应用软件可信要素实现方法研究

软件可信是指软件系统能按照其设定目标所期望的方式运行,能适应环境和需求的变化,有抵御异常情况的能力,在受到干扰时仍能持续提供服务。为了验证软件在真实运行环境中的实际行为是否与其期望的功能相符,需要在软件中嵌入一系列可信要素。本文讨论了如何通过安全编译等手段在软件中加入可信要素,真实的监测软件运行的实际行为,并结合软件行为声明,提供对软件可信度量的数据基础。     

A general technique to embed non-uniform discontinuities into standard solid finite elements

A general technique to embed non-uniform displacement discontinuities into standard solid finite elements is presented. The technique is based on the decomposition of the kinematic fields into a component related to the deformation of the solid portion of the element and one related to the rigid-body motion due to a displacement discontinuity. This decomposition simplifies the incorporation of discontinuity interfaces and provides a suitable framework to account for non-uniform discontinuity modes. The present publication addresses two families of finite element formulations: displacement-based and stress hybrid finite element.     

An overview of rate control techniques in HEVC and SHVC video encoding

Video standards are crucial for exchanging video content, enabling a myriad of services and supporting a wide variety of devices ranging from personal devices to clouds and IoT. One of the core requirements in video standards is the rate control that regulates the bit allocation and picture quality. This paper presents an overview of rate control techniques in the HEVC video coding standard. While providing an insight into the rate control mechanism specific to HEVC, it describes the basic operating principle of rate control algorithms, including their essential parameter, outputs, and performance measures. We review rate control in past coding standards and bring out the basic features of HEVC that drive the need for new rate control algorithms. Alongside, we delineate the Rate-Distortion model-based taxonomy of various algorithms, including their classification criteria. The paper gives out another classification of the rate control algorithms based on their basic principle and mechanisms. The article also explains the scalable extension of HEVC, namely SHVC, while highlighting some of the possible SHVC rate control design challenges. Finally, we present some of the unresolved research issues in HEVC rate control and outline possible future research directions.

     

The use of colour in language syntax analysis

The use of colour to reflect the syntax of typed-in language statements can make a language processor much more user-friendly. This paper shows how colour has been used in MicroCODIL and suggests ways in which the approach could be applied to other systems.     

HAS QoE prediction based on dynamic video features with data mining in LTE network

Evaluation of HTTP adaptive streaming (HAS) quality of experience (QoE) over LTE network is a challenging topic because of multi-segment and multi-rate features of dynamic video sequences. Different from the traditional QoE evaluation methods based on network parameters, this paper proposes the HAS QoE prediction methods based on its dynamic video segment features with data mining. Considering the application requirement of the trade-off between accuracy and complexity, two sets of methodologies are designed to evaluate the HAS QoE including regression and classification. In regression method, we propose the evolved PSNR (ePSNR) model using differential peak signal to noise ratio (dPSNR) statistics as the segment features to evaluate HAS QoE. In classification method, we propose the improved weighted k-nearest neighbors (WkNN) by using dynamic weighted mapping according to the position of video chunk to meet the dynamic segment and rate features of HAS. In order to train and test these methods, we build a real-time HAS video-on-demand (VOD) system in LTE network and do subjective test in different video scenes. With the mean opinion score (MOS), the regression and classification methods are trained to predict the HAS QoE. The validated results show that the proposed ePSNR and WkNN methods outperform other evaluation methods.     

A video prediction approach for animating single face image

Generating dynamic 2D image-based facial expressions is a challenging task for facial animation. Much research work focused on performance-driven facial animation from given videos or images of a target face, while animating a single face image driven by emotion labels is a less explored problem. In this work, we treat the task of animating single face image from emotion labels as a conditional video prediction problem, and propose a novel framework by combining factored conditional restricted boltzmann machines (FCRBM) and reconstruction contractive auto-encoder (RCAE). A modified RCAE with an associated efficient training strategy is used to extract low dimensional features and reconstruct face images. FCRBM is used as animator to predict facial expression sequence in the feature space given discrete emotion labels and a frontal neutral face image as input. Both quantitative and qualitative evaluations on two facial expression databases, and comparison to state-of-the-art showed the effectiveness of our proposed framework for animating frontal neutral face image from given emotion labels.

     

A low complexity detection method for video data discontinuity implemented on SoC-FPGA by using pixel location prediction scheme

Multimedia Tools and Applications - Image/video processing in real-time is always in high demand for the quality of video. There are several factors which cause the loss of the video content, such...     

NDNetGaming - development of a no-reference deep CNN for gaming video quality prediction

Multimedia Tools and Applications - Gaming video streaming services are growing rapidly due to new services such as passive video streaming of gaming content, e.g. Twitch.tv, as well as cloud...     

