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Secure Biometric Hashing by Random Fusion of Global and Local Features
Ou, Yang,Rhee, Kyung-Hyune Korea Multimedia Society 2010 멀티미디어학회논문지 Vol.13 No.6
In this paper, we present a secure biometric hashing scheme for face recognition by random fusion of global and local features. The Fourier-Mellin transform and Radon transform are adopted respectively to form specialized representation of global and local features, due to their invariance to geometric operations. The final biometric hash is securely generated by random weighting sum of both feature sets. A fourfold key is involved in our algorithm to ensure the security and privacy of biometric templates. The proposed biometric hash can be revocable and replaced by using a new key. Moreover, the attacker cannot obtain any information about the original biometric template without knowing the secret key. The experimental results confirm that our scheme has a satisfactory accuracy performance in terms of EER.
Ouyang, Dongyan,Hirakawa, Kazutaka Korean Society of Photoscience 2015 Rapid communication in photoscience Vol.4 No.2
Biomolecular photo-damaging activity of a water-soluble cationic porphyrin was examined using human serum albumin (HSA), a water-soluble protein as a target biomolecule model by a fluorometry. Dichlorophosphorus(V) tetraphenylporphyrin ($Cl_2P(V)TPP$), was synthesized and used as a photosensitizer. This porphyrin could bind to HSA and cause the photosensitized oxidation of HSA through the singlet oxygen generation and the oxidative photo-induced electron transfer (ET). Near infrared emission spectroscopy demonstrated the photosensitized singlet oxygen generation by this porphyrin. Decrement of the fluorescence lifetime of $Cl_2P(V)TPP$ by HSA supported the ET mechanism. Furthermore, the estimated Gibb's energy indicated that the ET mechanism is possible in the terms of energy. Because oxygen concentration in cancer cell is relatively low, ET mechanism is considered to be advantageous for photosensitizer of photodynamic therapy.
Determination and prediction of amino acid digestibility in brown rice for growing-finishing pigs
Ouyang Qing,Li Rui,Feng Ganyi,Hou Gaifeng,Jiang Xianji,Liu Xiaojie,Tang Hui,Long Ciming,Yin Jie,Yin Yulong 아세아·태평양축산학회 2024 Animal Bioscience Vol.37 No.8
Objective: The experiment aimed to determine the standardized ileal digestibility (SID) of crude protein (CP) and amino acids (AA) in 10 brown rice samples fed to pigs, and to construct predictive models for SID of CP and AA based on the physical characteristics and chemical composition of brown rice.Methods: Twenty-two cannulated pigs (initial body weight: 42.0±1.2 kg) were assigned to a replicated 11×3 incomplete Latin square design, including an N-free diet and 10 brown rice diets. Each period included 5 d adaptation and 2 d ileal digesta collection. Chromic oxide was added at 0.3% to all the diets as an indigestible marker for calculating the ileal CP and AA digestibility.Results: The coefficients of variation of all detected indices for physical characteristics and chemical composition, except for bulk weight, dry matter (DM) and gross energy, in 10 brown rice samples were greater than 10%. The SID of CP, lysine (Lys), methionine, threonine (Thr), and tryptophan (Trp) in brown rice was 77.2% (62.6% to 85.5%), 87.5% (80.3% to 94.3%), 89.2% (78.9% to 98.9%), 55.4% (46.1% to 67.6%) and 92.5% (86.3% to 96.3%), respectively. The best prediction equations for the SID of CP, Lys, Thr, and Trp were as following, SIDCP = –664.181+8.484×DM (R2 = 0.40), SIDLys = 53.126+6.031×ether extract (EE)+0.893×thousand-kernel volume (R2 = 0.66), SIDThr = 39.916+7.843×EE (R2 = 0.41), and SIDTrp = –361.588+4.891×DM+0.387×total starch (R2 = 0.85).Conclusion: Overall, a great variation exists among 10 sources of brown rice, and the thousand-grain volume, DM, EE, and total starch can be used as the key predictors for SID of CP and AA. Objective: The experiment aimed to determine the standardized ileal digestibility (SID) of crude protein (CP) and amino acids (AA) in 10 brown rice samples fed to pigs, and to construct predictive models for SID of CP and AA based on the physical characteristics and chemical composition of brown rice. Methods: Twenty-two cannulated pigs (initial body weight: 42.0±1.2 kg) were assigned to a replicated 11×3 incomplete Latin square design, including an N-free diet and 10 brown rice diets. Each period included 5 d adaptation and 2 d ileal digesta collection. Chromic oxide was added at 0.3% to all the diets as an indigestible marker for calculating the ileal CP and AA digestibility. Results: The coefficients of variation of all detected indices for physical characteristics and chemical composition, except for bulk weight, dry matter (DM) and gross energy, in 10 brown rice samples were greater than 10%. The SID of CP, lysine (Lys), methionine, threonine (Thr), and tryptophan (Trp) in brown rice was 77.2% (62.6% to 85.5%), 87.5% (80.3% to 94.3%), 89.2% (78.9% to 98.9%), 55.4% (46.1% to 67.6%) and 92.5% (86.3% to 96.3%), respectively. The best prediction equations for the SID of CP, Lys, Thr, and Trp were as following, SIDCP = –664.181+8.484×DM (R2 = 0.40), SIDLys = 53.126+6.031×ether extract (EE)+0.893×thousand-kernel volume (R2 = 0.66), SIDThr = 39.916+7.843×EE (R2 = 0.41), and SIDTrp = –361.588+4.891×DM+0.387×total starch (R2 = 0.85). Conclusion: Overall, a great variation exists among 10 sources of brown rice, and the thousand-grain volume, DM, EE, and total starch can be used as the key predictors for SID of CP and AA.
