Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Copy Move Forgery Detection based on double matching

Copy Move is a technique widespreadly used in digital image tampering, meaning Copy Move Forgery Detection (CMFD) is still a significant research. In this paper, a novel CMFD method is proposed, including double matching process and region localizing process. In double matching process, the first matching is conducted on Delaunay triangles consisting of Local Intensity Order Pattern (LIOP) keypoints, to find the approximate location of suspicious regions. In order to find sufficient keypoint pairs, the existing set of matching triangles is expanded by adding their neighbors iteratively, covering the whole tampered regions, and the second matching with a looser threshold is conducted on the vertices. In the region localizing process, considering the case of multiple copies, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to classify the keypoint pairs described in a new model. Experimental results indicate that the proposed method, with good robustness, outperforms some state-of-the-art methods.     

一种标准模型下无证书签名方案的安全性分析与改进

无证书签名具有基于身份密码体制和传统公钥密码体制的优点，可解决复杂的公钥证书管理和密钥托管问题.Wu和Jing提出了一种强不可伪造的无证书签名方案，其安全性不依赖于理想的随机预言机.针对该方案的安全性，提出了两类伪造攻击.分析结果表明，该方案无法实现强不可伪造性，并在"malicious-but-passive"的密钥生成中心攻击下也是不安全的.为了提升该方案的安全性，设计了一个改进的无证书签名方案.在标准模型中证明了改进的方案对于适应性选择消息攻击是强不可伪造的，还能抵抗恶意的密钥生成中心攻击.此外，改进的方案具有较低的计算开销和较短的私钥长度，可应用于区块链、车联网、无线体域网等领域.     

Copy-move forgery detection of duplicated objects using accurate PCET moments and morphological operators

This paper presents a new method for copy-move forgery detection of duplicated objects. A bounding rectangle is drawn around the detected object to form a sub-image. Morphological operator is used to remove the unnecessary small objects. Highly accurate polar complex exponential transform moments are used as features for the detected objects. Euclidian distance and correlation coefficient between the feature vectors are calculated and used for searching the similar objects. A set of 20 forged images with duplicated objects is carefully selected from previously published works. Additional 80 non-forged images are edited by the authors and forged by duplicating different kinds of objects. Numerical simulation is performed where the results show that the proposed method successfully detect different kinds of duplicated objects. The proposed method is much faster than the previously existing methods. Also, it exhibits high robustness to various attacks such as additive white Gaussian noise, JPEG compression, rotation, and scaling.     

Homogeneous and Universal Detection of Various Targets with a Dual-Step Transduced Toehold Switch Sensor

Toehold switch sensors represent a class of new advances that allow specific targets to trigger in situ expression of a protein reporter. Although they offer unique advantages of a label-free nature and high portability, they generally require repeated sequence design, high expenditure, and laborious optimization of toehold switch sequences according to different targets. To simplify the sensing process further and to improve its practicability, we innovatively introduce a dual-step pre-transduction upon traditional toehold switch sensor. Through two successive toehold-mediated strand-displacement reactions that are initiated, respectively, by a linear and an associative trigger, different DNAs, RNAs, or ligands of functional nucleic acids can be generally transduced into the input of one high-performance toehold switch sensor. This advance greatly increases the target range. Furthermore, the whole process is signal-on, homogeneous, and free of any requirements for complicated operations such as probe labeling, separation, and reconstruction of the toehold switch, being promising and practical even in portable or point-of-care assays.     

DFD-SS: Document Forgery Detection using Spectral – Spatial Features for Hyperspectral Images

In the present era of machines and edge-cutting technologies, still document frauds persist. They are done intuitively by using almost identical inks, that it becomes challenging to detect them—this demands an approach that efficiently investigates the document and leaves it intact. Hyperspectral imaging is one such a type of approach that captures the images from hundreds to thousands of spectral bands and analyzes the images through their spectral and spatial features, which is not possible by conventional imaging. Deep learning is an edge-cutting technology known for solving critical problems in various domains. Utilizing supervised learning imposes constraints on its usage in real scenarios, as the inks used in forgery are not known prior. Therefore, it is beneficial to use unsupervised learning. An unsupervised feature extraction through a Convolutional Autoencoder (CAE) followed by Logistic Regression (LR) for classification is proposed (CAE-LR). Feature extraction is evolved around spectral bands, spatial patches, and spectral-spatial patches. We inspected the impact of spectral, spatial, and spectral-spatial features by mixing inks in equal and unequal proportion using CAE-LR on the UWA writing ink hyperspectral images dataset for blue and black inks. Hyperspectral images are captured at multiple correlated spectral bands, resulting in information redundancy handled by restoring certain principal components. The proposed approach is compared with eight state-of-art approaches used by the researchers. The results depicted that by using the combination of spectral and spatial patches, the classification accuracy enhanced by 4.85% for black inks and 0.13% for blue inks compared to state-of-art results. In the present scenario, the primary area concern is to identify and detect the almost similar inks used in document forgery, are efficiently managed by the proposed approach.     

一种精确测量ASTM标准圆形拉伸试样锥度的方法

介绍了ASTM标准中圆形拉伸试样主要部位的尺寸与要求。采用国产万能工具显微镜-投影法对试样在平行长度部位的锥度进行了测量。该方法简便易行,结果准确可靠,满足了标准要求,同时发挥了老设备的作用,保证了试样的加工精度。     

Probing Ambiguous Base‐Pairs by Genetic Transformation with XNA Templates

The templating potential of anhydrohexitol oligonucleotides bearing ambiguous bases was studied in vivo, by using a selection screen for mosaic heteroduplex plasmids in Escherichia coli. 1,5‐Anhydro‐2,3‐dideoxy‐2‐(5‐nitroindazol‐1‐yl)‐D ‐arabino‐hexitol showed the greatest ambiguity among the three nucleosides tested. At most two successive ambiguous bases could be tolerated on hexitol templates read in bacterial cells. Hexitol nucleosides bearing simplified heterocycles thus stand as promising monomers for generating random DNA sequences in vivo from defined synthetic oligonucleotides.     

申遗背景下的中国自然遗产潜力评估

参照世界自然遗产地学、美学、生物生态过程和濒危物种栖息地4项申遗评选标准,依据地貌景观、生物景观等价值构成,建立美学价值评估方法;依据世界遗产13类地学主题评价框架、地质演化过程、典型类型地貌等价值构成,建立地学价值评估方法;依据生态系统过程、物种多样性、栖息地等价值构成,建立生物生态学价值评估方法。利用全球对比的方法提取和评估突出普遍价值,将中国自然遗产潜力区分为青藏高原区、西北干旱区、东部季风区等生态环境基本单元,筛选我国世界自然遗产潜力区。重点提出海洋类世界自然遗产和西部北部地区申遗的可能性,提出中国申报世界自然遗产突出普遍价值的评估策略与优先原则。