MediaWiki Research Articles

Blurred Detail Enhancement Algorithm for Motion Video Image Based on Computer Network

In this work, a blurred detail enhancement algorithm is proposed to improve the feature recognition ability of motion video image based on computer network. The edge contour features of motion video images are decomposed using multi-scale Retinex algorithm, and the image recognition technology based on computer network is used to simulate the human visual features to enhance and repair the overlay or blurred features contained in the images. The feature extraction and feature focusing of blurred detail of motion video image are realized by computer vision template feature matching method, respectively. On this basis, the enhancement effect of blurred detail image is achieved. The proposed method is proved to be able to can improve the output peak signal to noise ratio (PSNR) the blur detail quality, and the recognition ability of motion video images.

3-D Feature Matching for Point Cloud Object Extraction

Effective object extraction plays an important role in many point cloud-based applications. This letter proposes a 3-D feature matching framework for point cloud object extraction. To determine the optimal affine transformation parameters for each template feature point, a convex dissimilarity function and the locally affine-invariant geometric constraints are designed to construct the overall objective function. The 3-D feature matching framework is integrated into a point cloud object extraction workflow. Extraction results on six test data sets show that average completeness, correctness, quality, and $F_{1}$ -measure of 0.96, 0.97, 0.93, and 0.96, respectively, are obtained in extracting light poles, vehicles, and palm trees. Comparative studies also confirm that the proposed method performs effectively and robustly, and exhibits superior or compatible performance over the other compared methods.

Historical document layout analysis using anisotropic diffusion and geometric features

There are several digital libraries worldwide which maintain valuable historical manuscripts. Usually, digital copies of these manuscripts are offered to researchers and readers in raster-image format. These images carry several document degradations that may hinder automatic information retrieval solutions such as manuscript indexing, categorization, retrieval by content, etc. In this paper, we propose a learning-free and hybrid document layout analysis for handwritten historical manuscripts. It has two main phases: page characterization and segmentation. First, the proposed method locates main-content initially using top-down whitespace analysis. It employs anisotropic diffusion filtering to find whitespaces. Then, it extracts template features representing manuscripts’ authors writing behavior. After that, moving windows are used to scan the manuscript page and define main-content boundaries more precisely. We evaluated the proposed method on two datasets: One set is publicly available with 38 historical manuscript pages, and the other set of 51 historical manuscript pages that are collected from the online Harvard Library. Experiments on both datasets show promising results in terms of segmentation quality of main-content that reaches up to 98.5% success rate.

Розробка методів керування доступом до інформації у WIKI-ресурсах

The prospects and scope of Wiki technologies are considered, the structure of the content re presentation and MediaWiki software are analyzed. Considerable attention is given to architecture of MediaWiki and the components of this architecture. The problem deal with the need to access management to the content of Wiki-resources in accordance with the specifics of the information contained in such resources is identified. The results of the analysis show that the basic tools of MediaWiki are not capable to allow a satisfactory solution to this problem. Therefore, we need to create specialized software that is based on the content classification with the use of separate namespaces, categories, templates and semantic properties extracted from MediaWiki. Such software has to be independent of the MediaWiki core and be based on programming of content analysis at the skin level. We test the proposed solution in the process of development of the portal version of the Great Ukrainian Encyclopedia (e-vue) with use of knowledge regarding the knowledge base structure of this portal. Prom ble s in programming 2020; 1: 33-46

Cybersecurity Named Entity Recognition Using Multi-Modal Ensemble Learning

Cybersecurity named entity recognition is an important part of threat information extraction from large-scale unstructured text collection in many cybersecurity applications. Most existing security entity recognition studies and systems use regular matching strategy or machine learning algorithms. Due to the peculiarity and complexity of security named entity, these models ignore the characteristic of security data and the correlation of entities. Therefore, through the in-depth study of security entity characteristic, we propose a novel security named entity recognition model based on regular expressions and known-entity dictionary as well as conditional random fields (CRF) combined with four feature templates. This model is named RDF-CRF. The rule-based expressions can match security entities with good accuracy in simpler situations, the known-entity dictionary can extract common and specific security entity, and the CRF-based extractor leverages the identified entities by rule-based and dictionary-based extractors to further improve the recognition performance. In order to demonstrate the effectiveness of our proposed model, extensive experiments are performed on a security text dataset collected from public security webs. The experimental results show that can achieve better performance than state-of-the-art methods.

