nils-herrmann/clean_scirepeval huggingface dataset:
An Improvement of Tree-Rule Firewall for a Large Network: Supporting Large Rule Size and Low Delay

Firewalls are important network devices which provide first hand defense against network threat. This level of defense is depended on firewall rules. Traditional firewalls, i.e., Cisco ACL, IPTABLES, Check Point and Juniper NetScreen firewall use listed rule to regulate packet flows. However, the listed rules may lead to rule conflictions which make the firewall to be less secure or even slowdown in performance. Based on our previous research works, we proposed the Tree-Rule firewall which does not encounter such rule conflicts within its rule set and operates faster than the traditional firewalls. However, in big or complex networks, the Tree-Rule firewall still may face two main problems. 1. Firewall administrators may face difficulty to write big and complex rule. 2. Difficulty to select appropriate attribute column for the Root node. In this paper, we propose an improved model for the Tree-Rule firewall by extending our previous models. We offer the use of combination between IN and OUT interfaces of the firewall to separate a big rule to many small independent rules. Each separated rule then can be managed in an individual screen. Sequence of verifying attributes, i.e., Source IP, Destination IP and Destination Port numbers, can be ordered independently in each separated rule. We implement the two main schemes on Linux Cent OS 6.3. We found that the improved Tree-Rule firewall can be managed easily with low processing delay.
Hybrid Tree-Rule Firewall for High Speed Data Transmission

Traditional firewalls employ listed rules in both configuration and process phases to regulate network traffic. However, configuring a firewall with listed rules may create rule conflicts, and slows down the firewall. To overcome this problem, we have proposed a Tree-rule firewall in our previous study. Although the Tree-rule firewall guarantees no conflicts within its rule set and operates faster than traditional firewalls, keeping track of the state of network connections using hashing functions incurs extra computational overhead. In order to reduce this overhead, we propose a hybrid Tree-rule firewall in this paper. This hybrid scheme takes advantages of both Tree-rule firewalls and traditional listed-rule firewalls. The GUIs of our Tree-rule firewalls are utilized to provide a means for users to create conflict-free firewall rules, which are organized in a tree structure and called ‘tree rules’. These tree rules are later converted into listed rules that share the merit of being conflict-free. Finally, in decision making, the listed rules are used to verify against packet header information. The rules which have matched with most packets are moved up to the top positions by the core firewall. The mechanism applied in this hybrid scheme can significantly improve the functional speed of a firewall.
Hexagonal Structure for Intelligent Vision

Using hexagonal grids to represent digital images have been studied for more than 40 years. Increased processing capabilities of graphic devices and recent improvements in CCD technology have made hexagonal sampling attractive for practical applications and brought new interests on this topic. The hexagonal structure is considered to be preferable to the rectangular structure due to its higher sampling efficiency, consistent connectivity and higher angular resolution and is even proved to be superior to square structure in many applications. Since there is no mature hardware for hexagonal-based image capture and display, square to hexagonal image conversion has to be done before hexagonal-based image processing. Although hexagonal image representation and storage has not yet come to a standard, experiments based on existing hexagonal coordinate systems have never ceased. In this paper, we firstly introduced general reasons that hexagonally sampled images are chosen for research. Then, typical hexagonal coordinates and addressing schemes, as well as hexagonal based image processing and applications, are fully reviewed.
Visual tracking based on weighted subspace reconstruction error

It is a challenging task to develop an effective and robust visual tracking method due to factors such as pose variation, illumination change, occlusion, and motion blur. In this paper, a novel tracking algorithm based on weighted subspace reconstruction error is proposed. We first compute the discriminative weights by sparse construction error with template dictionary consisted of positive and negative samples, and then confidence map for candidates is computed through subspace reconstruction error. Finally, the location of the target object is estimated by maximizing the decision map which is combined discriminative weights and subspace reconstruction error. Furthermore, we use the new evaluation criterion to verify the robustness of the current tracking result, which can reduce the accumulated error effectively. Experimental results on some challenging video sequences show that the proposed algorithm performs favorably against seven state-of-the-art methods in terms of accuracy and robustness.
Patch-based object tracking via Locality-constrained Linear Coding

