Kernel Algorithm Research Articles

RT-APT: A real-time APT anomaly detection method for large-scale provenance graph

Advanced Persistent Threats (APTs) are prevalent in the field of cyber attacks, where attackers employ advanced techniques to control targets and exfiltrate data without being detected by the system. Existing APT detection methods heavily rely on expert rules or specific training scenarios, resulting in the lack of both generality and reliability. Therefore, this paper proposes a novel real-time APT attack anomaly detection system for large-scale provenance graphs, named RT-APT. Firstly, a provenance graph is constructed with kernel logs, and the WL subtree kernel algorithm is utilized to aggregate contextual information of nodes in the provenance graph. In this way we obtain vector representations. Secondly, the FlexSketch algorithm transforms the streaming provenance graph into a sequence of feature vectors. Finally, the K-means clustering algorithm is performed on benign feature vector sequences, where each cluster represents a different system state. Thus, we can identify abnormal behaviors during system execution. Therefore RT-APT enables to detect unknown attacks and extract long-term system behaviors. Experiments have been carried out to explore the optimal parameter settings under which RT-APT can perform best. In addition, we compare RT-APT and the state-of-the-art approaches on three datasets, Laboratory, StreamSpot and Unicorn. Results demonstrate that our proposed method outperforms the state-of-the-art approaches from the perspective of runtime performance, memory overhead and CPU usage.

Diffusion random Fourier adaptive filtering algorithm based on logistic distance metric for distributed estimation

Distributed adaptive filtering over networks can improve filtering performance by fusing information from nodes within the same neighbor. In nonlinear estimation, adaptive filters derived from a linear framework usually suffer from large misalignment. To solve the above problem, this work develops a diffusion kernel filtering algorithm based on the random Fourier approximation method. To promote robustness to impulsive noise, the minimum logistic distance metric (LDM) is employed as a loss function. Compared to traditional kernel algorithms, the presented algorithm uses a fixed-length filter and is suitable for online distributed adaptive filtering tasks. In addition, this work also conducts a performance analysis based on Isserlis' and Price's theorems with several statistical assumptions. Simulations are conducted to exhibit the robustness of the proposed method to impulsive noise and to examine the accuracy of the theory on performance analysis.

Remora‐CNN: A Novel and Effective Method for Rice Leaf Disease Detection and Classification

ABSTRACTFor millions of people worldwide, rice is one of the main food crops. Nevertheless, while being grown, rice is susceptible to many diseases. Most rice plant diseases are influenced by biotic and abiotic factors, including nematodes, viroids, fungus, viruses, bacteria, and other microorganisms, as well as temperature and other environmental factors. Thus, an automatic early classification of leaf disease is necessary to improve the rice yield. In this paper, for identifying and categorizing the rice leaf disease, a convolutional neural network (CNN) model is used, and the CNN is trained using the Remora Optimization Algorithm (ROA). A better classification outcome is attained by performing the segmentation process using K‐means with the Fractional Tangential‐Spherical Kernel (FTSK) algorithm. Furthermore, the developed Remora Optimization‐ Convolutional Neural Network (Remora‐CNN) method achieved the optimal performance based on the testing accuracy, sensitivity and specificity of 0.925, 0.931, and 0.941 using the Rice Leaf Disease Image Samples Dataset.

Retraction Note: Solutions of Bagley–Torvik and Painlevé equations of fractional order using iterative reproducing kernel algorithm with error estimates

Retraction Note: Solutions of Bagley–Torvik and Painlevé equations of fractional order using iterative reproducing kernel algorithm with error estimates

A Hybrid Algorithm for Multiple Disease Prediction: Radial Basis Function and Logistic Regression

Disease prediction is an important aspect of modern medicine, which aims to diagnose disease early and provide appropriate treatment to patients. This research uses a hybrid approach that combines the RBF (Radial Basis Function) kernel algorithm with logistic regression to predict various diseases in medical datasets. This method is intended to improve prediction performance by exploiting the advantages of each algorithm. This research uses a dataset containing medical information about several diseases collected from the Kaggle dataset. First, the RBF kernel is applied to transform the data features into a more informative, non-linear representation. Then, the logistic regression model is used to make predictions based on the features that have been processed by the RBF kernel. In this research, the hybrid RBF (Radial Basis Function) method was proven to be superior in predicting multiple diseases. This method shows the highest accuracy of 0.9460, as well as excellent precision, recall, and F1-score values of 0.8680, 0.8097, and 0.8294, respectively. The advantage of the hybrid RBF method lies in its ability to capture complex patterns in data that other methods often cannot identify, as well as its ability to handle non-linear decision boundaries, which are a common characteristic in medical datasets. Keywords: Disease prediction, Hybrid approach, RBF kernel algorithm, Logistic regression, medical datasets