Nested context-aware sanitisation and feature injection in clustered templates of JavaScript worms on the cloud-based OSN

This article presents an enhanced JavaScript feature-injection based framework that obstructs the execution of cross-site scripting (XSS) worms from the virtual machines of cloud-based online social network (OSN). It calculates the features of clustered-sanitised compressed templates of JavaScript attack vectors embedded in the HTTP response messages. Any variation observed in such JavaScript feature set indicates the injection of XSS worms on the cloud-based OSN server. The injected worms will further undergo through the process of nested context-aware sanitisation for its safe interpretation on the web browser. The prototype of our framework was developed in Java and installed in the virtual machines of cloud environment. The experimental evaluation of our framework was performed on the platform of OSN-based web applications deployed in the cloud platform. The performance analysis done revealed that our framework detects the injection of malicious JavaScript code with low false negative rate and acceptable performance overhead.

The effect of sulphuric acid and oxalic acid on the formation of anodic aluminium oxide templates using two-step anodization method

The prevention of corrosion on the surface of aluminum by formation of a thin coating layer using anodization methods for enhanced applications is an active area of research. In this study, the fabrication of nanoporous anodic aluminum oxide (AAO) templates was performed by a two-step anodization process using aluminum with 99.98% purity in 0.3 M solution of oxalic acid or 0.1, 0.3, or 0.5 M solutions of sulfuric acid as electrolyte. Anodizing time, electrolyte solution and concentration, and current were found to be the main factors determining the pore diameter and interpore size. In addition, the voltage affected the number of pores on the AAO template. The structural features of the nanoporous templates were examined by scanning electron microscopy. The optimum conditions were determined to be 0.3 M sulfuric acid at 25 V.

Nested context-aware sanitisation and feature injection in clustered templates of JavaScript worms on the cloud-based OSN

This article presents an enhanced JavaScript feature-injection based framework that obstructs the execution of cross-site scripting (XSS) worms from the virtual machines of cloud-based online social network (OSN). It calculates the features of clustered-sanitised compressed templates of JavaScript attack vectors embedded in the HTTP response messages. Any variation observed in such JavaScript feature set indicates the injection of XSS worms on the cloud-based OSN server. The injected worms will further undergo through the process of nested context-aware sanitisation for its safe interpretation on the web browser. The prototype of our framework was developed in Java and installed in the virtual machines of cloud environment. The experimental evaluation of our framework was performed on the platform of OSN-based web applications deployed in the cloud platform. The performance analysis done revealed that our framework detects the injection of malicious JavaScript code with low false negative rate and acceptable performance overhead.

A Community Action Toolkit: A Roadmap for Using Environmental Rights to Fight Pollution

Pollution is the leading cause of disease, disability and premature death around the world. But its impacts are not evenly distributed. Poor and marginalized communities often bear the brunt of the environmental, health, and socioeconomic impacts, especially in middle- and low-income countries. Far removed from powerful decision-makers, these communities face multiple barriers in their efforts to fight pollution. Many are unaware of their rights to access pollution information or participate in policymaking decisions, and often lack the advocacy skills needed to form strategic partnerships and effectively engage government officials. Access to information, public participation and justice are environmental rights that not only are fundamental to good environmental governance, but also offer an important tool for improving the development, implementation and enforcement of pollution laws. Strategically applying these rights can enable civil society and local communities to evaluate the environmental and social justice aspects of pollution, demand better compliance with laws and help build a pollution accountability movement. Developed in partnership with more than a dozen civil society organizations across six countries, this toolkit offers practical guidance on how to use environmental rights to fight air, water and solid waste pollution. Designed to support civil society, local community activities and those concerned about pollution, the toolkit enables everyone, everywhere to conduct policy research, collect and use pollution information in decision-making processes, form coalitions and develop advocacy campaigns. Its eight modules feature important concepts, research indicators, worksheets and templates, which can be modified to fit a country’s context and downloaded to complete as a workbook. From Thailand to Morocco to Jamaica, civil society organizations and local communities have already used this toolkit to strengthen their right to a clean, healthy and safe environment. In Indonesia, for example, the Ministry of Environment and Forestry passed a new List of Public Information regulation that required over 111 documents, maps, and reports to be made proactively available. And in Morocco, there is growing civil society engagement with government officials over a newly passed Right to Information law. Communities around the world can follow in their footsteps and use this toolkit to protect lives, livelihoods and the environment from pollution.

