last years. Despite producing interesting outputs, recent studies have demonstrated that GANs remain difficult to control and suffer from training problems, such as instability and mode collapse, as well as several aspects of their models are designed by hand. Neuroevolution is a recent evolution strategy that uses Evolutionary Algorithms (EAs) in order to provide an automatic design of deep neural networks architectures. Many recent efforts have been dedicated to contributing on GANs by developing different single-level EAs approaches, however they could not solve their common issues completely which are still open problems. In this paper, we propose a new design of evolutionary algorithms in the context of GANs, consisting of dividing the evolution search space into two complementary levels, modeling a Bi-level Evolutionary Approach called Bi-EvoGAN searching to surpass their adversarial optimization difficulties and also to demonstrate the performance of considering two levels of optimization in comparison to a single optimization level. To the best of our knowledge, this work presents the first application of the bi-level optimization in the context of generative adversarial networks. The upper level uses an Evolutionary Algorithm-based Topology Optimization method named "EA-TO" to evolve a population of GAN networks as a set of individuals in order to find the best network topology. The lower level applies an Evolutionary Algorithm-based Decomposition for Hyperparameter Optimization termed "EA/D-HO" in order to select the best hyperparameters of each GAN individual. The performance of Bi-EvoGAN is explained by the double optimization of the GAN topology and their hyperparameters through the new bilevel optimization design allowing to evolve a GAN population in its higher fitness which will improve the quality of the search method and consequently to accelerate the convergence. Experimental studies are carried out based on four well-known benchmark datasets, MNIST, Fashion-MNIST, CelebA and LSUN-Bedroom allowing to show the efficiency of our approach in comparison to the other state-of-the-art approaches.
An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images

Medical diagnosis greatly benefits from the use of MRI images. There have been many MRI image enhancement algorithms proposed over the years to aid doctors in disease diagnosis. Conventional image enhancing methods, nevertheless, may also boost the noise that was already there in the captured picture, which might cause distortion, which is unfavourable for the disease diagnosis. Therefore, an appropriate technique for noise suppression is necessary. An attempt has been made to incorporate a two-step filtering based algorithm for noise removal in cardiac MRI images while maintaining the images crucial compositional elements, such as their borders and textures. A combination of adaptive optimum weighted mean filter (AOWMF) and bilateral filter (BF) has been implemented to reduce impulsive noise and Gaussian noise that are both present as mixed noise. The fundamental idea of the AOWMF approach includes the detection of noise pixels using Crow Optimization Algorithm (COA), and replacing them with an optimum weighted mean value depending on a criterion of maximization fitness function. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) have been used to assess the effectiveness of the employed noise reduction technique. The result analysis are experimented in MATLB which reveals that proposed method is able to achieve high PSNR and SSIM and further, this method has the potential to enhance more detail structures of the input cardiac MRI images than existing methods.
Graph-coupled time interval network for sequential recommendation

Modeling the dynamics of sequential patterns (i.e., sequential recommendation) has obtained great attention, where the key problem is how to infer the next interesting item according to users' historical actions. Owing to high efficiency and accuracy, several Transformer-like frameworks have successfully achieved this task without adopting complicated recurrent or convolutional operations. Nevertheless, they focus only on the user-item bipartite graph and forgo other auxiliary information, which is non-trivial to attain satisfactory performance especially under long-tail distribution scenarios. In modeling short-term user interests, they fail to capture the time intervals between the recent actions and the target timestamp, which may result in the suboptimal performance. To settle such two problems, we propose a novel architecture for the task of sequential recommendation, namely graph-coupled time interval network (GCTN). Specifically, by means of item category information, we devise a category-aware graph propagation module to better learn user and item embeddings. Furthermore, we design a time-aware self-attention mechanism, which explicitly captures the effect of the time interval between two actions for next item prediction. To integrate these two parts into an organic whole, we introduce a personalized gating strategy to differentiate the importance of each part under the special context. Extensive experiments demonstrate the effectiveness and efficiency of GCTN over recent state-of-the-art methods on four real-world datasets, seamlessly combining the advantages of graph neural networks and Transformers.
AddLat2D the 2D Lattice Generator

This work addresses the challenges of acquiring additive manufacturing data, given the complexities and design possibilities of such structures. Researchers in additive manufacturing struggle with scarcity and unsuitability of 2D datasets which pose further difficulties. To overcome these concerns, this research presents an application, AddLat2D, for generating 2D lattice structure datasets tailored to user specifications. Building upon a previous version of the application (Baldwin et al., 2023,2022), this work highlights our development and usage of AddLat2D to generate datasets that have custom image size and pixel intensity values.
Sentiment analysis of online reviews for electric vehicles using the SMAA-2 method and interval type-2 fuzzy sets