In this paper, the Locality-constrained Linear Coding(LLC) algorithm is incorporated into the object tracking framework. Firstly, we extract local patches within a candidate and then utilize the LLC algorithm to encode these patches. Based on these codes, we exploit pyramid max pooling strategy to generate a richer feature histogram. The feature histogram which integrates holistic and part-based features can be more discriminative and representative. Besides, an occlusion handling strategy is utilized to make our tracker more robust. Finally, an efficient graph-based manifold ranking algorithm is exploited to capture the relevance between target templates and candidates. For tracking, target templates are taken as labeled nodes while target candidates are taken as unlabeled nodes, and the goal of tracking is to search for the candidate that is the most relevant to existing labeled nodes by manifold ranking algorithm. Experiments on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to other state-of-the-art baselines.
Efficient Super-Resolution by Finer Sub-Pixel Motion Prediction and Bilateral Filtering

Super-resolution reconstruction produces high-resolution images from a set of low-resolution images of the same scene. In the last two and a half decades, many super-resolution algorithms have been proposed. These algorithms are very sensitive to their assumed models of motion and noise, and computationally expensive for many practical applications. In this paper we adopt earlier reported fast prediction based sub-pixel motion estimation and a novel interpolation scheme based on the bilateral filter to produce a fast color super-resolution reconstruction that can accommodate arbitrary local motion patterns. The proposed algorithm exploits photometric proximity and available finer fractional motion information in the high resolution grid, to reconstruct enhanced super-resolved image frames. Experiments show a PSNR performance comparable to the state-of-the-art but at a fraction of their computational cost.
A skeleton algorithm on clusters for image edge detection

Image edge detection in computer vision and image processing is a process which detects one kind of significant feature in an image that appears as large delta values in intensities. In this paper, a parallel algorithmic skeleton for edge detection is proposed based on the Spiral Architecture and the Gaussian multi-scale theory. UNIX-based network programming mechanisms in C are used for the implementation on a cluster of Sun-workstations. Our work provides an efficient algorithm for edge detection and is robust to noise.
Face hallucination based on nonparametric Bayesian learning

In this paper, we propose a novel example-based face hallucination method through nonparametric Bayesian learning based on the assumption that human faces have similar local pixel structure. We cluster the low resolution (LR) face image patches by nonparametric method distance dependent Chinese Restaurant process (ddCRP) and calculate the centres of the clusters (i.e., subspaces). Then, we learn the mapping coefficients from the LR patches to high resolution (HR) patches in each subspace. Finally, the HR patches of input low resolution face image can be efficiently generated by a simple linear regression. The spatial distance constraint is employed to aid the learning of subspace centers so that every subspace will better reflect the detailed information of image patches. Experimental results show our method is efficient and promising for face hallucination.
Uniformly Partitioning Images on Virtual Hexagonal Structure

Hexagonal structure is different from the traditional square structure for image representation. The geometrical arrangement of pixels on hexagonal structure can be described in terms of a hexagonal grid. Uniformly separating image into seven similar copies with a smaller scale has commonly been used for parallel and accurate image processing on hexagonal structure. However, all the existing hardware for capturing image and for displaying image are produced based on square architecture. It has become a serious problem affecting the advanced research based on hexagonal structure. Furthermore, the current techniques used for uniform separation of images on hexagonal structure do not coincide with the rectangular shape of images. This has been an obstacle in the use of hexagonal structure for image processing. In this paper, we briefly review a newly developed virtual hexagonal structure that is scalable. Based on this virtual structure, algorithms for uniform image separation are presented. The virtual hexagonal structure retains image resolution during the process of image separation, and does not introduce distortion. Furthermore, images can be smoothly and easily transferred between the traditional square structure and the hexagonal structure while the image shape is kept in rectangle
Learning-Based Number Recognition on Spiral Architecture

In this paper, a number recognition algorithm is proposed on spiral architecture, a hexagonal image structure. This algorithm employs RULES-3 inductive learning method to recognize numbers. The algorithm starts from a collection of samples of numbers from number plates. Edge maps of the samples are then detected based on spiral architecture. A set of rules are extracted using these samples by RULES-3. The rules describe the frequencies of 9 different edge masks appearing in the samples. Each mask is a cluster of 7 hexagonal pixels. In order to recognize a number plate, all numbers are tested one by one using the extracted rules. The number recognition is achieved by counting the frequencies of the 9 masks. In this paper, a comparison between results based on rectangular structure and the results based on spiral architecture is given. From the experimental results, we can make the conclusion that Spiral Architecture is better than rectangular structure for inductive learning-based number recognition
A Stateful Mechanism for the Tree-Rule Firewall

