Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation

A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant to leak any parameters of the trained object detectors. 10 years ago, Avidan & Butman introduced Blind Vision, which is a method for securely evaluating a Viola-Jones type object detector. Blind Vision uses standard cryptographic tools and is painfully slow to compute, taking a couple of hours to scan a single image. The purpose of this work is to explore an efficient method that can speed up the process. We propose the Random Base Image (RBI) Representation. The original image is divided into random base images. Only the base images are submitted randomly to the cloud server. Thus, the content of the image can not be leaked. In the meanwhile, a random vector and the secure Millionaire protocol are leveraged to protect the parameters of the trained object detector. The RBI makes the integral-image enable again for the great acceleration. The experimental results reveal that our method can retain the detection accuracy of that of the plain vision algorithm and is significantly faster than the traditional blind vision, with only a very low probability of the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul 14, 2017, Hong Kong, Hong Kon

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA. 2-7 Feb. 201

Consolidation considering clogging

In land reclamation projects, the vacuum preloading method has been widely used to strengthen dredged fills by removing water. However, during the improvement process, clogging inevitably occurs in the drains and soils, hindering water drainage and causing inhomogeneous consolidation results. Therefore, it is essential to evaluate the effect of clogging on the consolidation behavior of dredged slurry at different radii. In this study, analytical solutions are derived under an uneven strain assumption to calculate the consolidation in the clogging zone and the normal zone, with time-dependent discharge capacity and clogging in the soil considered. Results calculated by the proposed solutions indicated that the clogging effect slows down the development of consolidation, reduces the final consolidation degree, and increases the difference between consolidations at different radii. It is found that the influence of the clogging effect's varies with the speed of the discharge capacity decay, the value of the initial discharge capacity of the drain, the permeability, and the radius of the clogging zone. Finally, a practical application of the proposed solution is discussed, and the proposed solution is suggested for the calculation of consolidation when treating high-water-content slurry

Factors in cognitive processing of Japanese loanwords by advanced Chinese Japanese-as-a-foreign-language learners

IntroductionPrevious studies have highlighted the challenges faced by Chinese Japanese-as-a-foreign-language (JFL) learners (whose L2 is English) in acquiring L3 Japanese loanwords. These challenges arise from the linguistic characteristics of loanwords and the limited emphasis on teaching and learning them. However, there is a lack of research on the specific factors that influence the processing of Japanese loanwords among Chinese JFL learners. Significant motivation exists, therefore, to investigate these influencing factors as they provide valuable insight into the integration of phonographic and ideographic language systems, ultimately facilitating future lexical acquisition.MethodsIn this study, an experiment was conducted on 31 Chinese JFL learners to investigate the effects of loanword familiarity, English vocabulary proficiency, English-Japanese phonological similarity, and context on the processing of Japanese loanwords.ResultsData analysis, using a (generalized) linear mixed-effect model, provided the following insights: (1) the processing of Japanese loanwords is influenced by English-Japanese phonological similarity, loanword familiarity, context, and learner English proficiency. Among these four factors, familiarity has the most significant impact on Japanese loanword processing; (2) the effects of context and phonological similarity on the processing of Japanese loanwords are not consistently positive. As learners improve their proficiency in L3 Japanese, they tend to decrease their reliance on English knowledge and instead access loanword representations directly to conceptual representations.DiscussionBased on the findings of this study, a processing model for Japanese loanwords among advanced Chinese JFL learners is proposed. The model emphasizes the critical importance of the characteristics of loanwords, including phonological similarity and familiarity. It is necessary to determine the specific circumstances in which context considerably enhances learner processing ability

Research Progress of Forest Land Nutrient Management in China

Forest land fertilization is a supplement and regulation method based on the regular pattern of forest physiological activity and nutrient demand, combined with the ability of soil to supply nutrient elements. We summarized the important achievements and influential events of forest land fertilization and nutrient management in modern times, and discussed the main problems of forest land fertilization at this stage. The main theories of comprehensive nutrition diagnosis method, formula fertilization method, site nutrient effect fertilization model, and ASI-based balanced fertilization method were analyzed. The main scientific research institutions, main tree species, and main research results of forest fertilization research are described. The development trend of the comprehensive nutrition diagnosis method, the combination of forest fertilization theory and environmental ecology principle, the combination of fertilization and forest oriented cultivation goal, the application of precise fertilization technology in forest land, the development of new forest specific fertilizer, the research of plant nutrition molecular genetics, the research of root state and rhizosphere microecosystem, the application of advanced technology and technology, and the development and application of new nonpollution fertilizer were discussed. It is an important research direction to apply the existing research results to forestry production and improve the quality

Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation

Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic XRD patterns are proved to be an automatic, accurate, and high throughput method to analysis common XRD data collected from solid sample in ambient environment. However, it remains unknown that whether synthetic XRD based models are capable to solve u-XRD mapping data for in-situ experiments involving liquid phase exhibiting lower quality with significant artifacts. In this study, we collected u-XRD mapping data from an LaCl3-calcite hydrothermal fluid system and trained two categories of models to solve the experimental XRD patterns. The models trained by synthetic XRD patterns show low accuracy (as low as 64%) when solving experimental u-XRD mapping data. The accuracy of the DNN models was significantly improved (90% or above) when training them with the dataset containing both synthetic and small number of labeled experimental u-XRD patterns. This study highlighted the importance of labeled experimental patterns on the training of DNN models to solve u-XRD mapping data from in-situ experiments involving liquid phase.Comment: See link below for supporting information https://docs.google.com/document/d/1m2SyaBDej4BhkWCA38GRXJe5Jd7Di7cp/edit?usp=sharing&ouid=108731997922646321851&rtpof=true&sd=tru