Numerous electric vehicles (EVs) consumers express their opinions about EVs on automotive websites. These online reviews can be mined and analyzed to understand consumer sentiment and preferences. Although the existing sentiment analysis (SA) has made insight into sentiment polarity of EVs, the SA results on different EV attributes do not consider the accuracy of sentiment classification and the difficulty of accurately obtaining attribute weights in the process of all attribute information fusion. To address these issues, we provide an integrated framework for the EV SA problem using the stochastic multi-criteria acceptability analysis (SMAA) method with interval type-2 fuzzy sets (IT2FSs). We first use a logistic regression model to classify the sentiment polarity for each online comment and construct IT2FS by sentiment classification accuracy to represent the SA results for different EV attributes. Based on this, we considered all feasible weights in the weight space and utilize the SMAA-IT2FS method to aggregate SA results and achieve EV ranking. In addition, our approach provide decision makers with information to assist in decision-making such as the use of central weight vectors to identify the strengths and weaknesses of EVs and the analysis of potential competitors through dominance relationships. Finally, real data extracted from AutoHome websites are used as experimental data to illustrate the implementation of the proposed method and to demonstrate the effectiveness of our approach.
MVIRA: A model based on Missing Value Imputation and Reliability Assessment for mortality risk prediction

Mortality risk prediction is to predict whether a patient has the risk of death based on relevant diagnosis and treatment data. How to accurately predict patient mortality risk based on electronic health records (EHR) is currently a hot research topic in the healthcare field. In actual medical datasets, there are often many missing values, which can seriously interfere with the effect of model prediction. However, when missing values are interpolated, most existing methods do not take into account the fidelity or confidence of the interpolated values. Misestimation of missing variables can lead to modeling difficulties and performance degradation, while the reliability of the model may be compromised in clinical environments.We propose a model based on Missing Value Imputation and Reliability Assessment for mortality risk prediction (MVIRA). The model uses a combination of variational autoencoder and recurrent neural networks to complete the interpolation of missing values and enhance the characterization ability of EHR data, thus improving the performance of mortality risk prediction. In addition, we also introduce the Monte Carlo Dropout method to calculate the uncertainty of the model prediction results and thus achieve the reliability assessment of the model.We perform performance validation of the model on the public datasets MIMIC-III and MIMIC-IV. The proposed model showed improved performance compared with competitive models in terms of overall specialties.The proposed model can effectively improve the accuracy of mortality risk prediction, and can help medical institutions assess the condition of patients.
Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications

IoT comprises sensors and other small devices interconnected locally and via the Internet. Typical IoT devices collect data from the environment through sensors, analyze it and act back on the physical world through actuators. We can find them integrated into home appliances, Healthcare, Control systems, and wearables. This chapter presents a variety of applications where IoT devices are used for anomaly detection and correction. We review recent advancements in Machine/Deep Learning Models and Techniques for Anomaly Detection in IoT networks. We describe significant in-depth applications in various domains, Anomaly Detection for IoT Time-Series Data, Cybersecurity, Healthcare, Smart city, and more. The number of connected devices is increasing daily; by 2025, there will be approximately 85 billion IoT devices, spreading everywhere in Manufacturing (40%), Medical (30%), Retail, and Security (20%). This significant shift toward the Internet of Things (IoT) has created opportunities for future IoT applications. The chapter examines the security issues of IoT standards, protocols, and practical operations and identifies the hazards associated with the existing IoT model. It analyzes new security protocols and solutions to moderate these challenges. This chapter’s outcome can benefit the research community by encapsulating the Information related to IoT and proposing innovative solutions.
Perspective Chapter: New Image Denoising Approach Based on SWT and 2-D Dual-Tree Discrete Wavelet Transform

In this chapter, we propose a new image denoising approach. It consists in applying a Stationary Wavelet Transform (SWT) based image denoising technique, in the domain of 2‐D Dual-Tree Discrete Wavelet Transform. In fact, this proposed approach consists first of applying the 2‐D Dual-Tree Discrete Wavelet Transform to the noisy image. Then, the obtained noisy wavelet coefficients are denoised by applying to each of them a SWTbased image denoising technique. Finally, the denoised image is reconstructed by applying the inverse of the 2‐D Dual-Tree Discrete Wavelet Transform to the obtained denoised wavelet coefficients. For applying this SWT based image denoising technique, we use soft thresholding, the Daubechies 4 as the mother wavelet and the decomposion level is equal to 5. The performance of this proposed image denoising approach, is pouved by the results obtained from the computations of PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity).
Resourcefulness as an Important Indicator of Personal Well-Being

The study of the dynamics and features of students’ personal resourcefulness in the conditions of war in Ukraine is an important task, since understanding the peculiarities of the resourcefulness functioning will allow carrying out the necessary measures for its maintenance, recovery and development. The authors proposed a model of personal resourcefulness as a metacognitive experience. In the study, an express questionnaire “Personal resources” (O. Savchenko, S. Sukach) was used for the diagnosis of resourcefulness level. The study was conducted on a sample of 69 students of KNEU. To solve different tasks, several methodologies were used: factorial experimental design, comparative study of the same group in different conditions, and clustering of empirical data to determine different types of students’ resourcefulness. It was found that the overall level of personal resourcefulness is a significant factor in subjective, psychological, social and global well-being. Five types of personal resourcefulness were identified, namely “high resourcefulness,” “high-stress resistance,” “inefficient use of strategies,” “passivity in the search for resources” and “high emotional exhaustion.” Students with different types of resourcefulness have significant differences in well-being. The negative changes in students’ personal resourcefulness were revealed in a condition of the war in Ukraine (decrease in the level of overall resourcefulness and increase in emotional exhaustion). It has been established that students do not actively use restoring resource strategies in a prolonged stressful situation.