Nanjing University of Information Science and Technology

About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.

The Eighth Visual Object Tracking VOT2020 Challenge Results

Abstract: The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).

A climatology of aerosol optical depth over China from recent 10 years of MODIS remote sensing data

Abstract: Using the recent 10-year (March 2000 to February 2010) MODIS data of aerosol optical depth (AOD), the distributions of annual and seasonal mean AOD over China are presented, and the trends and seasonal variations in AOD over 10 regions in China are analysed. The spatial pattern of annual mean AOD is characterized generally with two low centres and two high centres over China. Two low AOD centres are located in the areas with a high vegetation cover and a sparse population in (1) the high-latitude region in Northeast China with AOD of about 0.2 and (2) the high-altitude region in Southwest China with AOD from 0.1 to 0.2. These two low AOD centres are connected by a low AOD zone (0.2–0.3) in a northeast–southwest direction across China. Demarcated by this low AOD zone, two high centres with AOD of about 0.8 are situated in (1) the most densely populated and industrialized regions in China with high anthropogenic aerosols from North China Plain, Yangtze River areas covering Sichuan Basin, Central China and Yangtze River Delta to South China with Pearl River Delta region and (2) Tarim Basin in Northwest China with high natural aerosols dominated with desert dust. The spatial AOD patterns over China keep seasonally unchanged, but the strengths of the AOD-centres vary from season to season. The wintertime AOD is lowest in China. The monthly AOD variations from March to September in Southern China correspond with high AOD before, after the rain periods and low AOD during the rain periods of Asian summer monsoon. Asian summer monsoons also make a notable impact on the seasonal cycle of aerosols in China. The AOD in Northern China changes monthly with a single peak between April and June and a low in the winter months. The positive trends in AOD occur mostly in the aerosol source regions with higher annual mean AOD (>0.25), while the negative trends are found in the regions with lower annual mean AOD (<0.25) over China.

Enabling Semantic Search Based on Conceptual Graphs over Encrypted Outsourced Data

Abstract: Currently, searchable encryption is a hot topic in the field of cloud computing The existing achievements are mainly focused on keyword-based search schemes, and almost all of them depend on predefined keywords extracted in the phases of index construction and query However, keyword-based search schemes ignore the semantic representation information of users’ retrieval and cannot completely match users’ search intention Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge In this paper, for the first time, we define and solve the problems of semantic search based on conceptual graphs (CGs) over encrypted outsourced data in clouding computing (SSCG) We first employ the efficient measure of “sentence scoring” in text summarization and Tregex to extract the most important and simplified topic sentences from documents We then convert these simplified sentences into CGs To perform quantitative calculation of CGs, we design a new method that can map CGs to vectors Next, we rank the returned results based on “text summarization score” Furthermore, we propose a basic idea for SSCG and give a significantly improved scheme to satisfy the security guarantee of searchable symmetric encryption (SSE) Finally, we choose a real-world dataset, ie, the CNN dataset to test our scheme The results obtained from the experiment show the effectiveness of our proposed scheme