Tainan University of Technology

About: Tainan University of Technology is a education organization based out in Tainan City, Taiwan. It is known for research contribution in the topics: Password & Authentication. The organization has 311 authors who have published 641 publications receiving 10826 citations.

Cryptanalysis and improvement of a ECDLP based proxy blind signature scheme

Abstract: The proxy blind signature scheme (PBSS), allows the authorized proxy signer to use his proxy private key to create a legal blind signature on behalf of original signer. Recently, Alghazzawi, Salim and Hasan (A-S-H), presented the elliptic curve cryptography (ECC) based PBSS. However, we found their scheme is vulnerable to a linkability attack, which shows that there was lacked of unlinkability. In this paper, the improvements are being done in this regards in such a manner that the proposed signature scheme satisfies all the security properties, including unlinkability property. The security of the proposed PBSS rely on the elliptic curve discrete logarithm problem (ECDLP). The signature scheme presented can be implemented in low power and small processor device such as smart card.

Hydrocarbon pneumonitis following fuel siphonage: A case report and literature review.

Abstract: Background People sometimes siphon fuel to fill their tanks. However, this is a potentially dangerous procedure and may cause hydrocarbon pneumonitis. We present the case of a patient with severe hydrocarbon pneumonitis after siphoning fuel. The patient underwent artificial ventilation and was admitted to hospital for 97 days. Methods We review the relevant literature for a better understanding of clinical features and management strategies for hydrocarbon pneumonitis following fuel siphonage. Results We reviewed 15 articles, which included 3 original articles and 12 case reports that reported the clinical features of fuel siphonage. In addition, we added our presented case for data analysis. A total of 40 cases were included in this review. The literature review found that hydrocarbon pneumonitis caused by fuel siphonage occurs worldwide and that most patients (80%) became symptomatic within 1 day of aspiration. Cough (70%), chest pain (62.5%), dyspnoea (55%), and fever (52.5%) presented in more than half of all patients. The right middle lobe (80%) was the predominantly involved lung field; more than one-third of patients (36.7%) showed the involvement of two lobes. Conclusion Patient history, computed tomographic scans of the chest, and bronchoalveolar lavage are the commonly used diagnostic tools. Supportive care remains the foundation of treatment, whereas antibiotics, steroids, and bronchoalveolar lavage are practical therapies. Patients' clinical improvement precedes the resolution of lesions on chest X-ray. Most complications arise from pulmonary lesions. The prognosis of patients suffering from hydrocarbon pneumonitis following fuel siphonage might be improved by accurate diagnosis and appropriate care.

Knowledge Discovery on In Vitro Fertilization Clinical Data Using Particle Swarm Optimization

Abstract: In vitro fertilization (IVF) is a medically assisted reproduction technique (ART) for treating infertility. During IVF procedures, a female patient requires hormone treatment to control ovulation, oocytes are taken from the patient and fertilized in vitro, and after fertilization, one or usually more resulting embryos are transferred into the uterus. Although IVF is considered as a method of last resort for infertile couples, the success rate is still low, which can be only as high as 40% for women under age of 30. In this study, we build a predictive model which takes into account a patient’s physiology and the results of the stages of an IVF cycle, to assist obstetricians and gynecologists in increasing success rate of IVF. The predictive model is based on a knowledge discovering technique incorporated with particle swarm optimization (PSO), which is a competitive heuristic technique for solving optimization task. This study uses the database of IVF cycles developed by a women and infants clinic in Taiwan as the foundation. A repertory grid is developed to help selecting attributes for the data mining technique. The results show that the proposed technique can exploit rules approved by the obstetrician/gynecologist and the assistant on both comprehensibility and justifiability.

High-efficiency face detection and tracking method for numerous pedestrians through face candidate generation

Abstract: This paper is dedicated to developing high-efficiency face detection and tracking method for big dynamic crowds or numerous pedestrians. Three modules constitute the proposed method, i.e., face candidate generation, face candidate verification, and face target tracking. In this work, face candidates are localized using the features of the face area, edge information, and skin color. Non-face parts in the face candidates are further verified by the C-SVM learning model and then removed, by which the face targets can be generated with lower computation-complexity and satisfactory accuracy than other approaches. Finally, the face targets are tracked by an efficient and reliable searching scheme for improving the effective face detection rate. Experimental results show that the average face detection rate (FDR) of 85%, average effective FDR of 95%, a frame rate of 28–66 frames per second (fps), and about 30 faces detected per frame are obtained from various test videos with big dynamic crowds or numerous pedestrians, indicating the feasibility of the proposed method to achieve unconstrained face detection with high-efficiency and cost-effectiveness. This result makes the proposed method more attractive for the video surveillance system as compared to other approaches, especially in the high computational complexity-based methods.