Search

Abstract The purpose of relation extraction is to identify the semantic relations between entities in sentences that contain two entities. Recently, many variants of the convolution neural network (CNN) have been introduced to relation extraction for the extracting of features—the quality of the neural network model directly affects the final quality of relation extraction. However, the traditional convolution network uses a fixed convolution kernel, so it is difficult to choose the size of the convolution kernel dynamically, which results in networks with weak representation ability. To address this, a novel CNN is designed with selective kernel networks and multigranularity. In the process of feature extraction, the model can adaptively select the size of the convolution kernel, that is, give more weight to the appropriate convolution kernel. It is then combined with multigranularity convolution to obtain more abundant semantic information. Finally, a new pooling method is designed to obtain more comprehensive information and improve model performance. Experimental results indicate that this method is effective without excessively deep network layers, and it also outperforms several competitive baseline methods.

Video recognition applications running on Graphics Processing Unit are composed of heterogeneous software portions, such as kernels or layers for neural networks. The authors propose the concepts of kernel vulnerability factor (KVF) and layer vulnerability factor (LVF), which indicate the probability of faults in a kernel or layer to affect the computation. KVF and LVF indicate the high‐level portions of code that are more likely, if corrupted, to impact the application's output. KVF and LVF restrict the architecture/program vulnerability factor analysis to specific portions of the algorithm, easing the criticality analysis and the implementation of selective hardening. We apply the proposed metrics to two Histogram of Oriented Gradients (HOG), and You Only Look Once (YOLO) benchmarks. We measure the KVF for HOG by using fault‐injection at both the architectural level and high level. We propose for HOG an efficient selective hardening technique able to detect 85% of critical errors with an overhead in performance as low as 11.8%. For YOLO, we study the LVF with architectural‐level fault‐injection. We qualify the observed corrupted outputs, distinguishing between tolerable and critical errors. Then, we proposed a smart layer duplication that detects more than 90% of errors, with an overhead lower than 60%. [ABSTRACT FROM AUTHOR]

In this study, the lifting scheme is first employed to design two‐channel biorthogonal graph filter bank. The biorthogonal condition is parameterised by imposing a single‐level lifting structure on the analysis and synthesis graph kernels. Based on the parametric structure, the two kernels are separately optimised by constrained quadratic programming. The obtained two‐channel biorthogonal graph filter banks are of structurally perfect reconstruction. Numerical results and comparison are included to show the proposed algorithm can lead to biorthogonal graph filter banks with improved performance. [ABSTRACT FROM AUTHOR]

Purpose: The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. Methods: A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0-16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. Results: At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels. Conclusions: The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions. [ABSTRACT FROM AUTHOR]

With the flourish of the nonlocal mean method, the neighborwise similarity metric is widely applied in speckle reduction for its robust performance on the search of similar samples. In this metric, an isotropic kernel function is usually chosen to aggregate the corresponding pixels' distance between two neighborhoods. It means that the kernel function is considered as the explanation of the local spatial relationship at each pixel. However, for anisotropic features (such as edges and lines), a strong relationship exists along their directions rather than across them, so the isotropic kernel is not suitable to explain the spatial relationship around these features. Meanwhile, due to the inherent speckle in synthetic aperture radar (SAR) images, the discrimination and exploration of the geometrical properties of anisotropic features are important for the construction of adaptive kernel function. In this paper, the sketch map which is a representation of the sketch information of SAR images is extracted as the criterion for designing the kernel function. Meanwhile, due to the properties of symmetric and maximal self-similarity, a modified ratio distance is proposed and used jointly with the constructed kernel function as a similarity metric. Then, under the local stationary assumption, the local maximal homogeneous region of each pixel is searched by using the region growing method with the proposed metric. Moreover, maximal likelihood rule is used within the region for the estimation of true value. From the experiments on the synthetic and real SAR images, a promising performance in terms of speckle reduction and preservation of the details is achieved by our proposed method. [ABSTRACT FROM PUBLISHER]

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature‐based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero‐day Android malware. Hence, the authors develop and analyse proactive machine‐learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static‐analytic Bayesian classification‐based solutions for detecting unknown Android malware. [ABSTRACT FROM AUTHOR]

Most well-known instructional operating systems are complex, particularly if their companion software is taken into account. It takes considerable time and effort to craft these systems, and their complexity may introduce maintenance and evolution problems. In this paper, a courseware called Awk-Linux is proposed. Awk-Linux can be crafted relatively more easily and it does not depend on any hardware simulator or platform. The basic hardware functions provided by Awk-Linux include timer interrupt and page-fault interrupt, which are simulated through program instrumentation over user programs. Course projects based on Awk-Linux provide source code extracted and simplified from a Linux kernel. Results of this study indicate that the projects helped students better to understand inner workings of operating systems. [ABSTRACT FROM AUTHOR]

