Recently, semantic segmentation has been widely applied in various realistic scenarios. Many semantic segmentation backbone networks use various forms of dense connection to improve the efficiency of gradient propagation in the network. They achieve excellent segmentation accuracy but lack inference speed. Therefore, we propose a backbone network SCDNet with a dual path structure and higher speed and accuracy. Firstly, we propose a split connection structure, which is a streamlined lightweight backbone with a parallel structure to increase inference speed. Secondly, we introduce a flexible dilated convolution using different dilation rates so that the network can have richer receptive fields to perceive objects. Then, we propose a three-level hierarchical module to effectively balance the feature maps with multiple resolutions. Finally, a refined flexible and lightweight decoder is utilized. Our work achieves a trade-off of accuracy and speed on the Cityscapes and Camvid datasets. Specifically, we obtain a 36% improvement in FPS and a 0.7% improvement in mIoU on the Cityscapes test set.
Privacy policy statements are an essential approach to self-regulation by website operators in the area of personal privacy protection. However, these policies are often lengthy and difficult to understand, with users appearing to actually read the privacy policy in only a few cases. To address these obstacles, we propose a framework, Privacy Policy Analysis Framework for Automatic Annotation and User Interaction (PPAI) that stores, classifies, and categorizes queries on natural language privacy policies. At the core of PPAI is a privacy-centric language model that consists of a smaller fine-grained dataset of privacy policies and a new hierarchy of neural network classifiers that take into account privacy practices with high-level aspects and finegrained details. Our experimental results show that the eight readability metrics of the dataset exhibit a strong correlation. Furthermore, PPAI’s neural network classifier achieves an accuracy of 0.78 in the multi-classification task. The robustness experiments reached higher accuracy than the baseline and remained robust even with a small amount of labeled data.
Visual sensors on autonomous vehicles are vulnerable to adverse weather, which seriously reduces the performance of semantic segmentation task and threats people’s safety. Therefore, segmentation models need to carry out extensive training on a large amount of adverse weather data which is difficult and expensive to acquire to improve their robustness. To solve this problem, researchers have proposed domain generalization methods that do not need target domain (such as adverse weather) data to adapt during training. However, most of them focus on the synthetic-to-real problem which is caused by the difference in texture between real and virtual images. To address these challenges, we analyze the formation mechanism behind adverse weather, extract two kinds of weather cues and establish the relationship between them and adverse weather. On this basis, we propose CSCR, a domain randomization framework for simulating adverse weather. Specifically, CSCR includes the Common Cue Randomization (CCR) module which simulates adverse illumination style and the Specific Cue Randomization (SCR) module which randomizes weather cues that occur in specific adverse weather. We conduct extensive experiments from fair weather to fog, night, rain and snow on driving datasets. Compared with the source model, CSCR increases by more than 9% points in mIoU on average which even exceeds some domain adaptation methods while still keeping the memory of fair weather. The CSCR framework can be easily applied to existing segmentation models and significantly improve their generalization ability.
Medical Recommendation Service (MRS) can provide simple diagnostics and recommend a suitable doctor for patients when they input their disease symptoms by using AI and big data. However, people may be unwilling to share medical data due to data privacy issues and a lack of incentives, which makes it hard for MRS to continue to develop. Until now, there are no efficient solutions. To solve this, we propose a blockchain-based anonymous reward mechanism for medical recommendation (BARM) in smart healthcare. The proposed scheme uses accumulator, commitment, and Signatures of Knowledge (SoK) to provide anonymous rewards for users to mobilize more users to share data as much as possible. And we design a smart contract that can provide submit, query, and revoke functions to realize anonymous authentication of patients. The formal security analysis shows that the proposed scheme meets the required security requirements. Detailed performance analysis results show that the computation cost and communication cost of the scheme is feasible, which can be applied to the medical recommendation scenario of anonymous protection and promote the development of smart healthcare.
The fMRI functional imaging technology has become an important means to detect brain diseases. It mainly diagnoses whether there are lesions in the patient’s brain region by monitoring the changes of the blood oxygen level (BOLD) in the brain region. Existing brain disease classification techniques are mainly achieved through network construction, feature extraction and classification model construction. But in the existing methods, almost no one uses the time attribute. In this paper, we propose a brain disease diagnosis framework with temporal attribute and feature fusion, use temporal and BOLD signal’s changes in different time periods to construct a brain connectivity network. We extract frequent sequences and sequences’ connection rules in the subjects’ brain connection network, and finally use pseudo-binary matrix to fuse the two features and finish classification model construction. Experiment turns out that we achieve better accuracy than other schemes.
