Consensus Adversarial Defense Method Based on Augmented Examples

Deep learning has been used in many computer-vision-based industrial Internet of Things applications. However, deep neural networks are vulnerable to adversarial examples that have been crafted specifically to fool a system while being imperceptible to humans. In this study, we propose a consensus defense (Cons-Def) method to defend against adversarial attacks. Cons-Def implements classification and detection based on the consensus of the classifications of the augmented examples, which are generated based on an individually implemented intensity exchange on the red, green, and blue components of the input image. We train a convolutional neural network using augmented examples together with their original examples. For the test image to be assigned to a specific class, the class occurrence of the classifications on its augmented images should be the maximum and reach a defined threshold. Otherwise, it is detected as an adversarial example. The comparison experiments are implemented on MNIST, CIFAR-10, and ImageNet. The average defense success rate (DSR) against white-box attacks on the test sets of the three datasets is 80.3%. The average DSR against black-box attacks on CIFAR-10 is 91.4%. The average classification accuracies of Cons-Def on benign examples of the three datasets are 98.0%, 78.3%, and 66.1%. The experimental results show that Cons-Def shows a high classification performance on benign examples and is robust against white-box and black-box adversarial attacks

Using outlier elimination to assess learning-based correspondence matching methods

Recently, deep learning (DL) technology has been widely used in correspondence matching. The learning-based models are usually trained on benign image pairs with partial overlaps. Since DL model is usually data-dependent, non-overlapping images may be used as poison samples to fool the model and produce false registrations. In this study, we propose an outlier elimination based assessment method (OEAM) to assess the registrations of learning-based correspondence matching method on partially overlapping and non-overlapping images. OEAM first eliminates outliers based on spatial paradox. Then OEAM implements registration assessment in two streams using the obtained core correspondence set. If the cardinality of the core set is sufficiently small, the input registration is assessed as a low-quality registration. Otherwise, it is assessed to be of high quality, and OEAM improves its registration performance using the core set. OEAM is a post-processing technique imposed on learning-based method. The comparison experiments are implemented on outdoor (YFCC100M) and indoor (SUN3D) datasets using four deep learning-based methods. The experimental results on registrations of partially overlapping images show that OEAM can reliably infer low-quality registrations and improve performance on high-quality registrations. The experiments on registrations of non-overlapping images demonstrate that learning-based methods are vulnerable to poisoning attacks launched by non overlapping images, and OEAM is robust against poisoning attacks crafted by non-overlapping images

Prior knowledge-based deep learning method for indoor object recognition and application

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision

Ensuring water resource security in China; the need for advances in evidence based policy to support sustainable management.

China currently faces a water resource sustainability problem which is likely to worsen into the future. The Chinese government is attempting to address this problem through legislative action, but faces severe challenges in delivering its high ambitions. The key challenges revolve around the need to balance water availability with the need to feed a growing population under a changing climate and its ambitions for increased economic development. This is further complicated by the complex and multi-layered government departments, often with overlapping jurisdictions, which are not always aligned in their policy implementation and delivery mechanisms. There remain opportunities for China to make further progress and this paper reports on the outcomes of a science-to-policy roundtable meeting involving scientists and policy-makers in China. It identifies, in an holistic manner, new opportunities for additional considerations for policy implementation, continued and new research requirements to ensure evidence-based policies are designed and implemented and identifies the needs and opportunities to effectively monitor their effectiveness. Other countries around the world can benefit from assessing this case study in China

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An efficient and secure itinerary-based data aggregation algorithm for WSNs

The existing privacy-preserving data aggregation methods in wireless sensor networks (WSNs) generally rely on a network infrastructure, and data privacy is achieved by encryption techniques. However, such an infrastructure is very susceptible to the dynamic network topologies, and excessive encryption process causes a high energy consumption and re-duces the accuracy of the aggregation results. In this paper, we propose a secure and concentric-circle itinerary-based data aggregation algorithm (called SCIDA for short). With the help of a well-designed itinerary for aggregation propagation and data aggregation, SCIDA is not susceptible to network topology structure and thus suitable for wireless sensor net-works with dynamic network topologies and can save energy for network infrastructure maintenance. In addition, SCIDA uses a secure channel to ensure data privacy and avoids dramatic energy consumption caused by heavy encryption operations. SCIDA does not need to carry out encryption during data aggregation, which significantly reduces energy consumption, and prolongs the lifetime of the network. Theoretical analysis and experimental results show that SCIDA enjoys low communication overhead and energy con-sumption, yet high safety and accuracy