A Novel Cancelable FaceHashing Technique Based on Non-Invertible Transformation With Encryption and Decryption Template

A novel cancelable FaceHashing technique based on non-invertible transformation with encryption and decryption template has been proposed in this paper. The proposed system has four components: face preprocessing, feature extraction, cancelable feature extraction followed by the classification, and encryption/decryption of cancelable face feature templates. During face preprocessing, the facial region of interest has been extracted out to speed the process for evaluating discriminant features. In feature extraction, some optimization techniques such as Sparse Representation Coding, Coordinate descent, and Block coordinates descent have been employed on facial descriptors to obtain the best representative of those descriptors. The representative descriptors are further arranged in a spatial pyramid matching structure to extract more discriminant and distinctive feature vectors. To preserve them, the existing BioHashing technique has been modified and extended to some higher levels of security attacks and the modified BioHashing technique computes a cancelable feature vector by the combined effect of the facial feature vector and the assigned token correspond to each user. The elements of computed cancelable feature vector are in a numeric form that has been employed to perform both verifications as well as identification task in online while the original facial feature vectors are kept offline either in hard drive or disc. Then, to enhance more security levels and also to preserve the cancelable face features, an RSA based encryption-decryption algorithm has been introduced. The proposed system has been tested using four benchmark face databases: CASIA-FACE-v5, IITK, CVL, and FERET, and performance are obtained as correct recognition rate and equal error rate. The performance are compared to the state-of-the-art methods for the superiority of the proposed feature extraction technique and individual performance analysis has been performed at all the security levels of the proposed Cancelable FaceHashing Technique. These comparisons show the superiority of the proposed system.

Dürrenmatt'ın “Şüphe” Adlı Polisiye Romanında Kurgu ve Üslup

İlk örnekleri 19. yüzyılda görülen polisiye romanların ortaya çıkışıyla birlikte günlük hayattan gerçeklerin ve toplumsal olayların polisiye romanlarda işlendiği görülmektedir. Polisiye romanlar hızlı tüketilmeleri ve genelde yazarlarının edebî kaygı gütmeyişi gibi sebeplerle yığın edebiyatı altında değerlendirilmiştir. Böyle olsa da okurun polisiye romandaki aktif katılımı, başından sonuna içinde yer aldığı kovalamaca ve sonunda suçlunun adalete teslim edilmesi, okura belki de diğer romanlarda bulamadığı bir okuma zevki verdiğinden, polisiye romanların popülerliği 21. Yüzyılda bile devam etmektedir. Önemli bir edebiyatçı ve oyun yazarı olan Friedrich Dürrenmatt da maddi kaygılar nedeniyle polisiye roman yazmıştır. Dürrenmatt, yazdığı polisiyelerle türün şablon özelliklerinin sınırlarını aşmış ve türe yeni bir soluk getirmiştir. Polisiyelerinde kullandığı üslup da eserlerinin yığın edebiyatı altında yer alan polisiye romanlardan farklılaşmasını sağlamıştır. Bu çalışmada, Dürrenmatt'ın “Şüphe” (1951) adlı polisiye romanı iki aşamada incelenmiştir. Birinci inceleme aşamasında söz konusu roman, klâsik polisiye anlatıların kurgusal özelliklerine göre incelenmiş ve bu özelliklerle örtüşen ya da onlardan ayrılan yerleri gösterilmiştir. İkinci aşamada ise roman edebî metinlerde üslubu belirleyen unsurlara göre ele alınmıştır. Sonuçta Dürrenmatt'ın “Şüphe” adlı polisiye romanının klâsik polisiye anlatıların kurgusal özelliklerinden farklılaştığı ve taşıdığı üslup özellikleriyle yığın edebiyatı altında değerlendirilemeyeceği gösterilmeye çalışılmıştır.

