Shandong Normal University

About: Shandong Normal University is a education organization based out in Jinan, Shandong, China. It is known for research contribution in the topics: Laser & Catalysis. The organization has 12378 authors who have published 12576 publications receiving 174572 citations.

DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks

Abstract: Our daily lives have been immersed in widespread location-based social networks (LBSNs). As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing users' decisions in urban computing environments. To provide reliable information and improve the experience for legitimate users, we design and implement DeepScan, a malicious account detection system for LBSNs. Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities, and exploits a supervised machine-learning-based model for detection. Using real traces collected from Dianping, a representative LBSN, we demonstrate that DeepScan can achieve excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system.

Highly ordered arrays of hat-shaped hierarchical nanostructures with different curvatures for sensitive SERS and plasmon-driven catalysis

Abstract: Regulation of hot spots exhibits excellent potential in many applications including nanolasers, energy harvesting, sensing, and subwavelength imaging. Here, hat-shaped hierarchical nanostructures with different space curvatures have been proposed to enhance hot spots for facilitating surface-enhanced Raman scattering (SERS) and plasmon-driven catalysis applications. These novel nanostructures comprise two layers of metal nanoparticles separated by hat-shaped MoS 2 films. The fabrication of this hybrid structure is based on the thermal annealing and thermal evaporation of self-assembled polystyrene spheres, which are convenient to control the metal particle size and the curvature of hat-shaped nanostructures. Based on the narrow gaps produced by the MoS 2 films and the curvature of space, the constructed platform exhibits superior SERS capability and achieves ultrasensitive detection for toxic molecules. Furthermore, the surface catalytic conversion of p-nitrothiophenol (PNTP) to p, p′-dimercaptobenzene (DMAB) was in situ monitored by the SERS substrate. The mechanism governing this regulation of hot spots is also investigated via theoretical simulations.

Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram.

Abstract: We propose an innovative registration method to correct motion artifacts for wide-field optical coherence tomography angiography (OCTA) acquired by ultrahigh-speed swept-source OCT (>200 kHz A-scan rate). Considering that the number of A-scans along the fast axis is much higher than the number of positions along slow axis in the wide-field OCTA scan, a non-orthogonal scheme is introduced. Two en face angiograms in the vertical priority (2 y-fast) are divided into microsaccade-free parallel strips. A gross registration based on large vessels and a fine registration based on small vessels are sequentially applied to register parallel strips into a composite image. This technique is extended to automatically montage individual registered, motion-free angiograms into an ultrawide-field view.

Arsenic, lead, and cadmium bioaccessibility in contaminated soils: Measurements and validations

Abstract: Ingestion of metal-contaminated soils poses risks to human health. To accurately assess the risks, in vitro assays have been developed to measure metal bioaccessibility. However, they often differ ...

A novel coverless information hiding method based on the average pixel value of the sub-images

Abstract: In traditional information hiding methods, the secret information is embedded into the carriers, which will inevitably leave traces of modification on the carriers In those methods, the modified images can be easily detected by some steganalysis algorithm and thus the security can not be guaranteed To address this problem, the concept of coverless information hiding is proposed However, general coverless information hiding method has a lower information hiding capacity In this paper, we propose a novel coverless information hiding method based on the average pixel values of sub-images We generate hash sequences by a hashing algorithm and realize the secret information hiding through mapping relationship In the first place, we build a dictionary and a hash array Then we map the dictionary and the hash array through mapping relationship Furthermore, we build a multi-level index structure for retrieving the stego-images efficiently The experimental results and analysis show that our method has a good performance in the capacity of information hiding, the security, the robustness to image attacks and the hiding success rate based on different image databases