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。     

The 3-dimensional morphology of dendrite during equiaxed solidification of an Al-5 wt.% Cu alloy

In a sample quenched during equiaxed solidification of an Al-5 wt.% Cu alloy, the multi-scales 3-dimensional morphology of equiaxed dendrite was observed. The slim primary stem and secondary branches constitute the frame of dendrite, and rows of dense tertiary branches further divide the 3-dimensional space. In the divided space, the quartic branches grow further. The dendritic branches,which are perpendicular to each other, can change their growth directions and coalesce into a whole. In the tertiary branches and quartic branches, the formation of double branch structures is induced by competitive growth. The branch that wins in the competitive growth will produce a cabbage-like structure by wrapping the failed branches. In addition, the side branch can also wrap the original parent branch to produce cabbage-like structures. Depending on the historical growth direction, the dendritic arms can form vein-like and spicate structures, and the shapes of single dendritic arm may be the cylinder, plate and trapezoid platform. According to the compositions and etching morphology, the single dendritic arm in the final solidification structures should coalesce from a fine porous structure. The porous structures at different length-scales are principally induced by the preferential growth. Based on 3-dimensional morphology of equiaxed dendrite, a new research object for the investigation of microsegregation was suggested.     

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.     

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.     

Historical narratives as strategic resources: analysis of the Turkish international contracting sector

The Turkish contracting sector has an enviable reputation for operating in high-risk international markets. The sector’s ability to respond to market instability could meaningfully be construed as sectoral-level capability. We aim to demonstrate how history can be mobilised in a formalised “strategy text” to create a strategic narrative on the sectoral level. The Geography of Contractors as published by the Turkish Contractors Association (TCA) ostensively portrays the strategic development of Turkish international contractors over four decades. Such quasi-historical narratives are routinely mobilised for the purposes of creating a shared memory on the sectoral level. The chosen strategy text draws from multiple narrative fragments derived from past experience to generate a strategic agenda for the future. The representation given to multiple voices reflects the pluralistic nature of strategy making praxis. The overarching strategy narrative reflects a performative intent in legitimising some practices whilst discrediting others. Narrative analysis demonstrates the way in which actors, actions and events are positioned within a plot structure, with direct implications for the enactment of future strategic practices. The findings suggest that strategic actions can only ever be identified in retrospect and that such arguments are always made with an eye on the future.     

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method. In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.      

六盘水市煤炭行业竞争战略分析

我国是富煤贫油少气的国家，煤炭是我国最重要的一次能源。但从能源激烈的竞争市场环境而言，当前我国煤炭行业发展出现了困境。为了更好地发展六盘水地区的煤炭行业，结合竞争战略理论和六盘水市的煤炭行业发展状况，运用PEST分析法和波特五力模型对六盘水市煤炭行业进行分析，得出整合资源，结合自身优势大力吸引人才，最终达到产业转型的生态化煤炭发展战略模型。分析结果可为提高六盘水煤炭行业竞争力提供理论支持。