Robust Detection of Moving Human Target Behind Wall via Impulse through-Wall Radar

Through-wall human target detection is highly desired in military applications. We have developed an impulse through-wall radar (TWR) to address this problem. In order to obtain a robust detection performance, firstly we adopt the exponential average background subtraction (EABS) method to mitigate clutters and improve the signal-to-clutter ratio (SCR). Then, different from the conventional constant false alarm rate (CFAR) methods that are applied along the fast-time dimension, we propose a new CFAR method along the slow-time dimension to resist the residual clutters in the clutter mitigation output because of timing jitters, based on the presence of a larger relative variation of human target moving in and out in comparison with that of residual clutters in the slow-time dimension. The proposed method effectively solves the false alarm issue caused by residual clutters in the conventional CFAR methods, and obtains robust detection performance. Finally, different through-wall experiments are provided to verify the proposed method.Defence Science Journal, 2013, 63(6), pp.636-642, DOI:http://dx.doi.org/10.14429/dsj.63.576

Melatonin enhances the anti-tumor effect of fisetin by inhibiting COX-2/iNOS and NF-κB/p300 signaling pathways.

Melatonin is a hormone identified in plants and pineal glands of mammals and possesses diverse physiological functions. Fisetin is a bio-flavonoid widely found in plants and exerts antitumor activity in several types of human cancers. However, the combinational effect of melatonin and fisetin on antitumor activity, especially in melanoma treatment, remains unclear. Here, we tested the hypothesis that melatonin could enhance the antitumor activity of fisetin in melanoma cells and identified the underlying molecular mechanisms. The combinational treatment of melanoma cells with fisetin and melatonin significantly enhanced the inhibitions of cell viability, cell migration and clone formation, and the induction of apoptosis when compared with the treatment of fisetin alone. Moreover, such enhancement of antitumor effect by melatonin was found to be mediated through the modulation of the multiply signaling pathways in melanoma cells. The combinational treatment of fisetin with melatonin increased the cleavage of PARP proteins, triggered more release of cytochrome-c from the mitochondrial inter-membrane, enhanced the inhibition of COX-2 and iNOS expression, repressed the nuclear localization of p300 and NF-κB proteins, and abrogated the binding of NF-κB on COX-2 promoter. Thus, these results demonstrated that melatonin potentiated the anti-tumor effect of fisetin in melanoma cells by activating cytochrome-c-dependent apoptotic pathway and inhibiting COX-2/iNOS and NF-κB/p300 signaling pathways, and our study suggests the potential of such a combinational treatment of natural products in melanoma therapy

Deep Learning Application in Security and Privacy - Theory and Practice:A Position Paper

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors have evolved to design exploitation techniques that circumvent protection. With the ever-increasing complexity of technology infrastructure, security and privacy-preservation specialists have started to look for adaptable and flexible protection methods that can evolve (potentially autonomously) as the adversarial actor changes its techniques. For this, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) were put forward as saviours. In this paper, we look at the promises of AI, ML, and DL stated in academic and industrial literature and evaluate how realistic they are. We also put forward potential challenges a DL based security and privacy protection technique has to overcome. Finally, we conclude the paper with a discussion on what steps the DL and the security and privacy-preservation community have to take to ensure that DL is not just going to be hype, but an opportunity to build a secure, reliable, and trusted technology infrastructure on which we can rely on for so much in our lives

Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems

We propose Februus; a new idea to neutralize highly potent and insidious Trojan attacks on Deep Neural Network (DNN) systems at run-time. In Trojan attacks, an adversary activates a backdoor crafted in a deep neural network model using a secret trigger, a Trojan, applied to any input to alter the model's decision to a target prediction---a target determined by and only known to the attacker. Februus sanitizes the incoming input by surgically removing the potential trigger artifacts and restoring the input for the classification task. Februus enables effective Trojan mitigation by sanitizing inputs with no loss of performance for sanitized inputs, Trojaned or benign. Our extensive evaluations on multiple infected models based on four popular datasets across three contrasting vision applications and trigger types demonstrate the high efficacy of Februus. We dramatically reduced attack success rates from 100% to near 0% for all cases (achieving 0% on multiple cases) and evaluated the generalizability of Februus to defend against complex adaptive attacks; notably, we realized the first defense against the advanced partial Trojan attack. To the best of our knowledge, Februus is the first backdoor defense method for operation at run-time capable of sanitizing Trojaned inputs without requiring anomaly detection methods, model retraining or costly labeled data.Comment: 16 pages, to appear in the 36th Annual Computer Security Applications Conference (ACSAC 2020

Towards interpreting recurrent neural networks through probabilistic abstraction

National Research Foundation (NRF) Singapore under its AI Singapore Programm

Quantitative Identification of Water Sources of Coalbed Methane Wells, Based on the Hydrogen and Oxygen Isotopes of Produced Water—A Case of the Zhijin Block, South China

