Environmental ethics support for eco city construction

The modern city based on the principle of anthropocentrism has caused many urban environmental problems, resulting in the contradiction between man and nature gradually developing into a state of binary opposition, and promoting the urban ecosystem to be on the verge of danger. On the basis of deep reflection on modern urban environmental problems, eco city construction came into being and developed rapidly. Eco city is an urban development model to solve the current urban environmental crisis and realize the harmonious coexistence between man and nature, man and man, man and societyeco city has internal consistency with environmental ethics, which contains the environmental ethics of non anthropocentrism, sustainable development and environmental justiceenvironmental ethics guides the construction of ecological city and is an important support for the construction of ecological city. First of all, environmental ethical values such as environmental ethical values, environmental ethical codes of ethics and environmental ethical beliefs provide theoretical support for the construction of ecological citysecondly, environmental ethical practices such as ecological ethical culture, ecological moral education, low-carbon economic model and ecological ethical legal system provide practical support for the construction of ecological cities

A Benchmark of Long-tailed Instance Segmentation with Noisy Labels (Short Version)

In this paper, we consider the instance segmentation task on a long-tailed dataset, which contains label noise, i.e., some of the annotations are incorrect. There are two main reasons making this case realistic. First, datasets collected from real world usually obey a long-tailed distribution. Second, for instance segmentation datasets, as there are many instances in one image and some of them are tiny, it is easier to introduce noise into the annotations. Specifically, we propose a new dataset, which is a large vocabulary long-tailed dataset containing label noise for instance segmentation. Furthermore, we evaluate previous proposed instance segmentation algorithms on this dataset. The results indicate that the noise in the training dataset will hamper the model in learning rare categories and decrease the overall performance, and inspire us to explore more effective approaches to address this practical challenge. The code and dataset are available in https://github.com/GuanlinLee/Noisy-LVIS

Adversarial Training Over Long-Tailed Distribution

In this paper, we study adversarial training on datasets that obey the long-tailed distribution, which is practical but rarely explored in previous works. Compared with conventional adversarial training on balanced datasets, this process falls into the dilemma of generating uneven adversarial examples (AEs) and an unbalanced feature embedding space, causing the resulting model to exhibit low robustness and accuracy on tail data. To combat that, we propose a new adversarial training framework -- Re-balancing Adversarial Training (REAT). This framework consists of two components: (1) a new training strategy inspired by the term effective number to guide the model to generate more balanced and informative AEs; (2) a carefully constructed penalty function to force a satisfactory feature space. Evaluation results on different datasets and model structures prove that REAT can effectively enhance the model's robustness and preserve the model's clean accuracy. The code can be found in https://github.com/GuanlinLee/REAT

Application of waterproof breathable fabric in thermal protective clothing exposed to hot water and steam

A hot water and steam tester was used to examine thermal protective performance of waterproof and breathable fabric against hot water and steam hazards. Time to cause skin burn and thermal energy absorbed by skin during exposure and cooling phases was employed to characterize the effect of configuration, placing order and properties of waterproof and breathable fabric on the thermal protective performance. The difference of thermal protective performance due to hot water and steam hazards was discussed. The result showed that the configuration of waterproof and breathable fabric presented a significant effect on the thermal protective performance of single- and double-layer fabric system, while the difference between different configurations in steam hazard was greater than that in hot water hazard. The waterproof and breathable fabric as outer layer provided better protection than that as inner layer. Increasing thickness and moisture regain improved the thermal protective performance of fabric system. Additionally, the thermal energy absorbed by skin during the cooling phase was affected by configuration, thickness and moisture regain of fabric. The findings will provide technical data to improve performance of thermal protective clothing in hot water and steam hazards

Development of a numerical model to predict physiological strain of firefighter in fire hazard

This paper aims to develop a numerical model to predict heat stress of frefghter under low-level thermal radiation. The model integrated a modifed multi-layer clothing model with a human thermoregulation model. We took the coupled radiative and conductive heat transfer in the clothing, the size-dependent heat transfer in the air gaps, and the controlling active and controlled passive thermal regulation in human body into consideration. The predicted core temperature and mean skin temperature from the model showed a good agreement with the experimental results. Parametric study was conducted and the result demonstrated that the radiative intensity had a signifcant infuence on the physiological heat strain. The existence of air gap showed positive efect on the physiological heat strain when air gap size is small. However, when the size of air gap exceeds 6mm, a diferent trend was observed due to the occurrence of natural convection. Additionally, the time length for the existence of the physiological heat strain was greater than the existence of the skin burn under various heat exposures. The fndings obtained in this study provide a better understanding of the physiological strain of frefghter and shed light on textile material engineering for achieving higher protective performance

Achieving Fine-grained Multi-keyword Ranked Search over Encrypted Cloud Data

With the advancement of Cloud computing, people now store their data on remote Cloud servers for larger computation and storage resources. However, users’ data may contain sensitive information of users and should not be disclosed to the Cloud servers. If users encrypt their data and store the encrypted data in the servers, the search capability supported by the servers will be significantly reduced because the server has no access to the data content. In this paper, we propose a Fine-grained Multi-keyword Ranked Search (FMRS) scheme over encrypted Cloud data. Specifically, we leverage novel techniques to realize multikeyword ranked search, which supports both mixed “AND”, “OR” and “NO” operations of keywords and ranking according to the preference factor and relevance score. Through security analysis, we can prove that the data confidentiality, privacy protection of index and trapdoor, and the unlinkability of trapdoor can be achieved in our FMRS. Besides, Extensive experiments show that the FMRS possesses better performance than existing schemes in terms of functionality and efficiency