Live video streaming service with pay-as-you-use model on Ethereum Blockchain and InterPlanetary file system

Wireless Networks - In centralized video streaming platforms, the platform owner, rather than the content producer, controls most of the content uploaded on the centralized video...     

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.     

Use of MALDI-TOF MS technology to evaluate adulteration of small ruminant milk with raw bovine milk

Detection of adulteration of small ruminant milk is very important for health and commercial reasons. New analytical and cost-effective methods need to be developed to detect new adulteration practices. In this work, we aimed to explore the ability of the MALDI-TOF mass spectrometry to detect bovine milk in caprine and ovine milk using samples from 18 dairy farms. Different levels of adulteration (0.5, 1, 5, 10, 20, 40, 60, and 80%) were analyzed during the lactation period of goat and sheep (in May, from 60 to 90 d in milk, and in August, from 150 to 180 d in milk). Two different ranges of peptide-protein spectra (500–4,000 Da; 4–20 kDa) were used to establish a calibration model for predicting the concentration of adulterant using partial least squares and generalized linear model with lasso regularization. The low molecular weight part of the spectra together with the generalized linear model with lasso regularization regression model appeared to have greater potential for our aim of detection of adulteration of small ruminants' milk. The subsequent prediction model was able to predict the concentration of bovine milk in caprine milk with a root mean square error of 11.4 and 17.0% in ovine milk. The results offer compelling evidence that MALDI-TOF can detect the adulteration of small ruminants' milk. However, the method is severely limited by (1) the complexity of the milk proteome resulting from the adulteration technique, (2) the potential degradation of thermolabile proteins, and (3) the genetic variability of tested samples. Additionally, the root mean square error of prediction based only on one individual sample adulteration series can drop down to 6.34% for quantification of adulterated caprine milk and 6.28% for adulterated ovine milk for the full set of concentrations or down to 2.33 and 4.00%, respectively, if we restrict only to low concentrations of adulteration (0, 0.5, 1, 5, 10%).     

The Propagation Background in Social Networks: Simulating and Modeling

Machine Intelligence Research - Recent years have witnessed the booming of online social network and social media platforms, which leads to a state of information explosion. Though extensive...     

Generation and evaluation of excavation schedules for hard rock tunnels in preconstruction and construction

Uncertain product characteristics in construction projects make it difficult for planners to develop schedules that reduce expected costs, durations, and associated risks. To overcome these challenges in hard rock tunnel projects, this research introduces a methodology that adapts stochastic programming and feedback control approaches for their excavation. Such approaches require rapid and consistent implementation using up-to-date information provided in a probabilistic manner throughout the entire excavation; therefore, the authors tailored dynamic programming and tunneling risk analysis methods for the methodology to address multiple sets of rock mass properties (RMPs), transitions among excavation methods at the excavation method level, decision-making times, and schedule adjustment policies (SAPs). In preconstruction and construction, the methodology allows construction planners of hard rock tunnels to generate a total-cost-optimal excavation schedule for each set of RMPs and evaluate the excavation costs and durations of schedules for multiple sets of RMPs in a timely and consistent manner by considering SAPs. Further research is required to take into account multiple advances of excavation methods for schedule generation and evaluation.Database subject headingsAutomated schedule generation, hard rock tunnel, uncertainties in rock mass properties, feedback control, stochastic programming, earthwork risk analysis.     

Aligning human psychomotor characteristics with robots,exoskeletons and augmented reality

In previous production literature, the uncertainty of human behaviour has been recognized as a source of productivity, quality, and safety problems. However, fundamental reasons for the uncertainty of human behavior have received little analysis in the production literature. Furthermore, potential for these fundamental reasons to be aligned with production technologies in order to improve production performance has not been addressed. By contrast, in this paper, fundamental reasons for the uncertainty of human behaviour are explained through a model of psychomotor characteristics that encompasses physiology, past experiences, personality, gender, culture, emotion, reasoning, and biocybernetics. Through reference to 10 action research cases, the formal model is applied to provide guidelines for planning production work that includes robots, exoskeletons, and augmented reality.     

Why do people purchase virtual goods? A uses and gratification (U&G) theory perspective

Few literature studies have investigated the relationships between different uses and gratifications (U&Gs) of mobile instant messaging (MIM) apps, continuation, and purchase intentions. To address this gap, the researchers aimed to examine the influence of the content, social, process, and technology U&Gs of MIM on continuation intentions toward MIMs, and purchase intentions toward virtual goods available on MIMs. A comprehensive research model was developed based on the U&G theory, which was tested using cross-sectional data from 309 Japanese MIM users. The study considered six different U&Gs of MIM as independent variables and purchase intentions towards stickers and continuation intentions towards MIM as dependent variables. The study results suggest that exposure U&G has a significant positive association with MIM sticker purchase intentions. The entertainment and affection U&G are positively associated with continuation intentions towards MIM use. The study contributes to the literature by investigating U&Gs that motivate MIM users to have both positive purchase intentions toward virtual goods, such as stickers, and continuation intentions toward MIMs. The study has significant theoretical and practical implications for both researchers and practitioners who are interested in virtual goods, the virtual economy, MIM apps, social media, new media, and the service economy.     

Representing financial data streams in digital simulations to support data flow design for a future Digital Twin

Discrete Event Simulations may be used to forecast detailed production system behaviour under future conditions and support better informed decision making. However, data beyond production metrics, e.g. financial information, is also necessary for most significant decisions. Herein a modelling approach is proposed and demonstrated based on simulating both production and financial transactions. The contribution of this paper is to represent the base production events as well as additional events which trigger financial transactions. The method is demonstrated for an idealised production business, employing Discrete Event Simulation and examining the impact of system labour arrangements on the business cash flow with a variable demand based on a normal probability distribution function (with distinct means and variances for each product). Based on the financial transactions a representation of a detail commercial scenario may be modelled alongside the production events. The presented studies identify the potential sensitivity of analysis to the volume and timing of transactions on accumulated accounting metrics such as profit. Additionally the paper illustrates how such a modelling approach can be used to characterise the system and assess specific control strategies when both production and finance data streams are available.