Detecting Critical Mismatched Driver Visual Attention During Lane Change: An Embedding Kernel Algorithm

Detecting Critical Mismatched Driver Visual Attention During Lane Change: An Embedding Kernel Algorithm

Parameterized Complexity of Streaming Diameter and Connectivity Problems

We initiate the investigation of the parameterized complexity of Diameter and Connectivity in the streaming paradigm. On the positive end, we show that knowing a vertex cover of size k allows for algorithms in the Adjacency List (AL) streaming model whose number of passes is constant and memory is O(logn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(\\log n)$$\\end{document} for any fixed k. Underlying these algorithms is a method to execute a breadth-first search in O(k)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(k)$$\\end{document} passes and O(klogn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(k \\log n)$$\\end{document} bits of memory. On the negative end, we show that many other parameters lead to lower bounds in the AL model, where Ω(n/p)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega (n/p)$$\\end{document} bits of memory is needed for any p-pass algorithm even for constant parameter values. In particular, this holds for graphs with a known modulator (deletion set) of constant size to a graph that has no induced subgraph isomorphic to a fixed graph H, for most H. For some cases, we can also show one-pass, Ω(nlogn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega (n \\log n)$$\\end{document} bits of memory lower bounds. We also prove a much stronger Ω(n2/p)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega (n^2/p)$$\\end{document} lower bound for Diameter on bipartite graphs. Finally, using the insights we developed into streaming parameterized graph exploration algorithms, we show a new streaming kernelization algorithm for computing a vertex cover of size k. This yields a kernel of 2k vertices (with O(k2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(k^2)$$\\end{document} edges) produced as a stream in poly(k)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {poly}(k)$$\\end{document} passes and only O(klogn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(k \\log n)$$\\end{document} bits of memory.

3D geometric kernel computation in polygon mesh structures

This paper introduces a novel approach to compute the geometric kernel of a polygon mesh embedded in 3D. The geometric kernel defines the set of points inside or on the shape’s boundary, ensuring visibility of the entire shape. The proposed method utilizes scattered rays to identify a sufficient number of sample points on the kernel surface and subsequently leverages these points to locate as many surface vertices as possible. By computing the convex hull of these identified points, we derive an approximation of the kernel. Notably, the output of our method consists exclusively of interior or boundary points of the actual kernel. Comparative evaluations against established CGAL and Polyhedron Kernel algorithms highlight our method’s superior computational speed and high approximation accuracy. The parametric structure of our solution allows for different levels of accuracy to be obtained, enabling the user to tailor the approximation to their specific needs. This property sets our algorithm apart from others and provides greater flexibility in its use. Additionally, adjusting the algorithmic settings also enables the computation of the kernel itself with a trade-off in computational speed. Furthermore, our algorithm swiftly and accurately identifies an empty kernel for non-star-shaped configurations.

Kernelization for edge triangle packing and covering via a discharging method

Edge Triangle Packing and Edge Triangle Covering are dual problems extensively studied in the field of parameterized complexity. Given a graph G and an integer k, Edge Triangle Packing seeks to determine whether there exists a set of at least k edge-disjoint triangles in G, while Edge Triangle Covering aims to find out whether there exists a set of at most k edges that intersects all triangles in G. Previous research has shown that Edge Triangle Packing has a kernel of (3+ϵ)k vertices, while Edge Triangle Covering has a kernel of 6k vertices. In this paper, we show that the two problems allow kernels of 3k vertices, improving all previous results. A significant contribution of our work is the utilization of a novel discharging method for analyzing kernel size, which exhibits potential for analyzing other kernel algorithms.

Nearly Time-Optimal Kernelization Algorithms for the Line-Cover Problem with Big Data

Nearly Time-Optimal Kernelization Algorithms for the Line-Cover Problem with Big Data

Stability of convergence rates: kernel interpolation on non-Lipschitz domains

Abstract Error estimates for kernel interpolation in Reproducing Kernel Hilbert Spaces usually assume quite restrictive properties on the shape of the domain, especially in the case of infinitely smooth kernels like the popular Gaussian kernel. In this paper we prove that it is possible to obtain convergence results (in the number of interpolation points) for kernel interpolation for arbitrary domains $\varOmega \subset{\mathbb{R}} ^{d}$, thus allowing for non-Lipschitz domains including e.g., cusps and irregular boundaries. Especially we show that, when going to a smaller domain $\tilde{\varOmega } \subset \varOmega \subset{\mathbb{R}} ^{d}$, the convergence rate does not deteriorate—i.e., the convergence rates are stable with respect to going to a subset. We obtain this by leveraging an analysis of greedy kernel algorithms. The impact of this result is explained on the examples of kernels of finite as well as infinite smoothness. A comparison to approximation in Sobolev spaces is drawn, where the shape of the domain $\varOmega $ has an impact on the approximation properties. Numerical experiments illustrate and confirm the analysis.

