Nanocomposites of CsPbBr3 perovskite quantum dots embedded in Gd2O3:Eu3+ hollow spheres for LEDs application

Gd₂O₃:Eu³⁺@CsPbBr₃ quantum dots (QDs) mesoporous hollow nanocomposites with good luminescent properties and high stability were built. Among which, the hollow Gd₂O₃:Eu³⁺ spheres and CsPbBr₃ QDs were prepared by urea homogeneous precipitation and hot-injection method, respectively. Finally, the Gd₂O₃:Eu³⁺@CsPbBr₃ QDs shell–core compounds were constructed through mechanical stirring. The structure, morphology, stability and luminescent properties were studied by Fourier transform infrared spectroscopy (FT-IR), differential scanning calorimetry/thermogravity (DSC/TG), X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), photoluminescence excitation/photoluminescence (PLE/PL) and life decay tools. Compared to the original CsPbBr₃ QDs, Gd₂O₃:Eu³⁺@CsPbBr₃ QDs display better photostability, thermal stability and current stability. The resulting Gd₂O₃:Eu³⁺@CsPbBr₃ QDs composite exhibits good yellow emission. The Gd₂O₃:Eu³⁺@CsPbBr₃ QDs mixed silicone resin was directly coated on the blue LED chip, then the w-LED device with the color coordinate of (0.31, 0.32) was successfully assembled. The Gd₂O₃:Eu³⁺@CsPbBr₃ QDs compounds with excellent luminescent properties and stability are expected to be widely used in lighting and display areas.     

DECOUPLER DESIGN AND CONTROL SYSTEM TUNING BY INA FOR DISTILLATION COMPOSITION CONTROL†

Six distillation column models have been used in studies of the usefulness of the Inverse Nyquist Array (INA) method for design of dual composition control for distillation. Five of the column models are experimental, one describing an industrial column, the others four pilot plants. One column model is obtained by modeling from first principles.

The control strategies investigated and compared are multiloop SISO, 2-way decoupling and l-way decoupling. The control variables are the standard ones, i.e. reflux flow and boilup.

In most of the cases INA has been found to be a useful tool for design of the loops and for comparison of the different control approaches. INA has also been found to be useful for simultaneous tuning of the decouplers and the feedback controllers.

For 2-way decoupling the criterion to minimize interaction at the critical frequency for each primary feedback loop has been found useful in most, but not all, cases. The scheme can be designed by pure gains in the decouplers and there is no reason to introduce dynamics into the decouplers—the systems studied are already sufficiently rich in dynamics.

INA has been found to be a suitable vehicle for the choice between the two possible l-way decoupling schemes.

With the design approach taken, l-way decoupling has been found to provide considerably better control quality than 2-way decoupling in two of the six systems, the differences in control quality being small in the other cases.

The effect of model mismatching on the results, caused e.g. by process nonlinearities, is discussed and simple rules to decrease the parameter sensitivity for 2-way decoupling schemes are given. When model mismatching is considered, the advantage of l-way decoupling over 2-way decoupling seems to increase.

One deficiency of the INA design as used in this paper is that it does not single out the design which is to be preferred on the grounds of robustness.     

Data-mining based SQL injection attack detection using internal query trees

Detecting SQL injection attacks (SQLIAs) is becoming increasingly important in database-driven web sites. Until now, most of the studies on SQLIA detection have focused on the structured query language (SQL) structure at the application level. Unfortunately, this approach inevitably fails to detect those attacks that use already stored procedure and data within the database system. In this paper, we propose a framework to detect SQLIAs at database level by using SVM classification and various kernel functions. The key issue of SQLIA detection framework is how to represent the internal query tree collected from database log suitable for SVM classification algorithm in order to acquire good performance in detecting SQLIAs. To solve the issue, we first propose a novel method to convert the query tree into an n-dimensional feature vector by using a multi-dimensional sequence as an intermediate representation. The reason that it is difficult to directly convert the query tree into an n-dimensional feature vector is the complexity and variability of the query tree structure. Second, we propose a method to extract the syntactic features, as well as the semantic features when generating feature vector. Third, we propose a method to transform string feature values into numeric feature values, combining multiple statistical models. The combined model maps one string value to one numeric value by containing the multiple characteristic of each string value. In order to demonstrate the feasibility of our proposals in practical environments, we implement the SQLIA detection system based on PostgreSQL, a popular open source database system, and we perform experiments. The experimental results using the internal query trees of PostgreSQL validate that our proposal is effective in detecting SQLIAs, with at least 99.6% of the probability that the probability for malicious queries to be correctly predicted as SQLIA is greater than the probability for normal queries to be incorrectly predicted as SQLIA. Finally, we perform additional experiments to compare our proposal with syntax-focused feature extraction and single statistical model based on feature transformation. The experimental results show that our proposal significantly increases the probability of correctly detecting SQLIAs for various SQL statements, when compared to the previous methods.     