A data embedded video coding scheme for error-prone channels

We propose a novel data embedding scheme for fractional-pixel based video coding algorithms such as H.263 and MPEG-2. By modifying the motion estimation procedure at fractional-pixel precision, two bits data can be embedded in a motion vector (MV) for an inter-mode coded macroblock (MB). For half-pixel precision motion estimation, the resulted bitstream is compatible with the current video coding standards. Performance of the proposed data embedding scheme in terms of compression efficiency is also studied. As an application example of the proposed data embedding scheme, an error-resilient video coding scheme is also presented where some redundant information, which is used to protect MVs and coding modes of MBs in one frame, is embedded into the MVs in the next frame. When errors occur in one group of blocks (GOB), the decoder can exactly recover the MVs of the corrupted GOB if the next frame can be received correctly. Simulations show that the proposed scheme has better performance than standard H.263 coding scheme for transmission over error-prone channels     

Artificial neural network prediction of clozapine response with combined pharmacogenetic and clinical data

Although one third to one half of refractory schizophrenic patients responds to clozapine, however, there are few evidences currently that could predict clozapine response before the use of the medication. The present study aimed to train and validate artificial neural networks (ANN), using clinical and pharmacogenetic data, to predict clozapine response in schizophrenic patients. Five pharmacogenetic variables and five clinical variables were collated from 93 schizophrenic patients taking clozapine, including 26 responders. ANN analysis was carried out by training the network with data from 75% of cases and subsequently testing with data from 25% of unseen cases to determine the optimal ANN architecture. Then the leave-one-out method was used to examine the generalization of the models. The optimal ANN architecture was found to be a standard feed-forward, fully-connected, back-propagation multilayer perceptron. The overall accuracy rate of ANN was 83.3%, which is higher than that of logistic regression (LR) (70.8%). By using the area under the receiver operating characteristics curve as a measure of performance, the ANN outperformed the LR (0.821+/-0.054 versus 0.579+/-0.068; p<0.001). The ANN with only genetic variables outperformed the ANN with only clinical variables (0.805+/-0.056 versus 0.647+/-0.066; p=0.046). The gene polymorphisms should play an important role in the prediction. Further validation of ANN analysis is likely to provide decision support for predicting individual response.     

The combined use of axi-symmetric and solid elements for three dimensional stress analysis

This paper presents a simple but effective finite element technique to analyze the realistic compound structures composed of axi-symmetric as well as asymmetric components for which axi-symmetric elements are adopted to deal with axi-symmetric portions and solid elements are employed only for the rest of asymmetric parts. The connections between adjacent axi-symmetric and solid elements are achieved through the use of a Lagrangean multiplier technique and the inter-element compatibility conditions are also satisfied. Monotone convergence of the solutions is thus guaranteed. Several numerical experiments are carried out to demonstrate the versatility and high efficiency of this work. Excellent correlations between the calculated results and the referenced solutions obtained by the complete three dimensional analysis can be found. Since no restrictions are imposed on the formulation of this work, the present technique is quite general and can be further extended to deal with other types of elements considered.     

基于FPGA的AVS帧内预测电路设计

提出了一种AVS高清视频编码器帧内预测模块硬件结构。通过对AVS帧内预测各个预测模式的分析,设计了帧内预测编码流水线结构和模式预测运算单元电路。根据各预测模式的编码运算关系,合理安排流水线结构,采用8 bit数据并行流水处理,实现了高清视频帧内预测实时编码。将除Plane模式之外的其他预测模式采用同一硬件电路来实现,对运算比较复杂的Plane模式单独设计了硬件结构,节省了硬件资源。     

A fast and efficient saliency detection model in video compressed-domain for human fixations prediction

Multimedia Tools and Applications - Research and application of human fixations detection in video compressed-domain have gained an increasing attention in the latest years. However, both...     

数据挖掘在电信行业客户流失预测中的应用

客户流失是电信行业发展过程中所面临的一个严重问题,直接影响到运营商的企业效益。本文主要介绍了对电信行业客户流失情况进行数据挖掘的过程,改进了已有模型存在的缺乏灵活性、难以处理高维度数据的缺点,根据运营商的历史数据资料,利用SAS/EM模块对客户的固有特征和行为特征进行挖掘分析,采用决策树分类算法的CART算法建立了聚类分析模型和包括评估模块在内的一套完整的流失预测模型,能够直观地显示出流失客户的基本特征,并且可以对任意的数据集进行分析,有效提高了模型的普遍应用性和准确性。     

On the use of data filtering techniques for credit risk prediction with instance-based models

Many techniques have been proposed for credit risk prediction, from statistical models to artificial intelligence methods. However, very few research efforts have been devoted to deal with the presence of noise and outliers in the training set, which may strongly affect the performance of the prediction model. Accordingly, the aim of the present paper is to systematically investigate whether the application of filtering algorithms leads to an increase in accuracy of instance-based classifiers in the context of credit risk assessment. The experimental results with 20 different algorithms and 8 credit databases show that the filtered sets perform significantly better than the non-preprocessed training sets when using the nearest neighbour decision rule. The experiments also allow to identify which techniques are most robust and accurate when confronted with noisy credit data.     

A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation

Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are being still developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall modeling as well as other fields of hydrology.In the current research, the wavelet analysis was linked to the ANN concept for prediction of Ligvanchai watershed precipitation at Tabriz, Iran. For this purpose, the main time series was decomposed to some multi-frequently time series by wavelet theory, then these time series were imposed as input data to the ANN to predict the precipitation 1 month ahead. The obtained results show the proposed model can predict both short- and long-term precipitation events because of using multi-scale time series as the ANN input layer.