Artificial intelligence based ensemble approach for intrusion detection systems

Internet attacks pose a severe threat to most of the online resources and are a prime concern of security administrators these days. In spite of many efforts, the security techniques are unable to detect the intrusions accurately. Most of the methods suffer from the limitations of a high false positive rate, low detection rate and provide one solution which lacks the classification trade-offs. In this work, an effective two-stage method is proposed to produce a pool of non-dominating solutions or Pareto optimal solutions as base models and their ensembles for detecting the intrusions accurately. It generates Pareto optimal solutions to a chromosome structure in stage 1 formulating Pareto front. Whereas, another approximation to the Pareto front of optimal solutions is made to obtain non-dominating ensembles in the second stage. The final prediction ensemble solutions are computed from individual predictions using majority voting approach. Applicability of the suggested method is validated using benchmark dataset NSL-KDD dataset. The experimental results show that the recommended method provides better results than conventional ensemble techniques. The recommended method is also adequate to generate Pareto optimal solutions that address the issue of improving detection accuracy for minority as well as majority attack classes along with handling classification tradeoff problem. The proposed method resulted detection accuracy of 97% with FPR of 2% for KDD dataset respectively. The most attractive feature of the proposed method is that both generation of base classifier and their ensemble thereof are multi-objective in nature addressing the issue of low detection accuracy and classification tradeoffs.     

Perceptual visual quality assessment using deeply-learned gaze shifting kernel

Image quality assessment (IQA) is a useful technique in computer vision and machine intelligence. It is widely applied in image retrieval, image clustering and image recognition. IQA algorithms generally rely on human visual system (HVS), which can reflect how human perceive salient regions in the image. In this paper, we leverage both low-level features and high-level semantic features to select salient regions, which will be concatenated to form GSPs by the designed saliency-constraint algorithm to mimic human visual system. We design an enhanced IQA index based on the GSPs to calculate the simialrity between reference image and test image to achieve image quality assessment. Experiments demonstrate that our IQA method can achieve satisfactory performance.     

基于小结构体的无线传感器网络部署算法

目前无线传感器网络中节点的部署主要采用基于Voronoi图的算法,在使用Voronoi算法进行部署的过程中由于参与部署的节点数量多,算法的复杂度高,导致算法的迭代时间较长。为解决节点部署中算法迭代时间较长的问题,提出一种基于小结构体的部署算法（DABA）。首先,将节点组合成小结构体;然后,计算小结构体的中心位置坐标;最后,利用Voronoi图进行节点部署。所提算法对于部署区域存在障碍的情况仍然能有效进行部署。实验结果表明,DABA在部署时间方面能够比基于Voronoi图的算法减少三分之二。所提算法可明显减少算法的迭代时间,同时降低算法的复杂性。     

A novel combined model based on hybrid optimization algorithm for electrical load forecasting

Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people’s daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to sub-optimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning.     

面向异构IoT业务的LoRa网络自适应参数配置策略

前针对LoRa组网技术的研究主要受单一应用需求驱动, 可配置参数利用率低, 网络性能存在进一步优化的空间. 随着异构多类型IoT业务传输需求的日益增长, 优化网络的性能使之能够适应多类型业务显得尤为重要. 针对上述问题, 本文提出了一种基于模拟退火遗传算法的动态LoRa传输参数自适应配置策略, 在能耗约束的条件下可实现对多种异构业务的数据传输需求, 并可提高单网关网络可支持的终端设备数量和数据吞吐量. 基于LoRaSim的仿真结果表明: 与传统ADR (Adaptive Data Rate)相比, 本文所提方法的平均吞吐量提高了25.6%; 对于超过1000台终端设备的单网关LoRa网络, 当每个设备分组生成率小于1/100 s时, 网络的实际分组交付率(Packet Delivery Rate, PDR)超过90%. 该方法可适应多种异构业务的数据传输需求并在有效提高数据吞吐量的同时保证各业务的PDR.     

