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.     

环境设计专业学习空间设计探析--以教室改造的参与式设计为例

学习空间的设计必须基于教育理念、学习模式、技术设施与精神氛围等因素展开,以更开放、灵活及多样化的有趣形态来吸引使用者。对于已掌握基本设计方法并具有一定设计思维的环境设计专业学生而言,设计与自身学习行为关联的学习空间,能体现"参与式设计"的特殊意义:即一方面以有着直观体验感受的熟悉空间作为训练案例来有效地整合设计能力;另一方面则通过用户与设计者身份的叠加,从使用者体验一维到对空间发展的专业预设考虑一维共同实现学习空间设计理念的更新。     

3D and simplified pseudo-2D modeling of single cell of a high temperature solid oxide fuel cell to be used for online control strategies

In the current study, a single cell of a planar SOFC is firstly modeled in 3D using commercial SOFC module of ANSYS Fluent and the results are validated against the experimental investigations in the literature. Many researchers have used ANSYS Fluent for simulating solid oxide fuel cells. However, there is a huge gap in the literature on explaining the detailed procedure that should be followed in order to use this software effectively. A thorough step-by-step approach is presented to provide a deep insight into the software. Thereafter, a simplified quasi-2D method with infinitely shorter computational time is developed and the results are compared with the 3D model. It is found that the reduced model is capable of being utilized as an alternate method for both online diagnosis and designing active control strategies.     

Capitalizing on the Koran to fuel online violent radicalization: A taxonomy of Koranic references in ISIS’s Dabiq

The current study set out to investigate to what extent ISIS is bolstering its jihadist ideology on a ‘cut-and-paste’ or ‘cherry-picked’ version of Islam in their renowned online propaganda magazine Dabiq. The main objective was to examine in a systematic and quantitative way to what extent ISIS utilizes the Koran in an atomistic, truncated and tailored manner to bolster its religious legitimacy. A total of 15 issues of Dabiq and 700 Koranic references were scrutinized. By means of a quantitative analysis we developed an innovative taxonomy of Koranic chapters and verses (i.e. surahs and ayat, respectively) on the basis of their appearance in Dabiq. Our large-scale data analysis provide consistent empirical evidence for severe decontextualization practices of the Koran in three ways: (1) a thin, Medinan-dominated religious layer, (2) ayah mutilation, and (3) clustered versus exclusive mentions. Limitations and implications for future research, policy makers and CVE initiatives are discussed.     

Common due date assignment scheduling for a no-wait flowshop with convex resource allocation and learning effect

This article addresses the no-wait flowshop scheduling problem with simultaneous consideration of common due date assignment, convex resource allocation and learning effect in a two machine setting. The processing time of each job can be controlled by its position in a sequence and also by allocating extra resource, which is a convex function of the amount of a common continuously divisible resource allocated to the job. The objective is to determine the optimal common due date, the resource allocation and the schedule of jobs such that the total earliness, tardiness and common due date cost (the total resource consumption cost) are minimized under the constraint condition that the total resource consumption cost (the total earliness, tardiness and common due date cost) is limited. Polynomial time algorithms are developed for two versions of the problem.     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

在线粒度分析仪的工业应用

催化裂化催化剂应具有良好的颗粒分布,以保证良好的流化状态。一般要求催化剂颗粒<40μm的不大于25%,40～111μm不小于50%,>111μm的不大于30%。目前催化裂化催化剂生产过程中通常采用压力式喷雾干燥成型工艺,用高压泵把制备好的胶体在与热空气的接触中,迅速脱去水分汽化,即得到粉状或颗粒状干燥产品。现有生产装置在生产过程中,因分析结果滞后,无法及时指导装置调整催化剂成品的粒度分布,特引进使用了在线粒度分析仪。通过对该分析仪的简单研究,介绍了该分析仪的使用现状,并对使用中出现的问题进行了简单总结。     

基于特征选择和深度信念网络的文本情感分类算法

由于人类语言的复杂性，文本情感分类算法大多都存在因为冗余而造成的词汇量过大的问题。深度信念网络（DBN）通过学习输入语料中的有用信息以及它的几个隐藏层来解决这个问题。然而对于大型应用程序来说，DBN是一个耗时且计算代价昂贵的算法。针对这个问题，提出了一种半监督的情感分类算法，即基于特征选择和深度信念网络的文本情感分类算法（FSDBN）。首先使用特征选择方法（文档频率（DF）、信息增益（IG）、卡方统计（CHI）、互信息（MI））过滤掉一些不相关的特征从而使词汇表的复杂性降低；然后将特征选择的结果输入到DBN中，使得DBN的学习阶段更加高效。将所提算法应用到中文以及维吾尔语中，实验结果表明在酒店评论数据集上，FSDBN在准确率方面比DBN提高了1.6%，在训练时间上比DBN缩短一半。     

Emotion recognition at the edge with AI specific low power architectures

Nowadays Deep Learning is applied in almost every research field and helps getting amazing results in a great number of challenging tasks. The main problem is that this kind of learning and consequently Neural Networks that can be defined deep, are resource intensive. They need specialized hardware to perform computation in a reasonable time. Many tasks are mandatory to be as much real-time as possible . It is needed to optimize many components such as code, algorithms, numeric accuracy and hardware, to make them “efficient and usable”. All these optimizations can help us to produce incredibly accurate and fast learning models. The paper reports a study in this direction for the challenging face detection and emotion recognition tasks.     

Value drivers of blockchain technology: A case study of blockchain-enabled online community

There is growing recognition that blockchain technology has significant potential to alter how organizations and people work and communicate. However, theoretical guidance concerning how organizations leverage blockchain technology to enhance value creation for users is still limited. Grounded in the socio-technical perspective and leveraging the rich data obtained from case analyses of blockchain-enabled online communities, this paper develops a theoretical model to identify the core value drivers that blockchain enables for online communities. The core value drivers include: a reputation-value system, data ownership mechanisms, and verification & tracking mechanisms. Our findings suggest that these three value drivers enhance value creation of online communities by motivating participation and protecting contributions.