Macrocyclic Diacetylene–Terthiophene Cocrystal: Molecular Self-Assembly, Topochemical Polymerization, and Energy Transfer

The macrocyclic structure offers direction-control self-assembly to generate columnar supramolecular architectures with a guest molecule-accessible interior cavity. Owing to the π–π stacking characteristic and photopolymerizable feature of the diacetylenic template, macrocyclic diacetylenes (MCDAs) have emerged as an intriguing molecular design for constructing arrays of covalently connected nanochannels. The energy transfer mechanism by engineering host–guest cocrystals of self-assembling π-electron-rich motifs has been widely accepted for devising organic electronics. A highly fluorescent and polymerizable cocrystal of macrocyclic diacetylene-terthiophene (MCDA-3T) was constructed. Single-crystal X-ray diffraction analysis reveals well-ordered columnar assembly of MCDA with stacking geometry close-to-ideal packing parameters preferred for the topochemical polymerization. Inspection of the extended crystal packing pattern and by elemental analysis confirms the inclusion of the 3T guest in outer-space pos...

The Kazakh terminology recognition system in the IT field

The design and development of the Kazakh language terminology recognition platform in IT field is one of the important means for more effective use of term resources in the field. Used a conditional random field (CRF) and manual modification method in the platform and based on the characteristics of the Kazakh language itself, this paper analyzes the rules of terminology formation and related terminology identification methods as well as describes the basic framework of the term recognition platform and the workflow and main functions of the training corpus, terminology recognition, feature template and term extraction in the system.

Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

With a large number of video surveillance systems installed for the requirement from industrial security, the task of object tracking, which aims to locate objects of interest in videos, is very important. Although numerous tracking algorithms for RGB videos have been developed in the decade, the tracking performance and robustness of these systems may be degraded dramatically when the information from RGB video is unreliable (e.g., poor illumination conditions or very low resolution). To address this issue, this paper presents a new tracking system, which aims to combine the information from RGB and infrared modalities for object tracking. The proposed tracking systems is based on our proposed machine learning model. Particularly, the learning model can alleviate the modality discrepancy issue under the proposed modality consistency constraint from both representation patterns and discriminability, and generate discriminative feature templates for collaborative representations and discrimination in heterogeneous modalities. Experiments on a variety of challenging RGB-infrared videos demonstrate the effectiveness of the proposed algorithm.

Human Action Recognition using Rule based Fuzzy Motion Feature Templates

This paper proposes a technique for human activity recognition in a video stream. To achieve high accuracy in activity recognition results, the method in its initial step deploys temporal template matching to recognize activities. As temporal templates are susceptible to get affected by speed, style and performance pattern of activity, so it becomes difficult to accurately differentiate among closely similar activities (e.g walking, running and jogging). The confusion in recognizing activities is reconciled by subsequent rule based activity distinction. The proposed method recognizes the human activities in video on various bench-marked data sets including KTH Dataset and Weizmann Dataset. Experimental results demonstrate the novelty of method with a wide spectrum of varied conditions. The average accuracy of the method is 97.20% under standard conditions.

Using XGBoost to Discover Infected Hosts Based on HTTP Traffic

In recent years, the number of malware and infected hosts has increased exponentially, which causes great losses to governments, enterprises, and individuals. However, traditional technologies are difficult to timely detect malware that has been deformed, confused, or modified since they usually detect hosts before being infected by malware. Host detection during malware infection can make up for their deficiency. Moreover, the infected host usually sends a connection request to the command and control (C&C) server using the HTTP protocol, which generates malicious external traffic. Thus, if the host is found to have malicious external traffic, the host may be a host infected by malware. Based on the background, this paper uses HTTP traffic combined with eXtreme Gradient Boosting (XGBoost) algorithm to detect infected hosts in order to improve detection efficiency and accuracy. The proposed approach uses a template automatic generation algorithm to generate feature templates for HTTP headers and uses XGBoost algorithm to distinguish between malicious traffic and normal traffic. We conduct a performance analysis to demonstrate that our approach is efficient using dataset, which includes malware traffic from MALWARE-TRAFFIC-ANALYSIS.NET and normal traffic from UNSW-NB 15. Experimental results show that the detection speed is about 1859 HTTP traffic per second, and the detection accuracy reaches 98.72%, and the false positive rate is less than 1%.