基于并查集和边缘检测模板的非理想虹膜定位

非理想虹膜图像往往存在虹膜边缘模糊、灰度变化不均匀、位置偏移及光斑干扰等问题, 这些问题的存在会在一定程度上影响虹膜内、外边界定位的准确率. 针对这个问题, 本文提出采用并查集和边缘检测模板的方法来对非理想虹膜进行内、外边界定位. 内边界定位是首先采用并查集方法完成瞳孔区域粗定位, 然后采用Hough变换对瞳孔进行精确定位; 外边界定位是先利用一系列边缘检测模板大致确定外圆的位置, 然后再依据外边界附近圆环内边缘点的密度来最终完成外边界的精确定位. 实验结果表明, 对于非理想虹膜图像, 本文方法的定位正确率和定位速度均高于其他同类方法.     

基于FPGA实现AES的侧信道碰撞攻击

为了解决攻击点在能量迹中具体位置的识别问题,在对侧信道碰撞攻击技术研究的基础上,提出了通过计算能量迹中每个采样点的方差来识别攻击点的方差检查技术。并利用基于相关系数的碰撞检测方法,对一种AES的FPGA实现实施了攻击。实验结果表明,方差检查技术可以有效地识别攻击点在能量迹中的具体位置。     

Face detection by structural models

Despite the successes in the last two decades, the state-of-the-art face detectors still have problems in dealing with images in the wild due to large appearance variations. Instead of leaving appearance variations directly to statistical learning algorithms, we propose a hierarchical part based structural model to explicitly capture them. The model enables part subtype option to handle local appearance variations such as closed and open month, and part deformation to capture the global appearance variations such as pose and expression. In detection, candidate window is fitted to the structural model to infer the part location and part subtype, and detection score is then computed based on the fitted configuration. In this way, the influence of appearance variation is reduced. Besides the face model, we exploit the co-occurrence between face and body, which helps to handle large variations, such as heavy occlusions, to further boost the face detection performance. We present a phrase based representation for body detection, and propose a structural context model to jointly encode the outputs of face detector and body detector. Benefit from the rich structural face and body information, as well as the discriminative structural learning algorithm, our method achieves state-of-the-art performance on FDDB, AFW and a self-annotated dataset, under wide comparisons with commercial and academic methods.     

Discourse cues to deception in the case of multiple receivers

Behavioral cues to deception are instrumental in detecting deception. As one of the primary sources of deception behavior, text has been analyzed at the level of sub-sentence or message but not the discourse of interaction. Additionally, empirical studies on cues to deception in the case of multiple receivers remain nonexistent. To fill these voids, we propose a discourse framework and six hypotheses about deception behaviors in a multi-receiver environment. The deception behaviors are operationalized by discourse features based on an analysis of real-world data. The results of statistical analysis validate the efficacy of discourse features in discriminating deceivers from truth-tellers.     

Detecting stable locality-aware predicates

In a large-scale locality-driven network such as in modular robotics and wireless sensor networks, knowing the state of a local area is sometimes necessary due to either interactions being local and driven by neighborhood proximity or the users being interested in the state of a certain region. We define locality-aware predicates (LAP) that aim at detecting a predicate within a specified area. We model the area of interest as the set of processes that are within a breadth-first search tree (BFST) of height k$k$ rooted at the initiator process. Although a locality-aware predicate specifies a predicate only within a local area, observing the area consistently requires considering the entire system in a consistent manner. This raises the challenge of making the complexities of the corresponding predicate detection algorithms scale-free, i.e., independent of the size of the system. Since all existing algorithms for getting a consistent view of the system require either a global snapshot of the entire system or vector clocks of the size of the system, a new solution is needed. We focus on stable LAP, which are those LAP that remain true once they become true. We propose a scale-free algorithm to detect stable LAP within a k$k$-height BFST. Our algorithm can detect both stable conjunctive LAP and stable relational LAP. In the process of designing our algorithm, we also propose the first distributed algorithm for building a BFST within an area of interest in a graph, and the first distributed algorithm for recording a consistent sub-cut within the area of interest. This paper demonstrates that LAPs are a natural fit for detecting distributed properties in large-scale distributed systems, and stable LAPs can be practically detected at low cost.     

Kokkos: Enabling manycore performance portability through polymorphic memory access patterns

The manycore revolution can be characterized by increasing thread counts, decreasing memory per thread, and diversity of continually evolving manycore architectures. High performance computing (HPC) applications and libraries must exploit increasingly finer levels of parallelism within their codes to sustain scalability on these devices. A major obstacle to performance portability is the diverse and conflicting set of constraints on memory access patterns across devices. Contemporary portable programming models address manycore parallelism (e.g., OpenMP, OpenACC, OpenCL) but fail to address memory access patterns. The Kokkos C++ library enables applications and domain libraries to achieve performance portability on diverse manycore architectures by unifying abstractions for both fine-grain data parallelism and memory access patterns. In this paper we describe Kokkos’ abstractions, summarize its application programmer interface (API), present performance results for unit-test kernels and mini-applications, and outline an incremental strategy for migrating legacy C++ codes to Kokkos. The Kokkos library is under active research and development to incorporate capabilities from new generations of manycore architectures, and to address a growing list of applications and domain libraries.