基于深度学习的三维数据分析理解方法研究综述

基于深度学习的三维数据分析理解是数字几何领域的一个研究热点.不同于基于深度学习的图像分析理解,基于深度学习的三维数据分析理解需要解决的首要问题是数据表达的多样性.相较于规则的二维图像,三维数据有离散表达和连续表达的方法,目前基于深度学习的相关工作多基于三维数据的离散表示,不同的三维数据表达方法与不同的数字几何处理任务对深度学习网络的要求也不同.本文首先汇总了常用的三维数据集与特定任务的评价指标,并分析了三维模型特征描述符.然后从特定任务出发,就不同的三维数据表达方式,对现有的基于深度学习的三维数据分析理解网络进行综述,对各类方法进行对比分析,并从三维数据表达方法的角度进一步汇总现有工作.最后基于国内外研究现状,讨论了亟待解决的挑战性问题,展望了未来发展的趋势.     

Geometry of Motion for Video Shakiness Detection

This paper presents a novel algorithm for automatically detecting global shakiness in casual videos. Perframe amplitude is computed by the geometry of motion, based on the kinematic model defined by inter-frame geometric transformations. Inspired by motion perception, we investigate the just-noticeable amplitude of shaky motion perceived by the human visual system. Then, we use the thresholding contrast strategy on the statistics of per-frame amplitudes to determine the occurrence of perceived shakiness. For testing the detection accuracy, a dataset of video clips is constructed with manual shakiness label as the ground truth. The experiments demonstrate that our algorithm can obtain good detection accuracy that is in concordance with subjective judgement on the videos in the dataset.     

一种基于协同进化的流水线向Seru系统转化方法

Seru生产系统是一种被广泛应用于电子制造产业的新型生产模式,但由于流水线向Seru系统转化问题（Line-seru conversion）包含有Seru构建与Seru调度两个相互耦合的子问题,现有算法难以在同时兼顾解的质量与计算效率的情况下对问题进行求解.因此,本文针对流水线向Seru系统转化问题的特点,提出了一种协同进化算法,即在进化算法中加入了协同机制,将Seru构建与Seru调度子问题作为两个子种群利用该机制进行协同进化,从而弥补了现有算法的不足.并且,本文还针对问题特点设计了个体基因编码方式,从而使规划获得的Seru生产系统具有更优的生产性能及均衡性能.实验表明,采用加入了协同机制的进化算法比传统解决流水线向Seru系统转化问题的方法具有更好的性能,本文所提的方法在最小化产品流通时间和劳动时间有较好的性能表现,并且具有较高的计算效率.     

Depth-aware blending of smoothed images for Bokeh effect generation

Bokeh effect is used in photography to capture images where the closer objects look sharp and everything else stays out-of-focus. Bokeh photos are generally captured using Single Lens Reflex cameras using shallow depth-of-field. Most of the modern smartphones can take bokeh images by leveraging dual rear cameras or a good auto-focus hardware. However, for smartphones with single-rear camera without a good auto-focus hardware, we have to rely on software to generate bokeh images. This kind of system is also useful to generate bokeh effect in already captured images. In this paper, an end-to-end deep learning framework is proposed to generate high-quality bokeh effect from images. The original image and different versions of smoothed images are blended to generate Bokeh effect with the help of a monocular depth estimation network. The model is trained through three phases to generate visually pleasing bokeh effect. The proposed approach is compared against a saliency detection based baseline and a number of approaches proposed in AIM 2019 Challenge on Bokeh Effect Synthesis. Extensive experiments are shown in order to understand different parts of the proposed algorithm. The network is lightweight and can process an HD image in 0.03 s. This approach ranked second in AIM 2019 Bokeh effect challenge-Perceptual Track.     

基于可变局部边缘模式的绿色植物物种识别

绿色植物物种识别在生态环境保护、中药制取、农业与园艺应用等方面有着重要的应用前景和潜在的经济价值。边缘是一种直观、简单、有效的对象识别特征,文中针对传统边缘算子方向少,尺度单一,操作不灵活等缺点,使用一种具有多尺度、多方向属性的圆形局部边缘模式算子(varied local edge pattern,VLEP)提取植物图像的边缘特征,同时考虑阈值细分的思想,在自建的绿色植物物种数据库上进行的实验结果表明,该算法不仅可以弥补传统算子由于边缘方向少、尺度单一导致丢失边缘信息的缺陷,同时可以有效用于绿色植物物种识别。