In this paper, we propose a novel connection tracking mechanism for Tree-rule firewall which essentially organizes firewall rules in a designated Tree structure. A new firewall model based on the proposed connection tracking mechanism is then developed and extended from the basic model of Net filter's Conn Track module, which has been used by many early generation commercial and open source firewalls including IPTABLES, the most popular firewall. To reduce the consumption of memory space and processing time, our proposed model uses one node per connection instead of using two nodes as appeared in Net filter model. This can reduce memory space and processing time. In addition, we introduce an extended hash table with more hashing bits in our firewall model in order to accommodate more concurrent connections. Moreover, our model also applies sophisticated techniques (such as using static information nodes, and avoiding timer objects and memory management tasks) to improve its processing speed. Finally, we implement this model on Linux Cent OS 6.3 and evaluate its speed. The experimental results show that our model performs more efficiently in comparison with the Net filter/IPTABLES.
Fractal Image Compression on Spiral Architecture

Image compression has many applications. For example, it is an important step for distributed and network based pattern recognition. For real time object recognition or reconstruction, image compression can greatly reduce the image size, and hence increase the processing speed and enhance performance. Fractal image compression is a relatively recent image compression method. Its basic idea is to represent images as a fixed point of a contractive iterated function system (IFS). Spiral architecture (SA) is a novel image structure on which images are displayed as a collection of hexagonal pixels. The efficiency and accuracy of image processing on SA have been demonstrated in many recently published papers. We have shown the existence of contractive IFS's through the construction of a complete metric space on SA. The selection of range and domain blocks for fractal image compression is highly related to the uniform image separation specific to SA. In this paper, we review the current research work on fractal image compression based on SA. We compare the results obtained on SA and the traditional square structure in terms of compression ratio and PSNR
Automatic liver parenchyma segmentation from abdominal CT images using support vector machines

This paper presents an automatic liver parenchyma segmentation algorithm that can segment liver in abdominal CT images. There are three major steps in the proposed approach. Firstly, a texture analysis is applied to input abdominal CT images to extract pixel level features. In this step, wavelet coefficients are used as texture descriptors. Secondly, support vector machines (SVMs) are implemented to classify the data into pixel-wised liver area or non-liver area. Finally, integrated morphological operations are designed to remove noise and finally delineate the liver. Our unique contributions to liver segmentation are twofold: one is that it has been proved through experiments that wavelet features present good classification result when SVMs are used; the other is that the combination of morphological operations with the pixel-wised SVM classifier can delineate volumetric liver accurately. The algorithm can be used in an advanced computer-aided liver disease diagnosis and liver surgical planning system. Examples of applying the proposed algorithm on real CT data are presented with performance validation based on the comparison between the automatically segmented results and manually segmented ones.
Keynote addresses: Keynote address I: Interaction between computer control design and its implementation

Traditionally, real-time computer experts and control designers work separately. The first deal with the control code without paying much attention to the impact of the code execution in the control performance whereas the control engineers conceive the control algorithms based on the required performance and the process knowledge, regardless their subsequent implementation. But they are interlaced and both designs should be jointly treated, mainly if the control tasks share resources with some other activities and these resources are limited. In this talk, the real-time control design and implementation will be reviewed from both perspectives. Some key concepts interacting with both issues, the control performance and the control implementation, such as the control effort or the control kernel are emphasized and some general directions in the co-design are summarized.
Global Optimization under Nonlinear Constraints Based on Apollonius Fill

Making use of Apollonius Fill, an algorithm is presented, which is for finding solutions of global optimization problems nonlinearly constrained by a circular region in the plane. Using this algorithm, global optimum can be computed fast and precisely. We request no more than first order derivatives of objective functions for the optimization algorithm. If we do not care about the processing time taken, for any given objective function, the global optimum can be obtained as precisely as requested. The proof of convergence of this algorithm is also given in this paper. We use a few numerical examples to show that this algorithm is effective, reliable, and hence is valuable in practice.
A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis

