Robust Vector Research Articles

A Novel Flight Dynamics Modeling Using Robust Support Vector Regression against Adversarial Attacks

<div>An accurate Unmanned Aerial System (UAS) Flight Dynamics Model (FDM) allows us to design its efficient controller in early development phases and to increase safety while reducing costs. Flight tests are normally conducted for a pre-established number of flight conditions, and then mathematical methods are used to obtain the FDM for the entire flight envelope. For our UAS-S4 Ehecatl, 216 local FDMs corresponding to different flight conditions were utilized to create its Local Linear Scheduled Flight Dynamics Model (LLS-FDM). The initial flight envelope data containing 216 local FDMs was further augmented using interpolation and extrapolation methodologies, thus increasing the number of trimmed local FDMs of up to 3,642. Relying on this augmented dataset, the Support Vector Machine (SVM) methodology was used as a benchmarking regression algorithm due to its excellent performance when training samples could not be separated linearly. The trained Support Vector Regression (SVR) predicted the FDM for the entire flight envelope. Although the SVR-FDM showed excellent performance, it remained vulnerable to adversarial attacks. Hence, we modified it using an adversarial retraining defense algorithm by transforming it into a Robust SVR-FDM. For validation studies, the quality of predicted UAS-S4 FDM was evaluated based on the Root Locus diagram. The closeness of predicted eigenvalues to the original eigenvalues confirmed the high accuracy of the UAS-S4 SVR-FDM. The SVR prediction accuracy was evaluated at 216 flight conditions, for different numbers of neighbors, and a variety of kernel functions were also considered. In addition, the regression performance was analyzed based on the step response of state variables in the closed-loop control architecture. The SVR-FDM provided the shortest rise time and settling time, but it failed when adversarial attacks were imposed on the SVR. The Robust-SVR-FDM step response properties showed that it could provide more accurate results than the LLS-FDM approach while protecting the controller from adversarial attacks.</div>

DeepDual-SD: Deep Dual Attribute-Aware Embedding for Binary Code Similarity Detection

Binary code similarity detection (BCSD) is a task of detecting similarity of binary functions which are not available to the corresponding source code. It has been widely utilized to facilitate various kinds of crucial security analysis in software engineering. Because of the complexity of the program compilation process, identifying binary code similarity presents tough challenges. The most sensible binary similarity detector relies on a robust vector representation of binary code. However, few BCSD approaches are suitable to form vector representations for analyzing similarities between binaries, which may not only diverge in semantics but also in structures. And the existing solutions which only depend on hands-on feature engineering to form feature vectors, fail to take into consideration the relationships between instructions. To resolve these problems, we propose a novel and unified approach called DeepDual-SD that aims to combine the dual attributes (semantic and structural attribute). More specifically, DeepDual-SD consists of two branches, in which one text-based feature representation is driven by semantic attribute learning to exploit instruction semantics, another graph-based feature representation for structural attribute learning to investigate structural differences. Meanwhile deep embedding (DE) technology is utilized to map this information into low-dimensional vector representation. In addition, to get together the dual attributes, a fusion mechanism based on gate architecture is designed for learning to pay proper attention between the two attribute-aware embeddings. Experimental verifications are conducted on Openssl and Debian datasets for several tasks, including cross-compiler, cross-architecture and cross-version scenarios. The results demonstrate that our method outperforms the state-of-the-art BCSD methods in different scenarios in terms of detection accuracy.