Machine vision gait-based biometric cryptosystem using a fuzzy commitment scheme

In this paper, a fuzzy commitment scheme is applied with a machine vision gait-based biometric system to enhance system security. The proposed biometric cryptosystem has two phases: enrolment and verification. Each of them comprises three main stages: feature extraction, reliable components extraction, and fuzzy commitment scheme. Gait features are extracted from gait images using local ternary pattern (LTP), and then, the average of one complete gait cycle using the gait energy image (GEl) concept is calculated. The average images are joined using a 2D joint histogram, which is reduced using principal component analysis (PCA) to produce the final feature vector. To enhance the robustness of the system, only highly robust and reliable bits from the feature vector are extracted. Finally, the fuzzy commitment scheme is used to secure feature templates. Bose–Chaudhuri–Hocquenghem codes (BCH) are used for key encoding in the enrolment phase and for decoding in the verification phase. The proposed system is tested using the CMU MoBo and CASIA A databases. The experimental results show that the best error rate for the CMU MoBo database is obtained when using a fast walk for enrolment and verification, where we obtain 0% for the false acceptance rate (FAR) and 0% for the false rejection rate (FRR) for a key length equal to 50 bits. The best error rate for CASIA A dataset is obtained when using the 45-degree direction to the image plane view for enrolment and verification, where we obtain 0% for the false acceptance rate (FAR) and 0% for the false rejection rate (FRR) for a key length equal to 45 bits.

Robust template matching with angle location using dynamic feature pairs updating

This work proposes a novel method for template matching in the wild. Different from the previous methods to search the matching position, our method obtains further ability on the angle location by dynamically updating corresponding feature pairs and rigid transformation parameters, which result in mutual enhancement of both feature extraction and template location. We propose a robust objective function with a valid feature selection for template matching against to noise disturbance, background changing, object deformation and partial occlusion. A hierarchical search strategy is used by adjusting the size of feature patch to improve the matching effectiveness. In addition, we extend the proposed method to match image sequences. It is beneficial to propagate reasonable feature pairs to a sequential object as initialization, recalling a stable tracking result. Extensive experiments are tested on the public database with challenging images and sequences. Experimental results demonstrate the merits of the proposed method compared to some state-of-the-art matching methods.

Real-Time Object Tracking with Template Tracking and Foreground Detection Network.

In this paper, we propose a fast and accurate deep network-based object tracking method, which combines feature representation, template tracking and foreground detection into a single framework for robust tracking. The proposed framework consists of a backbone network, which feeds into two parallel networks, TmpNet for template tracking and FgNet for foreground detection. The backbone network is a pre-trained modified VGG network, in which a few parameters need to be fine-tuned for adapting to the tracked object. FgNet is a fully convolutional network to distinguish the foreground from background in a pixel-to-pixel manner. The parameter in TmpNet is the learned channel-wise target template, which initializes in the first frame and performs fast template tracking in the test frames. To enable each component to work closely with each other, we use a multi-task loss to end-to-end train the proposed framework. In online tracking, we combine the score maps from TmpNet and FgNet to find the optimal tracking results. Experimental results on object tracking benchmarks demonstrate that our approach achieves favorable tracking accuracy against the state-of-the-art trackers while running at a real-time speed of 38 fps.

A new spatio-temporal patch-based feature template for effective gait recognition

Recognizing gait of people has been of great interest to the researchers of biometrics in the last decade. The robust features have been recently developed to identify human’s gait under different conditions. But developing efficient gait template preserving spatio-temporal features of walking is still an open problem. To address this issue, we develop a patch-based feature that can describe rhythm of walking under covariate factors properly. In our method, a new gait signature (i.e. set of spatio-temporal features) is computed from distribution of local patches in a sequence. The given signature has been used to adjust the weights of spatio-temporal coordinates and the corresponding weights are concatenated with the Gabor features. As a result, a new augmented template called Patch Gait Feature (PGF) is derived accordingly. In addition, to verify how our feature template is efficient in gait recognition, we apply two common classification methods (PCA + LDA and Random Subspace Method (RSM)) separately and evaluate the results under different challenging conditions. The recognition rate on the USF dataset indicates Rank1/Rank5 accuracies of 61.59/80.67% with PCA + LDA and 76.01/86.59% with RSM and shows an improvement of about 5% with rational computational complexity compared with other related methods.