Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.

Abstract: Purpose Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, e.g., few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results Based on the state-of-the-art network, we provide more than 40 pre-trained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average Dice Similarity Coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average Normalized Surface Dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. Conclusions To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation, and the largest number of pre-trained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.

Dominance-based rough set approach to incomplete interval-valued information system

Abstract: Since preference order is a crucial feature of data concerning decision situations, the classical rough set model has been generalized by replacing the indiscernibility relation with a dominance relation. The purpose of this paper is to further investigate the dominance-based rough set in incomplete interval-valued information system, which contains both incomplete and imprecise evaluations of objects. By considering three types of unknown values in the incomplete interval-valued information system, a data complement method is used to transform the incomplete interval-valued information system into a traditional one. To generate the optimal decision rules from the incomplete interval-valued decision system, six types of relative reducts are proposed. Not only the relationships between these reducts but also the practical approaches to compute these reducts are then investigated. Some numerical examples are employed to substantiate the conceptual arguments.

Secure Distributed Finite-Time Filtering for Positive Systems Over Sensor Networks Under Deception Attacks

Abstract: This paper is concerned with secure ${\ell _{1}}$ -gain performance analysis and distributed finite-time filter design for a positive discrete-time linear system over a sensor network in the presence of deception attacks. A group of intercommunicating sensors is densely deployed to measure, gather, and process the output of the positive system. Each sensor is capable of sharing its measurement with its neighboring sensors in accordance with a prescribed network topology while suffering from random communication link failure. Meanwhile, the aggregated measurement on each sensor during network transmission is corrupted by stochastic deception attacks which compromise the sensor’s measurement integrity. First, a unified sensor measurement transmission model is put forward to account for the simultaneous presence of deception attacks and various network-induced constraints. Second, delicate secure distributed filters are constructed by admitting the corrupted sensor measurement. Third, theoretical analysis on finite-time ${\ell _{1}}$ -gain boundedness of the filtering error system and design of desired positive filters are carried out. The solution to the filter gain parameters is characterized by a set of linear programming inequalities. Finally, the effectiveness of the obtained results is verified through the secure monitoring of power distribution in the smart grid.