Robust flow vector estimation for echocardiography with extended Nyquist velocity using dual-PRF approach: a flow phantom study

To enable quantitative assessments of multi-directional blood flow dynamics such as those in the heart, a two-dimensional (2D) flow vector estimation method using dual-angle Doppler measurements with a sector ultrasound probe was developed. However, that technique was prone to aliasing artifacts because the dual-angle transmissions reduced the pulse repetition frequency (PRF) and Nyquist flow speed by half for each Doppler measurement. To overcome this problem, this study devised a new dual-angle vector flow imaging framework with extended Nyquist velocity using the dual-PRF de-aliasing method. In the proposed framework, the Nyquist flow speed was doubled to 930 mm s−1 compared with the conventional dual-angle approach. Imaging experiments demonstrated that the proposed framework could correct the aliasing artifacts in color Doppler frames and successfully derive 2D flow vectors comparable to particle image velocimetry measurements with a relative error of −14.5% in the fast and 35.2% in the slow flow phases in a pulsatile flow condition.

Characterizing a Class of Robust Vector Polynomial Optimization via Sum of Squares Conditions

Characterizing a Class of Robust Vector Polynomial Optimization via Sum of Squares Conditions

Robust Twin Support Vector Regression with Smooth Truncated Hε Loss Function

Robust Twin Support Vector Regression with Smooth Truncated Hε Loss Function

Does the rise in cases of Kyasanur forest disease call for the implementation of One Health in India?

The viral hemorrhagic illness known as Kyasanur forest disease (KFD), also referred to as monkey fever, is transmitted by ticks. The etiological agent, which was formerly isolated from monkeys, is Kyasanur forest disease virus (KFDV), an RNA virus belonging to the family Flaviviridae. Since 1957, India has reported 400–500 cases annually, with a case fatality rate of 1–3%. Shiroma, Chikkamagalore, Uttara Kannada, Dakshina Kannada, and Udupi are the five regions in Karnataka, India where KFD is highly prevalent, with around 3263 notified cases reported between 2003 and 2012, of which 823 cases were laboratory confirmed.The symptoms of monkey fever can range from mild sickness to severe neurological sequelae. Currently, prophylaxis involves administration of formalin-inactivated tissue culture vaccine. Despite the continuing vaccination programs in endemic areas for KFD, new cases are being reported. The current availability and effectiveness of the vaccine are not enough to provide protective immunity and thus prevent new outbreaks.Our study examined the known literature, knowledge gaps, and host responses associated with KFD. There is a need for robust vector control, public awareness campaigns, mass vaccination programmes, a full understanding of the eco-epidemiological elements of the disease, and implementation of a One Health program. These could all support prevention and management protocols, and thus help to address the issue.

MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study

BackgroundTo develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function.MethodsA retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models.ResultsThe models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935–0.940), 0.919 (95%CI 0.916–0.922), and 0.959 (95%CI 0.956–0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800–0.807), 0.852 (95%CI 0.846–0.857), and 0.863 (95%CI 0.857–0.887) in the validation cohorts, respectively.ConclusionWe developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.

Robust Online Support Vector Regression with Truncated ε-Insensitive Pinball Loss

Advances in information technology have led to the proliferation of data in the fields of finance, energy, and economics. Unforeseen elements can cause data to be contaminated by noise and outliers. In this study, a robust online support vector regression algorithm based on a non-convex asymmetric loss function is developed to handle the regression of noisy dynamic data streams. Inspired by pinball loss, a truncated ε-insensitive pinball loss (TIPL) is proposed to solve the problems caused by heavy noise and outliers. A TIPL-based online support vector regression algorithm (TIPOSVR) is constructed under the regularization framework, and the online gradient descent algorithm is implemented to execute it. Experiments are performed using synthetic datasets, UCI datasets, and real datasets. The results of the investigation show that in the majority of cases, the proposed algorithm is comparable, or even superior, to the comparison algorithms in terms of accuracy and robustness on datasets with different types of noise.

