Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Novel approaches to purifying bacteriocin: A review

ABSTRACT

Bacteriocin is a proteinaceous biomolecule produced by bacteria (both Gram-positive and Gram-negative) that exhibits antimicrobial activity against closely related species, and food-borne pathogens. It has recently gained importance and attracted the attention of several researchers looking to produce it from various substrates and bacterial strains. This ushers in a new era of food preservation where the use of bacteriocin in food products will be an alternative to chemical preservatives, and heat treatment which are understood to cause unwanted side effects, and reduce sensory and nutritional quality. However, this new market depends on the success of novel downstream separation schemes from various types of crude feedstocks which are both effective and economic. This review focuses on the downstream separation of bacteriocin from various sources using both conventional and novel techniques. Finally, recommendations for future interesting areas of research that need to be pursued are highlighted.     

不同硬度PTMG型CPUE与硫化橡胶的性能对比研究

通过改变硬段含量、扩链系数等制备了几种不同硬度的四氢呋喃均聚醚(PTMG)浇注型聚氨酯弹性体(CPUE),讨论了PTMG型CPUE与硫化橡胶(VR)的物理机械性能、拉伸应力应变行为以及动态力学性能的区别。结果表明,随CPUE硬度的提高,材料的刚性增强,力学性能是低硬度VR的数倍。通过对CPUE硬度的调节,可以使之兼具聚氨酯和橡胶的优良性能。在玻璃态温度下CPUE的储能模量随硬度的减小而降低,但都高于VR,而且CPUE的硬度越大,二者相差越大,温度越低,相差也越大;在室温以上温度范围内,CPUE具有比VR更小的力学损耗。     

Modelling of chromatic contrast for retrieval of wallpaper images

Colour remains one of the key factors in presenting an object and, consequently, has been widely applied in retrieval of images based on their visual contents. However, a colour appearance changes with the change of viewing surroundings, the phenomenon that has not been paid attention yet while performing colour‐based image retrieval. To comprehend this effect, in this article, a chromatic contrast model, CAMcc, is developed for the application of retrieval of colour intensive images, cementing the gap that most of existing colour models lack to fill by taking simultaneous colour contrast into account. Subsequently, the model is applied to the retrieval task on a collection of museum wallpapers of colour‐rich images. In comparison with current popular colour models including CIECAM02, HSI and RGB, with respect to both foreground and background colours, CAMcc appears to outperform the others with retrieved results being closer to query images. In addition, CAMcc focuses more on foreground colours, especially by maintaining the balance between both foreground and background colours, while the rest of existing models take on dominant colours that are perceived the most, usually background tones. Significantly, the contribution of the investigation lies in not only the improvement of the accuracy of colour‐based image retrieval but also the development of colour contrast model that warrants an important place in colour and computer vision theory, leading to deciphering the insight of this age‐old topic of chromatic contrast in colour science. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 361–373, 2015     

Extended preservation of raw beef and pork meat by hyperbaric storage at room temperature

Hyperbaric storage (HS) was evaluated as a new food preservation methodology at room temperature (RT) for beef and pork meat, both minced and in pieces, and compared to refrigeration (RF) storage. The meat samples were stored at 50, 75 and 100 MPa and variable RT up to 60 days. HS at 75 and 100 MPa could not only inhibit microbial growth but also inactivate microorganisms. Regarding physicochemical analyses, an overall equal to better pH maintenance in HS samples was achieved, and similar colour differences between HS and RF were observed. Generally, similarities in moisture content and drip loss between HS (mostly 75 and 100 MPa) and RF were detected (tendency for lower values in the former and higher values in the latter for the higher pressure level). Protein solubility revealed a decrease of sarcoplasmic protein values during storage with a pressure level dependency in some samples.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.     

