Privacy-Protected Facial Biometric Verification via Fuzzy Forest Learning

Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacyrelated applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.

Towards Possibilistic Fuzzy Answer Set Programming

Fuzzy answer set programming (FASP) is a generalization of answer set programming to continuous domains. As it can not readily take uncertainty into account, however, FASP is not suitable as a basis for approximate reasoning and cannot easily be used to derive conclusions from imprecise information. To cope with this, we propose an extension of FASP based on possibility theory. The resulting framework allows us to reason about uncertain information in continuous domains, and thus also about information that is imprecise or vague. We propose a syntactic procedure, based on an immediate consequence operator, and provide a characterization in terms of minimal models, which allows us to straightforwardly implement our framework using existing FASP solvers.

Fractionated Radiation Exposure of Rat Spinal Cords Leads to Latent Neuro- Inflammation in Brain, Cognitive Deficits,and Alterations in Apurinic Endonuclease 1

Ionizing radiation causes degeneration of myelin, the insulating sheaths of neuronal axons, leading to neurological impairment. As radiation research on the central nervous system has predominantly focused on neurons, with few studies addressing the role of glial cells, we have focused our present research on identifying the latent effects of single/ fractionated -low dose of low/ high energy radiation on the role of base excision repair protein Apurinic Endonuclease-1, in the rat spinal cords oligodendrocyte progenitor cells’ differentiation. Apurinic endonuclease-1 is predominantly upregulated in response to oxidative stress by low- energy radiation, and previous studies show significant induction of Apurinic Endonuclease-1 in neurons and astrocytes. Our studies show for the first time, that fractionation of protons cause latent damage to spinal cord architecture while fractionation of HZE (28Si) induce increase in APE1 with single dose, which then decreased with fractionation. The oligodendrocyte progenitor cells differentiation was skewed with increase in immature oligodendrocytes and astrocytes, which likely cause the observed decrease in white matter, increased neuro-inflammation, together leading to the observed significant cognitive defects.

Modelling of Evaporator in Waste Heat Recovery System using Finite Volume Method and Fuzzy Technique

The evaporator is an important component in the Organic Rankine Cycle (ORC)-based Waste Heat Recovery (WHR) system since the effective heat transfer of this device reflects on the efficiency of the system. When the WHR system operates under supercritical conditions, the heat transfer mechanism in the evaporator is unpredictable due to the change of thermo-physical properties of the fluid with temperature. Although the conventional finite volume model can successfully capture those changes in the evaporator of the WHR process, the computation time for this method is high. To reduce the computation time, this paper develops a new fuzzy based evaporator model and compares its performance with the finite volume method. The results show that the fuzzy technique can be applied to predict the output of the supercritical evaporator in the waste heat recovery system and can significantly reduce the required computation time. The proposed model, therefore, has the potential to be used in real time control applications.