Interconnected systems, such as Web servers, database servers, cloud computing servers and so on, are now under threads from network attackers. As one of most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on these computing systems. In this paper, we present a DoS attack detection system that uses multivariate correlation analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-area-based technique is proposed to enhance and to speed up the process of MCA. The effectiveness of our proposed detection system is evaluated using KDD Cup 99 data set, and the influences of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The results show that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy.
Secure-GLOR: An Adaptive Secure Routing Protocol for Dynamic Wireless Mesh Networks

With the dawn of a new era, digital security has become one of the most essential part of any network. Be it a physical network, virtual network or social network, the demand for secure data transmission is ever increasing. Wireless mesh networks also stand the same test of security as the legacy networks. This paper presents a secure version of the Geo-Location Oriented Routing (GLOR) protocol for wireless mesh networks, incorporating a multilevel security framework. It implements authentication using the new features of the network model and enables encryption throughout the network to provide high levels of security.
Tensor error correction for corrupted values in visual data

The multi-channel image or the video clip has the natural form of tensor. The values of the tensor can be corrupted due to noise in the acquisition process. We consider the problem of recovering a tensor L of visual data from its corrupted observations X = L + S, where the corrupted entries S are unknown and unbounded, but are assumed to be sparse. Our work is built on the recent studies about the recovery of corrupted low-rank matrix via trace norm minimization. We extend the matrix case to the tensor case by the definition of tensor trace norm in . Furthermore, the problem of tensor is formulated as a convex optimization, which is much harder than its matrix form. Thus, we develop a high quality algorithm to efficiently solve the problem. Our experiments show potential applications of our method and indicate a robust and reliable solution.
Dense feature correspondence for video-based endoscope three-dimensional motion tracking

This paper presents an improved video-based endoscope tracking approach on the basis of dense feature correspondence. Currently video-based methods often fail to track the endoscope motion due to low-quality endoscopic video images. To address such failure, we use image texture information to boost the tracking performance. A local image descriptor - DAISY is introduced to efficiently detect dense texture or feature information from endoscopic images. After these dense feature correspondence, we compute relative motion parameters between the previous and current endoscopic images in terms of epipolar geometric analysis. By initializing with the relative motion information, we perform 2-D/3-D or video-volume registration and determine the current endoscope pose information with six degrees of freedom (6DoF) position and orientation parameters. We evaluate our method on clinical datasets. Experimental results demonstrate that our proposed method outperforms state-of-the-art approaches. The tracking error was significantly reduced from 7.77 mm to 4.78 mm.
Adaptive Stick-Like Features for Human Detection Based on Multi-scale Feature Fusion Scheme

Human detection has been widely used in many applications. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as clothing, posture and etc. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel method which successfully implements the Real AdaBoost training procedure on multi-scale images. Various object features are exposed on multiple levels. To further boost the overall performance, a fusion scheme is established using scores obtained at various levels which integrates decision results with different scales to make the final decision. Unlike other score-based fusion methods, this paper re-formulates the fusion process through a supervised learning. Therefore, our fusion approach can better distinguish subtle difference between human objects and non-human objects. Furthermore, in our approach, we are able to use simpler weak features for boosting and hence alleviate the training complexity existed in most of AdaBoost training approaches. Encouraging results are obtained on a well recognized benchmark database.
Local Binary Patterns on Hexagonal Image Structure

Local binary pattern (LBP) was designed and widely used for efficient texture classification. It has been used for face recognition and has potential applications in many other research areas such as human detection. LBP provides a simple and effective way to represent patterns. Uniform LBPs play an important role for LBP-based pattern /object recognition as they include majority of LBPs. In this paper, we present LBP codes on hexagonal image structure. We show that LBPs defined on hexagonal structure have higher percentages of uniform LBPs that will lead to a more efficient and accurate recognition scheme for image classification.
Bi-lateral filtering based edge detection on hexagonal architecture

Edge detection plays an important role in image processing but is still an open problem. This paper presents a novel edge detection method based on bi-lateral filtering which achieves better performance than single Gaussian filtering. In this form of filtering, both spatial closeness and intensity similarity of pixels are considered in order to preserve important visual cues provided by edges and reduce the sharpness of transitions in intensity values as well. In addition, the edge detection method proposed in this paper is achieved on hexagonally sampled images. Due to the compact and circular nature of the hexagonal lattice, a better quality edge map is obtained on hexagonal architecture than common edge detection on square architecture. Experimental results using our proposed method in this paper exhibit encouraging performance.