A deep bidirectional LSTM approach for video-realistic talking head

This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. The audio streams are converted into acoustic feature, i.e. Mel-Frequency Cepstrum Coefficients (MFCCs), and their textual labels are also extracted. The visual streams, in particular, the lower face region, are compactly represented by active appearance model (AAM) parameters by which the shape and texture variations can be jointly modeled. Given pairs of the audio and visual parameter sequence, a DBLSTM model is trained to learn the sequence mapping from audio to visual space. For any unseen speech audio, whether it is original recorded or synthesized by text-to-speech (TTS), the trained DBLSTM model can predict a convincing AAM parameter trajectory for the lower face animation. To further improve the realism of the proposed talking head, the trajectory tiling method is adopted to use the DBLSTM predicted AAM trajectory as a guide to select a smooth real sample image sequence from the recorded database. We then stitch the selected lower face image sequence back to a background face video of the same subject, resulting in a video-realistic talking head. Experimental results show that the proposed DBLSTM approach outperforms the existing HMM-based approach in both objective and subjective evaluations.     

Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people

Predicting the centrality of nodes is a significant problem for different applications in Opportunistic Mobile Social Networks (OMSNs). However, when calculating such metrics, current studies focused on analyzing static networks that do not change over time or using aggregated contact information over a period of time. Furthermore, the centrality measured in the past is not verified whether it is useful as a predictor for the future. In this paper, in order to capture the dynamic behavior of people, we focus on predicting nodes’ future centrality (importance) from the temporal perspective using real mobility traces in OMSNs. Three important centrality metrics, namely betweenness, closeness, and degree centrality, are considered. Through real trace-driven simulations, we find that nodes’ future centrality is highly predictable due to natural social behavior of people. Then, based on the observations in the simulation, we design several reasonable prediction methods to predict nodes’ future temporal centrality. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed methods. The results show that the Recent Weighted Average Method performs best in the MIT Reality trace, and the recent Uniform Average Method performs best in the Infocom 06 trace. Furthermore, we also evaluate the impact of parameters m and w on the performance of the proposed methods and find proper values of different parameters for each proposed method at the same time.     

基于时间条件算法的灭火机器人设计

详细介绍了灭火机器人的组装,在组装机器人的基础上,概括性的介绍了灭火机器人的各种算法及无火不进房算法中的时间算法和条件算法。为了让灭火机器人更加稳定的退出每个房间,结合时间算法和条件算法的优点,提出了时间条件算法,从而让机器人采用时间条件算法在迷宫中行走,并完成灭火任务。实验表明,此算法的应用使机器人在灭火过程中稳定性很高,并能及时避开迷宫墙壁;不但效果好,而且在很大程度上缩短了时间。     

地方本科院校计算机类专业人才培养模式的探索与实践

地方本科院校计算机类专业承担着为我国培养计算机应用型人才的重任,探索新时期下计算机应用型人才的培养模式,其根本出路就是坚持"以就业为导向、以服务为宗旨,走产学研结合之路"。在培养目标上,目前应分为以下4个方向:计算机硬件维护与应用、计算机网络安全技术、计算机程序开发技术、计算机信息管理。在理论教学上采取"基本素质模块"+"专业基础模块"+"专业主干模块"+"专业创新模块";在实践教学上采取"基本技能"+"专业技能"+"开发设计技能"+"岗位综合应用技能"。     

水利工程造价管理与控制

分析了以设计阶段为控制重点的全过程造价管理及目前我国水利工程造价管理中存在的一些问题,并就水利工程造价管理与控制提出了一些建议。     

Informative conditions for a data set in a MIMO closed‐loop system with a switching controller

In system identification, a data set needs to be informativeto ensure that the identification criterion has a unique global minimum asymptotically, and the parameter estimation is consistent. To guarantee a data set in a MIMO closed‐loop system with a switching controller to be informative, we develop a group of flexible conditions for the controller's switching rule and feedback laws, which provide a trade‐off between these two aspects through adjustable parameters. Compared with the results in the existing literature, our result includes all kinds of switching rules and therefore is not limited by the constraints on the switching rule.Copyright © 2014 John Wiley & Sons, Ltd.     

Aggregate‐induced emission in light‐emitting liquid crystal display technology

Nowadays, liquid crystal displays (LCDs) with light‐emission are considered as energy‐efficient devices and are promising alternatives to conventional LCDs. To realize such possibility, strong fluorescent materials with a dichroic properties are required. Aggregate‐induced emission (AIE) is an unusual photophysical phenomenon shown by some luminogenic materials that will be highly emissive in their aggregate state. In this work, we studied the AIE effect of a luminescent liquid crystalline molecule TPE‐PPE in our LC system as a luminophore dopant. The result showed the excellent AIE effect that higher concentration of luminogen in the nematic LC host induced stronger luminescent intensity. Through exposure of a photoisomeriable alignment material sulfonic‐dye‐1, the photopatterning of a light‐emitting LC device was achieved with the use of the TPE‐PPE/nematic LC mixture.     

Novel neural network modeling method and applications

Neural networks play an important role for designing the parametric model of electromagnetic structures. The current neural network methods are unfit for a circuit model with many input variables because it is costly to extract a large number of the training data and test data to complete the highly nonlinear mapping approximation. This article proposes a new neural network modeling method—the multidimensional neural network model, which can be used to solve the issue of multivariable radiofrequency and microwave passive device modeling. The entire multidimensional neural network modeling problem is simplified into a set of neural network submodels through decomposition method. Then the submodels are combined into an equivalent model, and the final entire model is produced through the neural‐network mapping model developed with the submodels and equivalent model. A microstrip hairpin filter model is developed using the proposed method. The simulation results show the correctness and the effectivity of the proposed method. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:769–779, 2015.