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.     

安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。     

The influence of textese on adolescents’ perceptions of text message writers

Text messages are characterised by a casual language style, ‘textese’ (e.g., c u on thurs). This study investigated adolescents’ perceptions of the use of different levels of textese in digital messages which varied in their intended recipient (friend, teacher). Grade 8 students in Australia (N = 90, aged 13–14 years) each read six text messages purportedly written by a fellow student, and rated the extent to which they agreed (on a 7-point Likert scale) that the message writer was intelligent, paid attention to detail, used an appropriate writing style, and was friendly. Overall, participants rated message senders most favourably on all measures when they used no textese, and least favourably when they used high levels of textese. This pattern was even stronger when messages were addressed to teachers rather than same-aged friends. The findings suggest that adolescents are sensitive to both writing style and recipient when considering digital messages.     

From enfant terrible to problem-solver? Tracing the competing discourse to explain blockchain-related technological diffusion

Since the inception of blockchain-related technologies over a decade ago, investors’ uptake of the technologies has grown rapidly. But even with the advancement in standing from conceptual beginnings to real-world experimentation, mainstream adoption of the technologies in either organizational or individual contexts has yet to eventuate. To resolve this paradox, we examine progression of the technologies’ diffusion instead, and in the process, seek to uncover the underlying dynamics of sentiments at play. Diffusion involves multiple actions by different parties, with social discourses via diverse media playing an essential role. In demonstrating the interplay of support to resistance via media framing and the diffusion of innovation theory, findings from our paper contribute to the technology adoption literature. Understanding resistant behavior toward a new technology area could help resolve potential issues that may arise. This would hopefully lead to better technological implementation outcomes in future.     

Convolutional neural network: Deep learning-based classification of building quality problems

The rapid development of the construction industry in China has introduced unprecedented quality-related problems in the country’s building industry. In response to this issue, the government has established various complaint channels to report quality problems. Therefore, building quality complaints (BQCs) need to be classified and solved by respective agencies or departments rapidly for avoiding adverse impact on the safety, health, and well-being of people. However, the current process of classifying BQCs is labor intensive, time consuming, and error prone. An automatic complaint classification is required to improve the effectiveness and efficiency of complaint handling, but studies on this issue are limited. Prevailing text classification research in construction has focused on utilizing conventional shallow machine learning. By contrast, this study explores a novel convolutional neural network (CNN)-based approach that incorporates a deep-learning method to automatically classify the short texts contained within BQCs. The presented approach enables capturing the semantic features in BQC texts and automatic classification of the BQCs into predefined categories. After the model optimization, tests are conducted to examine the practical application of the text classification approach compared with Bayes-based and support vector machine classifiers. Results indicate that the developed CNN-based approach performs well in the Chinese BQC classification with limited manual intervention and few complicated feature engineering.     

Naturalistic data collection of head posture during smartphone use

Association between smartphone use and head-down tilt posture has not yet been quantitatively evaluated in natural settings. This study aimed to objectively assess the angle and duration of head-down tilt posture of smartphone users during a typical working day via naturalistic data collection. Thirty-one college students conducted their typical school activities while their head posture and smartphone-app usage records were collected simultaneously for 8?hours. Participants spent 125.9?minutes (median usage duration) on their smartphones with significantly larger head-down tilt (p?<?.05) than when they were not using the phone. Head tilt angle greater than 30° was found to be more common when using the phone, while head tilt less than 20° was more common when they were not using the phone. Study findings provide empirical evidence that supports an association between the duration of smartphone use and the intensity of head-down tilt posture.

Practitioner Summary: Head postures of young smartphone users were quantified for 8?hours continuously during a typical workday using a wearable sensor. Participants spent more time in larger head-down tilt postures (greater than 30°) when they were using their smartphones as compared to when they were not using them.     

Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes

Numerical simulation techniques such as Finite Element Analyses are essential in today's engineering design practices. However, comprehensive knowledge is required for the setup of reliable simulations to verify strength and further product properties. Due to limited capacities, design-accompanying simulations are performed too rarely by experienced simulation engineers. Therefore, product models are not sufficiently verified or the simulations lead to wrong design decisions, if they are applied by less experienced users. This results in belated redesigns of already detailed product models and to highly cost- and time-intensive iterations in product development.Thus, in order to support less experienced simulation users in setting up reliable Finite Element Analyses, a novel ontology-based approach is presented. The knowledge management tools developed on the basis of this approach allow an automated acquisition and target-oriented provision of necessary simulation knowledge. This knowledge is acquired from existing simulation models and text-based documentations from previous product developments by Text and Data Mining. By offering support to less experienced simulation users, the presented approach may finally lead to a more efficient and extensive application of reliable FEA in product development.     

Text legibility for projected Augmented Reality on industrial workbenches

Augmented Reality is a promising technology for the product lifecycle development, but it is still not established in industrial facilities. The most relevant issues to be addressed relate to the ergonomics: avoid the discomfort of Head-Worn Displays, allow the operators to have free hands and improve data visualization. In this work we study the possibility to use projection-based Augmented Reality (projected AR), as optimal solution for technical visualization on industrial workbenches. In particular, text legibility in projected AR is difficult to optimize since it is affected by many parameters: environment conditions, text style, material and shape of the target surface. This problem is poorly addressed in literature and in the specific industrial field. We analyze the legibility of a set of colors prescribed by international standards for the industrial environments, on six widely used industrial workbenches surfaces. We compared the performance of 14 subjects using projected AR, with that using a traditional LCD monitor. We collected about 2500 measurements (times and errors) through the use of a test application, followed by qualitative interviews. The results showed that, as regards legibility, projected AR can be used in place of traditional monitors in most of the cases. Another not trivial finding is that the influence on legibility of surface irregularities (e.g., grooves, prominences) is more important than that of surface texturization. A possible limitation for the use of projected AR is given by the blue color, whose performance turned out to be lower than that of other colors with every workbench surface.     

Visual and manual loadings with QWERTY-like ambiguous keyboards: Relevance of letter-key assignments on mobile phones

Mobile phone touchscreens have many ergonomic problems related to text entry. Previous studies, which attempted to use ambiguous keyboards to resolve problem of small keys, focused on the disambiguation process without consideration of the user loading on graphic user interface. This study investigates user loadings, which interfere with performance in the key selection phase when using an ambiguous keyboard. Hence, three QWERTY-like ambiguous keyboards and a standard QWERTY keyboard were compared via visual search and manual operation experiments. The visual search experiment shows that layouts with many letters per key were strongly related to long visual search times, and layouts with large keys were operated faster and more accurately in the manual operation experiment. Consequently, the trade-off between visual and manual loading differed among different letter-key assignments. This study is important in that it elucidates the impacts of visual and manual loadings on ambiguous keyboards, as well as in providing user interface designers with an enhanced understanding of how to design ambiguous keyboards based on user criteria.Relevance to industryAmbiguous keyboard designs in previous studies were generally far from the actual needs of users. This study examines the factors affecting text entry performance of users via the user-centered approach, improving the understanding of designers.