Improved Deep Forest Mode for Detection of Fraudulent Online Transaction

As the rapid development of online transactions, transaction frauds have also emerged seriously. The fraud strategies are characterized by specialization, industrialization, concealment and scenes. Anti-fraud technologies face many challenges under the trend of new situations. In this paper, aiming at sample imbalance and strong concealment of online transactions, we enhance the original deep forest framework to propose a deep forest-based online transaction fraud detection model. Based on the BaggingBalance method we propose, we establish a global sample imbalance processing mechanism to deal with the problem of sample imbalance. In addition, the autoencoder model is introduced into the detection model to enhance the representation learning ability. Via the three-month real online transactions data of a China's bank, the experimental results show that, evaluating by the metric of precision and recall rate, the proposed model has a beyond 10 % improvement compared to the random forest model, and a beyond 5 % improvement compared to the original deep forest model

Physics-guided Noise Neural Proxy for Low-light Raw Image Denoising

Low-light raw image denoising plays a crucial role in mobile photography, and learning-based methods have become the mainstream approach. Training the learning-based methods with synthetic data emerges as an efficient and practical alternative to paired real data. However, the quality of synthetic data is inherently limited by the low accuracy of the noise model, which decreases the performance of low-light raw image denoising. In this paper, we develop a novel framework for accurate noise modeling that learns a physics-guided noise neural proxy (PNNP) from dark frames. PNNP integrates three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution-oriented loss (DDL). The PND decouples the dark frame into different components and handles different levels of noise in a flexible manner, which reduces the complexity of the noise neural proxy. The PPM incorporates physical priors to effectively constrain the generated noise, which promotes the accuracy of the noise neural proxy. The DDL provides explicit and reliable supervision for noise modeling, which promotes the precision of the noise neural proxy. Extensive experiments on public low-light raw image denoising datasets and real low-light imaging scenarios demonstrate the superior performance of our PNNP framework

Network service registration based on role-goal-process-service meta-model in a P2P network

Service composition-based network software customisation is currently a research hotspot in the field of software engineering. A key problem of the hotspot is how to efficiently discover services distributed over the Internet. In the service oriented architecture, service discovery suffers from the performance bottleneck of centralised universal description discovery and integration (UDDI), and inaccurate matching of service semantics. In this study, the authors describe a novel method for service labelling, registration and discovery, which is based on the role-goal-process-service meta-model. This approach enables ones to achieve accurate matching of service semantics by extending web service description language with RGP demand-information. The authors also suggest a peer-to-peer (P2P)-based architecture of service discovery to address the issues in the UDDI bottleneck and the complexity of semantic computation. By adopting the proposed approach, an experiment prototype system has been designed and implemented in Beijing municipal transportation system. The experimental results show the proposed approach is effective in addressing the aforementioned problems

Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling

Image denoising is a fundamental problem in computational photography, where achieving high-quality perceptual performance with low distortion is highly demanding. Current methods either struggle with perceptual performance or suffer from significant distortion. Recently, the emerging diffusion model achieves state-of-the-art performance in various tasks, and its denoising mechanism demonstrates great potential for image denoising. However, stimulating diffusion models for image denoising is not straightforward and requires solving several critical problems. On the one hand, the input inconsistency hinders the connection of diffusion models and image denoising. On the other hand, the content inconsistency between the generated image and the desired denoised image introduces additional distortion. To tackle these problems, we present a novel strategy called Diffusion Model for Image Denoising (DMID) by understanding and rethinking the diffusion model from a denoising perspective. Our DMID strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained diffusion model, and an adaptive ensembling method that reduces distortion in the denoised image. Our DMID strategy achieves state-of-the-art performance on all distortion-based and perceptual metrics, for both Gaussian and real-world image denoising.Comment: 10 pages,7 figure

Multi - mechanism coalescence design and matrix expression of logic action sequences of the over-turn nursing robot Part I: Functions and coalescence design

In order to effectively solve the problem in over-turn of a bedridden person with the assistance of external force, a double bed face- three embedded leave over-turn nursing robot with the flexible compensation was put forward, with the abstraction of the bedridden person as an organism. This robot, on the basis of concept gesture of the person in bed and the state of the robot supporting and proving the gesture with the actions and combination of the two bed faces, held the complete function of over-turn nursing with 7 states corresponding to 5 gestures of the bedridden person obeying the fundamental requirements of safety, rapidity, and comport. The design method of "PS-MM-KD" was proposed for multi-mechanism coalescent system with related specific tasks induced from the original problems with Systems Engineering. Mechanics and Mechanisms, then applied in the concrete sub-system design followed by analysis and verification of both the scheme and the sub-systems in the design, using the Kinematics and Dynamics, implementing the gears, chain wheel, slewing mechanism, screw nut and mortise and tenon joint type clutch mechanism design successfully. Based on those above, a "two-bed face/three-leaf embedded flexible compensation nursing robot" was designed adopting to all ages, people of various kinds of body geometry. PLC, sensor and logic algorithm were used to carry out the control and operation of 7 state-5 posture sequences for realization of the automation and intelligent over-turning in safety, comfort, and convenience

Multi - mechanism coalescence design and matrix expression of logic action sequences of the over-turn nursing robot Part II: Gesture-state in sets and matrix

As is expressed in Part I, Functions and coalescence design of the over-turn nursing robot, the performance and requirements have been put forward with systematic design of several mechanisms. Here, in order to control and function well the over-turn nursing robot, the three-dimensional and five-dimensional Euclidean space with the real number were adopted in terms of sets for gesture of the bedridden person and the corresponding state of the robot, respectively. The matrix method was employed to define and describe the gestures-robot performance and its transition path. The gesture-state sequence matrix not only accurately and clearly expressed the gesture series, state sequence and their corresponding relations, but also laid a theoretical and technical foundation for the path planning from the current gesture to the target one. The control and operation of 7 states and 5 gestures were done to realize the automation and intelligent over-turning safely, comfortably and conveniently

The serum matrix metalloproteinase-9 level is an independent predictor of recurrence after ablation of persistent atrial fibrillation

OBJECTIVES: This study investigated whether the serum matrix metalloproteinase-9 level is an independent predictor of recurrence after catheter ablation for persistent atrial fibrillation. METHODS: Fifty-eight consecutive patients with persistent atrial fibrillation were enrolled and underwent catheter ablation. The serum matrix metalloproteinase-9 level was detected before ablation and its relationship with recurrent arrhythmia was analyzed at the end of the follow-up. RESULTS: After a mean follow-up of 12.1±7.2 months, 21 (36.2%) patients had a recurrence of their arrhythmia after catheter ablation. At baseline, the matrix metalloproteinase-9 level was higher in the patients with recurrence than in the non-recurrent group (305.77±88.90 vs 234.41±93.36 ng/ml, respectively, p=0.006). A multivariate analysis showed that the matrix metalloproteinase-9 level was an independent predictor of arrhythmia recurrence, as was a history of atrial fibrillation and the diameter of the left atrium. CONCLUSION: The serum matrix metalloproteinase-9 level is an independent predictor of recurrent arrhythmia after catheter ablation in patients with persistent atrial fibrillation