Opinion Objects Identification and Sentiment Analysis
Ouyang Chunping,Liu Yongbin,Zhang Shuqing,Yang Xiaohua 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6
Sentiment analysis of reviews has been the focus of recent research, which also has been attempted in different domains such as product reviews, movie reviews, and customer feedback reviews. Most sentiment analysis of reviews focused on extracting overall evaluation for a single product which makes difficult for a customer to know all the features of product and make a decision. Thus, mining this data, identifying the user opinions about different features and classify them is an important task. This paper is devoted to identify opinion object from short comments, and analyze sentiment of product based on features-level. CRFs model based on word embedding feature is adopted by identifying opinion object, which obtains a satisfied results. In addition, calculate rules based on syntax parsing are proposed to accomplish features-level sentiment analysis which extracts user’s opinion on many aspects. Experimental results using short comments of movies show the effectiveness of our approach.
( Ouyang Ping ),( Sun Mao ),( He Xuewen ),( Wang Kaiyu ),( Yin Zhongqiong ),( Fu Hualin ),( Li Yinglun ),( Geng Yi ),( Shu Gang ),( He Changliang ),( Liang Xiaoxia ),( Lai Weiming ),( Li Lixia ),( Zou 한국미생물 · 생명공학회 2017 Journal of microbiology and biotechnology Vol.27 No.1
Staphylococcus aureus (S. aureus) is a common gram-positive bacterium that causes serious infections in humans and animals. With the continuous emergence of methicillin-resistant S. aureus (MRSA) strains, antibiotics have limited efficacy in treating MRSA infections. Accordingly, novel agents that act on new targets are desperately needed to combat these infections. S. aureus alpha-hemolysin plays an indispensable role in its pathogenicity. In this study, we demonstrate that sclareol, a fragrant chemical compound found in clary sage, can prominently decrease alpha-hemolysin secretion in S. aureus strain USA300 at sub-inhibitory concentrations. Hemolysis assays, western-blotting, and RT-PCR were used to detect the production of alpha-hemolysin in the culture supernatant. When USA300 was co-cultured with A549 epithelial cells, sclareol could protect the A549 cells at a final concentration of 8 μg/ml. The protective capability of sclareol against the USA300-mediated injury of A549 cells was further shown by cytotoxicity assays and live/dead analysis. In conclusion, sclareol was shown to inhibit the production of S. aureus alpha-hemolysin. Sclareol has potential for development as a new agent to treat S. aureus infections.
Topic Sentiment Analysis in Chinese News
Ouyang Chunping,Zhou Wen,Yu Ying,Liu Zhiming,Yang Xiaohua 보안공학연구지원센터 2014 International Journal of Multimedia and Ubiquitous Vol.9 No.11
Sentiment analysis in news is different from normal text sentiment analysis. News usually have a specific topic, a focus semantic emotion, therefore, this paper, based on the principal of using Emotion Dependency Tuple (EDT) as the basic unit of news emotion analysis, resolves topic sentiment analysis in news into three progressive sub-problem, namely, topic sentence recognition, EDT extraction and topic sentiment analysis. We use an improved TF-IDF and cross entropy to extract feature set of topics. Then, based on space vector model, calculate the topic association of a sentence and extract topic sentence. Finally, we construct topic sentence based on EDT and complete clustering of news topic sentiment. This method is evaluated using COAE2014 dataset, and differential means shows that our results close to the best results. This shows that the topic based EDT could effectively improve performance of sentiment analysis in news.
LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19
Ouyang, Sizhuo,Wang, Yuxing,Zhou, Kaiyin,Xia, Jingbo Korea Genome Organization 2021 Genomics & informatics Vol.19 No.3
Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.