Analyzing Public Sentiments on Disaster Relief Efforts Through Social Media Data

Social media has become a source of quick but not necessarily accurate information. Especially in social media X, which is often used to share information. This research aims to conduct sentiment analysis on posts related to natural disasters that aim to maximize assistance to victims of natural disasters. This research takes datasets from tweets on social media X, the data will be labeled into positive and negative. And then the preprocessing process will be carried out, in this study, categorization will be carried out on each tweet related to the category, then the data will be divided into training and testing. Then the Term Frequency-Inverse Document Frequency (TF-IDF) feature is used to assist in reducing the weight of words that often appear in the dataset, The next step involves designing a system with a focus on applying the Support Vector Machine (SVM) Polynomial Kernel algorithm which becomes a classifier which will later be used to find the best hyperline or decision boundary that divides each review into two classes, namely positive tweets and negative tweets. Then obtained with a value of Precision of 86.49%, Recall 99.21%, F1-Score 92.42%, and Accuracy of 87.01%. This research is expected to provide involvement in making a fast and effective decision for victims of natural disasters.

Near infrared spectroscopy for determination of moisture content in lyophilized formulation

A non-invasive near infrared (NIR) spectroscopic method was developed for the quantitative moisture determination in a lyophilized injection formulation. The calibration samples were prepared by exposing lyophilized samples at different temperatures and relative humidity. The samples from different scales and different process parameters were considered for adding robustness to the model. The NIR spectra were collected using a Fourier-transform (FT) NIR with a diffuse reflectance probe and the same samples were further analyzed by the Karl Fisher (KF) method for moisture content. The pre-treated NIR spectra were used for quantitative method development for moisture content. Partial least squares regression was used to develop calibrations in the 5600-4950 cm−1 region with calibration coefficient of determination (R2) of 0.96 and root mean square error of calibration (RMSEC) of 0.149. The model was cross-validated internally using the Kernel algorithm with r2 = 0.96 and RMSECV = 0.15. The accuracy of the NIR method against the KF method, precision, and reproducibility were good and the model was robust in predicting different external validation samples. This work allowed NIR as an alternative measurement for moisture analysis as well as facilitate 100% monitoring before packaging and save the cost of sample and time of KF analysis.

Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging

Pairwise learning, an important domain within machine learning, addresses loss functions defined on pairs of training examples, including those in metric learning and AUC maximization. Acknowledging the quadratic growth in computation complexity accompanying pairwise loss as the sample size grows, researchers have turned to online gradient descent (OGD) methods for enhanced scalability. Recently, an OGD algorithm emerged, employing gradient computation involving prior and most recent examples, a step that effectively reduces algorithmic complexity to O(T), with T being the number of received examples. This approach, however, confines itself to linear models while assuming the independence of example arrivals. We introduce a lightweight OGD algorithm that does not require the independence of examples and generalizes to kernel pairwise learning. Our algorithm builds the gradient based on a random example and a moving average representing the past data, which results in a sub-linear regret bound with a complexity of O(T). Furthermore, through the integration of O(√T logT) random Fourier features, the complexity of kernel calculations is effectively minimized. Several experiments with real-world datasets show that the proposed technique outperforms kernel and linear algorithms in offline and online scenarios.

Flexible Throat Patch for Phonetic Recognition and Ingestion Monitoring via Sensory Transition based on Machine Learning

AbstractThe throat is an organ that includes the larynx, esophagus, and bronchi. Vocalization or deglutition triggers micro‐movements of muscles around the throat. In this study, based on machine learning using a flexible patch, pronunciation is identified by analyzing the vibration and movement of the laryngeal muscles, and the type of food consumed is determined through peristalsis analysis. Previously, voices are recognized through cochlea hair cells, and the type of food intake is identified through taste receptors. However, this study converted auditory or gustatory senses to tactile stage through sensory transition and finally visualized. Using a customized throat patch with segmented nodes, the pronunciation of [a], [e], [i], [o], and [u], representative vowels of languages worldwide, are distinguished with an accuracy of 85.9% based on the polynomial kernel algorithm. In addition, cookies, water, gummies, and chocolate are distinguished with 91.1% accuracy by detecting differences in the esophagus's peristalsis. Transformation of auditory or gustatory sensations to tactile information and visualization is expected to be used for communication in noisy environments or soundless environments such as outer space. This approach can also provide significant potential for determining the type of ingestion for users who have lost their sense of taste.