Attitude-behaviour dissonance regarding the importance of food preservation for customers

The aim of the study was to identify the importance of individual food product attributes (the use of preservatives, processing method, shelf-life period and nutritional value) as well as their relation to the purchase behaviour. To achieve this, consumer preferences were decomposed in conditions of full access to information, and data was compared with actual consumer behaviour related to making purchasing decisions in front of the store shelf. Based on data from 338 respondents, conjoint analysis and repeated ANOVA measurements were carried out, allowing to eliminate individual behavioural patterns. The results showed a dissonance between the consumers attitudes towards the attributes and their shopping behaviour. The processing method was the most important declared attribute for consumers, meanwhile this information was the least searched for during purchase. At the same time, shelf-life period marked as the least important was the main information searched for by consumers when shopping. The results also show a clear dislike towards the use of microwaves as a preservation method, while traditional thermal preservation was marked as the most positive for consumers. In addition, consumers were classified into 3 heterogeneous groups, identifying differences in the preferences of food attributes. The obtained results have practical value relating not only to the preferences of individual attributes, but also to their various variants. The results can be a guide for companies to properly label food products. The indicated dissonance illustrates the educational gap and related insufficient motivation to get to know the product before buying it. This is extremely important in connection with technological development and new food processing methods.     

具有光照鲁棒的图像匹配方法

针对现有的基于局部特征的图像匹配算法对光照变化敏感、匹配正确率低等问题，提出一种具有光照鲁棒性的图像匹配算法。首先使用实时对比保留去色（RTCP）算法灰度化图像，然后利用对比拉伸函数模拟不同光照变换对图像的影响从而提取抗光照变换特征点，最后采用局部强度顺序模式建立特征点描述符，根据待匹配图像局部特征点描述符的欧氏距离判断是否为成对匹配点。在公开数据集上，所提算法与尺度不变特征变换（SIFT）算法、加速鲁棒特征（SURF）算法、"风"（KAZE）算法和ORB算法在匹配速度和匹配正确率上进行了对比实验。实验结果表明：随着图像亮度差异的增加，SIFT算法、SURF算法、"风"（KAZE）算法和ORB算法匹配正确率下降迅速，所提算法下降缓慢并且正确率均高于80%；所提算法特征点检测较慢和描述符维数较高，平均耗时为23.47 s，匹配速度不及另外四种算法，但匹配质量却远超过它们。对实时性要求不高的系统中，所提算法可以克服光照变化对图像匹配造成的影响。     

Suitability,efficiency and microbiological safety of novel physical technologies for the processing of ready‐to‐eat meals,meats and pumpable products

Consumer studies and market reports show an increase in consumption of ready‐to‐eat (RTE) foods. Although conventional processing technologies can in most cases produce safe products, they can also lead to the degradation of nutritional compounds and negatively affect quality characteristics. Consumers strongly prefer food that is minimally processed with the maximum amount of health‐promoting substances. Novel processing technologies as pre‐ or post‐treatment decontamination methods or as substitutes of conventional technologies have the potential to produce foods that are safe, rich in nutrient content and with superior organoleptic properties. Combining novel with conventional processes can eliminate potential drawbacks of novel technologies. This review examines available scientific information and critically evaluates the suitability and efficiency of various novel thermal and nonthermal technologies in terms of microbial safety, quality as well as nutrient content on the production of RTE meals, meats and pumpable products.     

垦71井区三维VSP资料波场分离方法应用研究

三维VSP资料是多偏移距VSP资料，资料中多种类型的波叠合在一起形成复杂波场。从复杂波场中分离出单一的保幅反射波波场是三维VSP波场分离的重要工作。常规二维VSP波场处理方法单一，难以适用于复杂的三维波场处理。针对三维VSP资料的波场特点，以分离上行反射P波为例，将单一波场分离方法加以适当组合，对垦71井区三维VSP实际资料进行了应用研究。结果表明，波场分离处理中叠加消去法和中值滤波相结合以及F—K滤波和中值滤波相结合的方法克服了单一方法的缺陷，波场处理后获得了波组特征明显、波场清晰单一的上行反射P波保幅波场，取得了较好的波场分离效果。