Inference and learning in hybrid probabilistic network

This paper proposed a novel hybrid probabilistic network, which is a good tradeoff between the model complexity and learnability in practice. It relaxes the conditional independence assumptions of Naive Bayes while still permitting efficient inference and learning. Experimental studies on a set of natural domains prove its clear advantages with respect to the generalization ability.     

无线传感器网络中一次性数字签名算法设计

一次性数字签名算法是构造密码协议的基础算法,可用于认证无线传感器网络(WSN)中传输数据的真实性和完整性。针对WSN的应用需求,给出一种高效的一次性数字签名算法。与现有一次性签名算法相比,该算法生成签名简单快捷,只需执行一次模指数乘法运算;签名长度短,只需传输一个群元素;适用范围广,能根据待签名消息的长度自动适配对应的公钥参数,该特性非常适合WSN中数据传输量较小的应用;同时该算法还能在标准模型下得到形式化的安全证明,其抗伪造攻击安全性可归约为双线性映射群中计算的Diffie-Hellman数学难题。     

Global structure constrained local shape prior estimation for medical image segmentation

Organ shape plays an important role in clinical diagnosis, surgical planning and treatment evaluation. Shape modeling is a critical factor affecting the performance of deformable model based segmentation methods for organ shape extraction. In most existing works, shape modeling is completed in the original shape space, with the presence of outliers. In addition, the specificity of the patient was not taken into account. This paper proposes a novel target-oriented shape prior model to deal with these two problems in a unified framework. The proposed method measures the intrinsic similarity between the target shape and the training shapes on an embedded manifold by manifold learning techniques. With this approach, shapes in the training set can be selected according to their intrinsic similarity to the target image. With more accurate shape guidance, an optimized search is performed by a deformable model to minimize an energy functional for image segmentation, which is efficiently achieved by using dynamic programming. Our method has been validated on 2D prostate localization and 3D prostate segmentation in MRI scans. Compared to other existing methods, our proposed method exhibits better performance in both studies.     

半监督复杂结构数据降维方法

现有的一些典型半监督降维算法,往往在利用标记信息的同时却忽略了样本数据本身的流形特征,或者是对流形特征使用不当,导致算法性能表现不佳并且应用领域狭窄。针对上述问题提出了半监督复杂结构数据降维方法,同时保持样本数据的全局与局部的流形特征。通过设置适当的目标函数,使算法结果能有更广泛的应用场合,实验证明了算法的有效性。     

论西方审美游戏说的流变和发展

探讨了从亚里斯多德的娱乐到康德的自由到席勒的生命冲动,从儿童游戏到上帝掷色子到后现代的游戏解构策略,看到了审美游戏说在西方历史中呈现出有趣的双向演绎和延伸,提供了理解西方审美理论的一个新参考点。     

对经典隐马尔可夫模型的经验性改进

文章分析了经典隐马尔可夫模型（Hidden Markov Model,HMM）齐次假设的理论缺陷,以及两种非齐次HMM。语音识别对比实验表明,经验性的惩罚概率法是稳健的、且更有效的补偿方法。实验结果还指出在最优惩罚概率下,经典HMM达到了与非齐次的基于段长分布的HMM（Duration Distribution Based HMM,DDBHMM）几乎相同的识别率,证明了齐次假设并不影响经典HMM在实用中的重要性。文章提出了一种改进Baum－Welch重估算法的初值的经验方法,用于HMM参数的估计,在汉语连续语音识别实验中一致性地降低了音节误识率。     

通用可组合安全计算的信任模型研究

通用可组合（UC）安全计算的存在性需要刻画某种基础假定的信任模型的支持。针对国内对于UC安全计算的信任模型研究甚少的现状,研究了UC安全性的本质要求及其对UC安全计算的限制,得出了使用这些信任模型的根本原因,进而分析了国外用来刻画这些基础假定的信任模型的设计原理,比较了它们的异同和优缺点,并展望了未来的研究方向。     

Tightly Secure Public-Key Cryptographic Schemes from One-More Assumptions

Journal of Computer Science and Technology - A tightly secure cryptographic scheme refers to a construction with a tight security reduction to a hardness assumption, where the reduction loss is a...     

SATWE程序中不同楼板模型对比和设计建议

对一规则平面高层结构采用SATWE程序中不同楼板假定模型进行了结构分析。结果表明,采用刚性楼板假定时,现浇楼板对梁刚度的增大不宜估计过高。楼板平面有限元分析表明,在楼板配筋时,框架梁处板顶负筋宜适当加大,普通楼面梁处板顶负筋宜适当减小。     

一次变色龙哈希函数及其在可修正区块链中的应用

提出了称作一次变色龙哈希函数的新密码学原语:同一哈希值的2个原像(一次碰撞)不会暴露任何陷门信息,而同一哈希值的3个原像(二次碰撞)则会暴露部分陷门信息,但足以导致严重的安全危害.基于经典的RSA困难问题构造了简单高效的一次变色龙哈希函数方案,并在随机预言模型下证明了其安全性.应用该一次变色龙哈希函数方案,进一步高效实现了对每个区块仅允许至多一次修正的可修正区块链,而任何区块的二次修改都将导致区块链崩溃的惩罚.对区块链进行有效治理是网络空间安全治理的关键领域,而可修正区块链则构成了区块链监管和治理的最核心技术.所提出的可修正区块链方案具有高效和修正权限契合实际需求的两大特点,有望为区块链监管(尤其是链上有害数据的事后治理)提供有力的技术参考.