The quantitative identification of water sources is an important prerequisite for objectively evaluating the degree of aquifer interference and predicting the production potential of coalbed methane (CBM) wells. However, this issue has not been solved yet, and water sources are far from being completely understood. Stable water isotopes are important carriers of water source information, which can be used to identify the water sources for CBM wells. Taking the Zhijin block in the Western Guizhou Province as an example, the produced water samples were collected from CBM wells. The relationships between the stable isotopic compositions of the produced water samples and the production data were quantitatively analyzed. The following main conclusions were obtained. (1) The δD and δ18O values of the produced water samples were between −73.37‰ and −27.56‰ (average −56.30‰) and between −11.04‰ and −5.93‰ (average −9.23‰), respectively. The water samples have D-drift characteristics, showing the dual properties of atmospheric precipitation genesis and water–rock interaction modification of the produced water. An index d was constructed to enable the quantitative characterization of the degree of D-drift of the produced water. (2) The stable isotopic compositions of produced water showed the control of the water sources on the CBM productivity. The probability of being susceptible to aquifer interference increased with the increasing span of the producing seam combination, reflected in the lowering δD and δ18O values and the decreasing gas productivity. (3) Three types of water, namely, static water, dynamic water, and mixed water, were identified. The characteristic values of the isotopic compositions of the static and dynamic water were determined. Accordingly, a quantitative identification method for the produced water sources was constructed, based on their stable isotopic compositions. The identification results have a clear correlation with the gas production, and the output of the static water contributes to the efficient CBM production. The method for the quantitative identification of the water sources proposed in this study, can help to improve the CBM development efficiency and optimize the drainage technology

Quantitative Identification of Water Sources of Coalbed Methane Wells, Based on the Hydrogen and Oxygen Isotopes of Produced Water—A Case of the Zhijin Block, South China

The quantitative identification of water sources is an important prerequisite for objectively evaluating the degree of aquifer interference and predicting the production potential of coalbed methane (CBM) wells. However, this issue has not been solved yet, and water sources are far from being completely understood. Stable water isotopes are important carriers of water source information, which can be used to identify the water sources for CBM wells. Taking the Zhijin block in the Western Guizhou Province as an example, the produced water samples were collected from CBM wells. The relationships between the stable isotopic compositions of the produced water samples and the production data were quantitatively analyzed. The following main conclusions were obtained. (1) The δD and δ18O values of the produced water samples were between −73.37‰ and −27.56‰ (average −56.30‰) and between −11.04‰ and −5.93‰ (average −9.23‰), respectively. The water samples have D-drift characteristics, showing the dual properties of atmospheric precipitation genesis and water–rock interaction modification of the produced water. An index d was constructed to enable the quantitative characterization of the degree of D-drift of the produced water. (2) The stable isotopic compositions of produced water showed the control of the water sources on the CBM productivity. The probability of being susceptible to aquifer interference increased with the increasing span of the producing seam combination, reflected in the lowering δD and δ18O values and the decreasing gas productivity. (3) Three types of water, namely, static water, dynamic water, and mixed water, were identified. The characteristic values of the isotopic compositions of the static and dynamic water were determined. Accordingly, a quantitative identification method for the produced water sources was constructed, based on their stable isotopic compositions. The identification results have a clear correlation with the gas production, and the output of the static water contributes to the efficient CBM production. The method for the quantitative identification of the water sources proposed in this study, can help to improve the CBM development efficiency and optimize the drainage technology

Bits Learning: User-Adjustable Privacy Versus Accuracy in Internet Traffic Classification

During the past decade, a great number of machine learning (ML)-based methods have been studied for accurate traffic classification. Flow features such as the discretizations of the first five packet sizes (PS) and flow ports (FP) are considered the best discriminators for per-flow classification. For the first time, this letter proposes to treat the first n-bits of a flow (BitFlow) as features and compares its overall performance with the well-known ACAS (automated construction of application signatures) that takes the first n-bytes of a flow (ByteFlow) as features. The results show that BitFlow achieves not only a higher classification accuracy but also 1-3 orders of magnitude faster speed than ACAS in training and classifying. More importantly, this letter also proposes to treat the first n-bits of each of the first few packet payloads (BitPack) as features, which enables a user-adjustable tradeoff between user privacy protection and classification accuracy maximization. The experiments show that BitPack can significantly outperform BitFlow, PS, and FP

Experimental and Numerical Studies on Permeability Properties of Thermal Damaged Red Sandstone under Different Confining Pressures

The stability and safety of underground rock mass engineering are closely related to the permeability process of fluids and permeability properties of rocks. To reveal the flow behavior of fluid in thermal damaged rock, first, a rock seepage testing system was applied to study the permeability properties of red sandstone specimens after different high-temperature treatments from 200 to 800°C under different confining pressures of 10 to 30 MPa. Meanwhile, the microstructures of the red sandstone specimens were characterized by the mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM). Then, the permeability process of pore water pressure and the flow form of fluid also were investigated by the numerical modeling method. The results show that the permeability properties of red sandstone specimens after high-temperature exposure follow linear Darcy’s law, and the relation between confining pressures and equivalent permeability coefficient (K0 ) can be described by a power function. Besides, the phenomenon that microscopic structural deterioration is intensified with increasing temperature and the average pore size and porosity of the red sandstone specimens are both power functions is related to the equivalent permeability coefficient. Furthermore, the results of numerical modeling indicated that the flow field within the range affected by confining pressures gradually becomes stable and orderly from disorder, and flow lines of the fluid become smooth and straight, and perpendicular to the isosurface of pore water pressure as time goes by. Moreover, the nonlinear correlation between pore water pressure and seepage path length changes to a linear correlation, which is consistent with linear Darcy’s law