Multiscale Multidirection Binary Pattern Learning for Discriminant Palmprint Identification

Binary pattern learning has been achieved promising recognition performance in palmprint identification. However, most of direction representation-based methods are hand-crafted. They present the direction characteristics only in one scale, and thus the comprehensive information from multiple scales and multiple directions are ignored. This is also true especially for the binary coding-based methods. As a solution to the problem, this paper proposes a multi-scale multi-direction binary (MSMDB) pattern learning method for palmprint identification. Specifically, we first form a feature container of multi-scales multi-directions for palmprint images. The container contains robust direction average vectors (DAVs). Then, a learning model is proposed to learn robust and discriminant direction information from the extracted convolution average features. The proposed model can project these DAVs into discriminant direction binary codes. The model not only maximizes the variance of the learned binary codes and inter-class distance, but also minimizes intra-class distance. Finally, we cluster all block histograms of multi-scale projections to form the discriminative direction binary palmprint descriptors for palmprint identification. We evaluate the performance of the proposed method on five public databases compared to other related methods. For example, MSMDB can achieve the best identification accuracy of 97.93 percent when the number of training data is 2 on the database IITD.

Self-Supervised Latent Representations of Network Flows and Application to Darknet Traffic Classification

Characterizing network flows is essential for security operators to enhance their awareness about cyber-threats targeting their networks. The automation of network flow characterization with machine learning has received much attention in recent years. To this aim, raw network flows need to be transformed into structured and exploitable data. In this research work, we propose a method to encode raw network flows into robust latent representations exploitable in downstream tasks. First, raw network flows are transformed into graph-structured objects capturing their topological aspects (packet-wise transitional patterns) and features (used protocols, packets’ flags, etc.). Then, using self-supervised learning techniques like Graph Auto-Encoders and Anonymous Walk Embeddings, each network flow graph is encoded into a latent representation that encapsulates both the structure of the graph and the features of its nodes, while minimizing information loss. This results in semantically-rich and robust representation vectors which can be manipulated by machine learning algorithms to perform downstream network-related tasks. To evaluate our network flow embedding models, we use probing flows captured with two /20 network telescopes and labeled using reports originating from different sources. The experimental results show that the proposed network flow embedding method allows for reliable darknet probing activity classification. Furthermore, a comparison between our self-supervised approach and a fully-supervised graph convolutional network shows that, in situations with limited labeled data, the downstream classification model that uses the derived latent representations as inputs outperforms the fully-supervised graph convolutional network. There are many applications of this research work in cybersecurity, such as network flow clustering, attack detection and prediction, malware detection, vulnerability exploit analysis, and inference of attacker’s intentions.

Histogram of the orientation of the weighted phase descriptor for multi-modal remote sensing image matching

Multi-modal remote sensing image (MRSI) has nonlinear radiation distortion (NRD) and significant contrast differences to which image gradient features are usually sensitive. Although image phase features are more robust against NRD, they might not be much helpful in resolving the problems of directional inversion or phase extreme value mutations that are common in the phase feature calculation. To address these issues, a new MRSI matching method—“histogram of the orientation of weighted phase” (HOWP)—is proposed in this paper. This method distinguishes itself from other methods in three aspects: (1) a feature aggregation strategy is used to optimize feature points by extracting the corner and blob features separately; (2) a novel weighted phase orientation model is established to replace the traditional image gradient orientation features; and (3) a regularization-based log-polar descriptor is constructed to generate robust feature description vectors. To evaluate the performance of the proposed method, we selected 50 sets of typical MRSIs with translation, scale, and rotation differences for comparison with the other four state-of-the-art methods. The results show that our method is more resistant to radiometric distortion and the contrasting differences in MRSIs. It also performs better in tackling the problems of direction reversal and phase extreme value mutation, as evidenced by more, the number of correct matches (NCM). Since the method has improved the average NCM by 1.6–4.5 times, the average success rate by 35.5%, and the average rate of correct matches by 11.1% with an average root of mean-squared error of 1.93 pixels. Moreover, we have put forward an extended version of the HOWP method (Simplified-HOWP) when there is no image rotation, which manifests in an average 0.75 times improvement in NCM of Simplified-HOWP performance over that of the HOWP method. The executable code and test data are linked in https://skyearth.org/publication/project/HOWP/.