In recent years, approaches to control performance and resource optimization for embedded control systems have been receiving increased attention. Most of them focus on theory, whereas practical aspects are omitted. Theoretical advances demand flexible real-time kernel support for multitasking and preemption, thus requiring more sophisticated and expensive software/hardware solutions. On the other hand, embedded control systems often have cost constraints related with mass production and strong industrial competition, thus demanding low-cost solutions. In this paper, it is shown that these conflicting demands can be softened and that a compromise solution can be reached. We advocate that recent research results on optimal resource management for control tasks can be implemented on simple multitasking preemptive real-time kernels targeting low-cost microprocessors, which can be easily built in-house and tailored to actual application needs. The experimental evaluation shows that significant control performance improvement can be achieved without increasing hardware costs. [ABSTRACT FROM PUBLISHER]

This paper presents an advanced aircraft flight control system which is capable of providing failure detection, identification, and adaptive compensation in the event of the damage of control surfaces. The new approach is based on the detection of immediate changes of correlation between pitching, yawing, and rolling rates. The failure compensation mechanism is realized by an adaptive wavelet neural net-based proportional-integral-derivative (PID) controller. The fault-tolerant mechanism is activated to regain effective control of the damaged aircraft whenever a control surface failure is detected. Design of a real-time executive kernel based on the RT-Linux operating system is experimentally conducted to realize and verify functions of our proposed design. [ABSTRACT FROM PUBLISHER]

User-level communication alleviates the software overhead of the communication subsystem by allowing applications to access the network interface directly. For that purpose, efficient address translation of virtual address to physical address is critical. In this study, we propose a system call based address translation scheme where every translation is done by the kernel instead of a translation cache on a network interface controller as in the previous cache based address translation. According to our experiments, our scheme achieves up to 4.5 % reduction in application execution time compared to the previous cache based approach [ABSTRACT FROM PUBLISHER]

Existing techniques to detect kernel-level rootkits expose some infections, but they don't identify specific attacks. This rootkit categorization approach helps system administrators identify the extent of specific infections, aiding in optimal recovery and faster reactions to future attacks. The authors present a framework to detect and classify rootkits and discuss a methodology for determining if a system has been infected by a kernel-level rootkit. Once infection is established, administrators can create new signatures for kernel-level rootkits to detect them. The authors conducted their research on a Red Hat Linux-based system, but the methodology is applicable to other Linux distributions based on the standard Linux kernel. They also believe the method can apply to other Unix- and Windows-based systems. [ABSTRACT FROM PUBLISHER]

Self-hashing has been proposed as a technique for verifying software integrity. Appealing aspects of this approach to software tamper resistance include the promise of being able to verify the integrity of software independent of the external support environment, as well as the ability to integrate code protection mechanisms automatically. In this paper, we show that the rich functionality of most modern general-purpose processors (including UltraSparc, x86, PowerPC, AMD64, Alpha, and ARM) facilitate an automated, generic attack which defeats such self-hashing. We present a general description of the attack strategy and multiple attack implementations that exploit different processor features. Each of these implementations is generic in that it can defeat self-hashing employed by any user-space program on a single platform. Together, these implementations defeat self-hashing on most modern general-purpose processors. The generality and efficiency of our attack suggests that self-hashing is not a viable strategy for high-security tamper resistance on modern computer systems. [ABSTRACT FROM PUBLISHER]

Microsoft's release of Windows Vista marks the arrival of a new era for software security. Fundamental changes have gradually occurred, bringing us to a point now where the threat landscape no longer resembles what it was just a few years ago. Vista's release is ideal to consider as a culmination point; it's from here that software attack strategies will move into new directions. In this article, the author examines some of these new directions, as well as some of the changes related to Vista that most encapsulate the current threat landscape for software security. Eight characterirstics most strongly define the new software security threat landscape. Let's take a look at them: actualization of Web vulnerability threats; advances in code analysis; more advanced techniques; client-side vulnerabilities; remote exploitation; targeted attacks; sale of vulnerability information; and anti-exploitation technology. [ABSTRACT FROM PUBLISHER]

The kernel is a fundamental piece of the operating system that provides and mediates access to a computer system's resources. Naturally, such a critical component plays a key role in providing users with a secure environment and should be subject to security practitioners' scrutiny. The author focuses on kernel security issues, recent vulnerabilities, and the emergence of publicly available exploit code for them. [ABSTRACT FROM PUBLISHER]

A novel inductive ensemble clustering method is proposed. In the proposed method, kernel support matching is applied to a co‐association matrix that aggregates arbitrary basic partitions in order to detect clusters of complicated shape. It also has the advantage of naturally detecting the number of clusters and assigning clusters for out‐of‐sample data. In the proposed method, a new similarity is learned from various clustering results of the basic partition, and a kernel support function capable of clustering learning data and test data is constructed. Experimental results demonstrated that the proposed method is effective with respect to clustering quality and has the robustness to induce clusters of out‐of‐sample data. [ABSTRACT FROM AUTHOR]