Effectiveness of the MineReduce approach in reducing the size of combinatorial optimization problem instances

Previous work has shown that the performance of metaheuristics can benefit from using data mining techniques, which can improve the obtained solutions. In a strategy that has been successfully used for over a decade, data mining techniques are applied to extract patterns from good solutions found in the early stages of the heuristic process, and these patterns are introduced into the solutions generated afterwards. Recently, a novel approach that uses data mining for problem size reduction, called MineReduce, has been proposed and achieved even more impressive results in improving metaheuristics. In this work, we apply the MineReduce approach to improve the performance of a multi-start iterated tabu search algorithm. The results show that with the incorporation of the MineReduce approach, the method can obtain better solutions while spending less time. Additionally, we assessed the effectiveness of the size reduction performed by MineReduce, comparing it to a kernelization algorithm. Despite the lack of guarantees on optimality or size-bounding, the reduction carried out by MineReduce was effective in practice.

Space limited linear-time graph algorithms on big data

We study algorithms for graph problems in which the graphs are of extremely large size N so that super-linear time ω(N) or linear space Θ(N) would become impractical. We use a parameter k to characterize the computational power of a normal computer that can provide additional time and space bounded by polynomials of k in dealing with the large graphs. In particular, we are interested in strict linear-time algorithms using space O(kO(1)). In our case studies, as examples, we present (1) a randomized greedy algorithm of time O(N) and space O(k2) for a parameterized version of the Maximal Matching problem; and (2) randomized kernelization algorithms of time O(N) and space O(kO(1)) for a number of well-known NP-hard problems. Our kernelization algorithms have their kernel sizes match the best kernel sizes by known polynomial-time kernelization algorithms with no space constraints for the problems. We also study the relationship between our proposed model and the streaming model. This study motivates a new streaming kernelization algorithm for the famous Vertex Cover problem that has an optimal update time complexity while matches the best known space complexity of streaming algorithms for the problem.

Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph.

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for τ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this article proposes a novel local sample-weighted MKC (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Furthermore, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC implicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.

Machine learning for predicting cognitive deficits using auditory and demographic factors.

Predicting neurocognitive deficits using complex auditory assessments could change how cognitive dysfunction is identified, and monitored over time. Detecting cognitive impairment in people living with HIV (PLWH) is important for early intervention, especially in low- to middle-income countries where most cases exist. Auditory tests relate to neurocognitive test results, but the incremental predictive capability beyond demographic factors is unknown. Use machine learning to predict neurocognitive deficits, using auditory tests and demographic factors. The Infectious Disease Center in Dar es Salaam, Tanzania. Participants were 939 Tanzanian individuals from Dar es Salaam living with and without HIV who were part of a longitudinal study. Patients who had only one visit, a positive history of ear drainage, concussion, significant noise or chemical exposure, neurological disease, mental illness, or exposure to ototoxic antibiotics (e.g., gentamycin), or chemotherapy were excluded. This provided 478 participants (349 PLWH, 129 HIV-negative). Participant data were randomized to training and test sets for machine learning. The main outcome was whether auditory variables combined with relevant demographic variables could predict neurocognitive dysfunction (defined as a score of <26 on the Kiswahili Montreal Cognitive Assessment) better than demographic factors alone. The performance of predictive machine learning algorithms was primarily evaluated using the area under the receiver operational characteristic curve. Secondary metrics for evaluation included F1 scores, accuracies, and the Youden's indices for the algorithms. The percentage of individuals with cognitive deficits was 36.2% (139 PLWH and 34 HIV-negative). The Gaussian and kernel naïve Bayes classifiers were the most predictive algorithms for neurocognitive impairment. Algorithms trained with auditory variables had average area under the curve values of 0.91 and 0.87, F1 scores (metric for precision and recall) of 0.81 and 0.76, and average accuracies of 86.3% and 81.9% respectively. Algorithms trained without auditory variables as features were statistically worse (p < .001) in both the primary measure of area under the curve (0.82/0.78) and the secondary measure of accuracy (72.3%/74.5%) for the Gaussian and kernel algorithms respectively. Auditory variables improved the prediction of cognitive function. Since auditory tests are easy-to-administer and often naturalistic tasks, they may offer objective measures or predictors of neurocognitive performance suitable for many global settings. Further research and development into using machine learning algorithms for predicting cognitive outcomes should be pursued.

Comparative study of kernel algorithms on SIMD vector processor for 5G massive MIMO

Comparative study of kernel algorithms on SIMD vector processor for 5G massive MIMO