Successful suspension-based viral vector manufacturing scale-up from process development to clinic

Cell and gene therapies have emerged as promising treatments for previously untreatable diseases, with viral vectors being used as the current preferred delivery vehicle. Safe, robust, and cost-effective vector manufacturing processes are necessary to support the demands of the patient population. Here, expert therapy developers, solutions providers, and CDMOs discuss considerations for the successful scale-up of viral vector processes from bench to commercial manufacturing.

Learning from crowds with robust support vector machines

Learning from crowds with robust support vector machines

Robust nonparallel support vector machine with privileged information for pattern recognition

Robust nonparallel support vector machine with privileged information for pattern recognition

A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial Attacks

Anchor link prediction across social networks plays an important role in multiple social network analysis. Traditional methods rely heavily on user privacy information or high-quality network topology information. These methods are not suitable for multiple social networks analysis in real-life. Deep learning methods based on graph embedding are restricted by the impact of the active privacy protection policy of users on the graph structure. In this paper, we propose a novel method which neutralizes the impact of users’ evasion strategies. First, graph embedding with conditional estimation analysis is used to obtain a robust embedding vector space. Secondly, cross-network features space for supervised learning is constructed via the constraints of cross-network feature collisions. The combination of robustness enhancement and cross-network feature collisions constraints eliminate the impact of evasion strategies. Extensive experiments on large-scale real-life social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of precision, adaptability, and robustness for the scenarios with evasion strategies.

Big Data e-Commerce Economic Development by Using IoT and Support Vector Machine

The development of e-commerce economy is closely related to the progress of computer technology, which provides an effective data basis for the development of e-commerce economy. Support vector machines are not only used to analyze and solve the second-class classification problems but also can analyze and solve the first-class classification problems. The rise of the e-commerce economy is not only conducive to increasing people’s income but also conducive to the realization of income increase in some economically backward areas and the improvement of various infrastructures, thus breaking the characteristics of inconvenient transportation in some areas and promoting the development of regional economy. Therefore, this study introduces the concept of Internet of Things and robust support vector machine technology, which are more advanced in computer technology, into the e-commerce system to optimize the existing e-commerce platform and knowledge mode. From the regression results of the fixed effect model, the e-commerce economy will have a certain positive impact on the regional economic gap, although the magnitude of the impact is not large. From the overidentification test and the regression results of the dynamic panel, the sign of the variable regression coefficient has been relatively stable, which means that the dynamic panel model described in this study not only does not have the problem of overidentification but also has stability.

Disturbance Rejection Analysis of a Robust Vector Controlled Induction Motor Drive with an Adaptive Quasi-Sliding Mode Controller

Disturbance Rejection Analysis of a Robust Vector Controlled Induction Motor Drive with an Adaptive Quasi-Sliding Mode Controller

Robust and Sparse Dual Tree Complex Wavelet Transform-Based Twin Support Vector Regression for 5G InH and V2I Communications

Robust and Sparse Dual Tree Complex Wavelet Transform-Based Twin Support Vector Regression for 5G InH and V2I Communications

A novel robust support vector machine classifier with feature mapping

Support vector machines with different kernel functions are useful for binary classification. In this paper, a novel support vector machine model with feature mapping is proposed. Feature mapping is achieved based on the equivalence of the recently proposed soft quadratic surface support vector machine. In this model, a technique for pre-selecting training points is initially utilized to improve robustness. Then, the feature mapping and kernel trick are combined to handle datasets with different distributions, particularly nonlinearly separable datasets. This model can be transformed into a convex quadratic programming problem, which is efficiently solved by applying the sequential minimal optimization algorithm. Finally, numerical tests on several artificial and benchmark datasets reveal the superior performance of the proposed method over other well-known classification methods.

A novel robust nonparallel support vector classifier based on one optimization problem

A novel robust nonparallel support vector classifier based on one optimization problem