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Tse C.S.,Chinese University of Hong Kong | Chang J.F.,Guangdong University of Education | Fung A.W.T.,Chinese University of Hong Kong | Lam L.C.W.,Chinese University of Hong Kong | And 3 more authors.
International Psychogeriatrics | Year: 2015

Background: With the proportion of older adults in Hong Kong projected to double in size in the next 30 years, it is important to develop measures for detecting individuals in the earliest stage of Alzheimer's disease (AD, 0.5 in Clinical Dementia Rating, CDR). We tested the utility of a non-verbal prospective memory task (PM, ability to remember what one has to do when a specific event occurs in the future) as an early marker for AD in Hong Kong Chinese. Methods: A large community dwelling sample of older adults who are healthy controls (CDR 0, N = 125), in the earliest stage of AD (CDR 0.5, N = 125), or with mild AD (CDR 1, N = 30) participated in this study. Their reaction time/accuracy data were analyzed by mixed-factor analyses of variance to compare the performance of the three CDR groups. Logistic regression analyses were performed to test the discriminative power of these measures for CDR 0 versus 0.5 participants. Results: Prospective memory performance declined as a function of AD severity: CDR 0 > CDR 0.5 > CDR 1, suggesting the effects of early-stage AD and AD progression on PM. After partialling out the variance explained by psychometric measures (e.g., ADAS-Cog), reaction time/accuracy measures that reflected the PM still significantly discriminated between CDR 0 versus 0.5 participants in most of the cases. Conclusion: The effectiveness of PM measures in discriminating individuals in the earliest stage of AD from healthy older adults suggests that these measures should be further developed as tools for early-stage AD discrimination. Copyright © International Psychogeriatric Association 2014. Source

Xu Q.,South China Normal University | Wu J.,South China Normal University | Chen Q.,Guangdong University of Education
Mathematical Problems in Engineering | Year: 2014

Personalized recommended method is widely used to recommend commodities for target customers in e-commerce sector. The core idea of merchandise personalized recommendation can be applied to financial field, which can also achieve stock personalized recommendation. This paper proposes a new recommended method using collaborative filtering based on user fuzzy clustering and predicts the trend of those stocks based on money flow. We use M/G/1 queue system with multiple vacations and server close-down time to measure practical money flow. Based on the indicated results of money flow, we can select the more valued stock to recommend to investors. The experimental results show that the proposed method provides investors with reliable practical investment guidance and receiving more returns. © 2014 Qingzhen Xu et al. Source

Lin S.,Sun Yat Sen University | Guo Y.,Sun Yat Sen University | Guo Y.,Guangdong University of Finance | Liang Y.,South China Agricultural University | And 2 more authors.
Proceedings of the 2015 IEEE International Conference on Networking, Architecture and Storage, NAS 2015 | Year: 2015

We proposed a new method for 3D model retrieval based on skeletons. As we know a sketch is drawn on a two dimensional plane while models are three dimensional. For better comparison we choose the same dimension for them. For simpler user input we use the front view of the skeleton to represent 3D models, in this way 3D models can be represented in 2D form. We get the skeleton of model through Skeleton Extraction algorithm based on mesh simplification and mesh contraction and get the front view of the skeleton by mapping to two dimensional spaces. The model compared with the Model libraries by Feature description and matching to find similar models on shape and topology structure after getting the feature extraction of the model by the gradient histogram matching algorithm. Experiments showed that most users can find the 3D models they want with our system. © 2015 IEEE. Source

Chen G.,Guangdong University of Education | Chen Q.,Guangdong University of Education | Zhang D.,Sun Yat Sen University
Proceedings - 2014 International Conference on Digital Home, ICDH 2014 | Year: 2014

In this paper we proposed a dictionary learning and dimensionality reduction (DLDR) scheme for image steganalysis. We construct a structural discriminative dictionary which is learned from the reduced dimension space and exploit the discriminative information in stego-images. Simulation results verify the effectiveness of the proposed approach and the performance is considerable. Both the dictionary and sparse coding can be correctly computed and the learned dictionary using the proposed method can be used to improve image steganlysis. © 2014 IEEE. Source

Chen G.,Guangdong University of Education | Chen Q.,Guangdong University of Education | Zhang D.,Sun Yat Sen University
2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015 | Year: 2015

Image steganalysis based on supervised distance metric learning is to find an appropriate measure of similarity between image features where the distribution discrepancy between cover-images and stego-images are analyzed in the reduced dimensional space. Our approach is novel in that it combines the merits of weight metric learning and image distribution analysis in reduced dimension space. By this learning metrics, we exploit a new steganalysis metric to discriminate stego-images from clean images. The experiment results show the effectiveness of the propose approach for some data hiding method. © 2015 IEEE. Source

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