A novel social network hybrid recommender system based on hypergraph topologic structure

With the advent and popularity of social network, more and more people like to share their experience in social network. However, network information is growing exponentially which leads to information overload. Recommender system is an effective way to solve this problem. The current research on recommender systems is mainly focused on research models and algorithms in social networks, and the social networks structure of recommender systems has not been analyzed thoroughly and the so-called cold start problem has not been resolved effectively. We in this paper propose a novel hybrid recommender system called Hybrid Matrix Factorization(HMF) model which uses hypergraph topology to describe and analyze the interior relation of social network in the system. More factors including contextual information, user feature, item feature and similarity of users ratings are all taken into account based on matrix factorization method. Extensive experimental evaluation on publicly available datasets demonstrate that the proposed hybrid recommender system outperforms the existing recommender systems in tackling cold start problem and dealing with sparse rating datasets. Our system also enjoys improved recommendation accuracy compared with several major existing recommendation approaches

Parameter Optimization of Drilling Cuttings Entering into Sieve Holes on a Surface Multi-Hole (SMH) Drill Pipe

The borehole drilling distance is short in soft and gas outburst-prone coal seams because of drill pipe jamming induced by cuttings accumulating in the borehole, hindering coal mine gas hazard prevention and utilization. A surface multi-hole (SMH) drill pipe composed of a bearing layer, fluid layer, and anti-sparking layer was proposed preliminarily, where several sieve holes were also set. To study the process of drilling cuttings in boreholes entering into the inner hole of an SMH drill pipe and its influencing factors, mechanical model analysis, CFD-DEM simulation, and a physical experiment were conducted. Our research results show the cutting entering region (CER) of the SMH drill pipe shrinks with the rotary speed, expands with the external extrusion force, and is offset with the sieve hole inclination angle. The drilling cuttings migrate and accumulate over time between the borehole wall and SMH drill pipe, which increases their compressive forces and induces increases in the mass and diameter of those entering into the sieve holes. The sieve hole diameter and depth are critical factors impacting the drilling cuttings entering into the sieve holes, which is also related to an appropriate rotary speed of the drill pipe. Finally, SMH drill pipes with a sieve hole diameter of 10 mm, inclination angle of 10°, and depth of 8 mm were determined and trial-manufactured

Parameter Optimization of Drilling Cuttings Entering into Sieve Holes on a Surface Multi-Hole (SMH) Drill Pipe

The borehole drilling distance is short in soft and gas outburst-prone coal seams because of drill pipe jamming induced by cuttings accumulating in the borehole, hindering coal mine gas hazard prevention and utilization. A surface multi-hole (SMH) drill pipe composed of a bearing layer, fluid layer, and anti-sparking layer was proposed preliminarily, where several sieve holes were also set. To study the process of drilling cuttings in boreholes entering into the inner hole of an SMH drill pipe and its influencing factors, mechanical model analysis, CFD-DEM simulation, and a physical experiment were conducted. Our research results show the cutting entering region (CER) of the SMH drill pipe shrinks with the rotary speed, expands with the external extrusion force, and is offset with the sieve hole inclination angle. The drilling cuttings migrate and accumulate over time between the borehole wall and SMH drill pipe, which increases their compressive forces and induces increases in the mass and diameter of those entering into the sieve holes. The sieve hole diameter and depth are critical factors impacting the drilling cuttings entering into the sieve holes, which is also related to an appropriate rotary speed of the drill pipe. Finally, SMH drill pipes with a sieve hole diameter of 10 mm, inclination angle of 10°, and depth of 8 mm were determined and trial-manufactured