TiO_2对ZrO_2－NiCr金属陶瓷烧结特性和相稳定性的影响

本文研究了ＴｉＯ２掺入对ＣａＯ稳定ＺｒＯ２（简称ＣａＳＺ－ＮｉＣｒ）金属陶瓷烧结特性的影响，测定了ＣａＳＺ－ＮｉＣｒ－ＴｉＯ２金属陶瓷烧结样品的密度和显微硬度，结果表明，ＴｉＯ２的掺入能提高ＣａＳＺ－ＮＩＣｒ金属陶瓷的烧结密度和显微硬度，对ＣａＳＺ－ＮｉＣｒ金属陶瓷有助烧结作用．金相观察表明，ＴｉＯ３的掺入改变了ＮｉＣｒ相在金属陶瓷中的形貌，说明了ＮｉＣｒ与ＣａＳＺ的浸润性有一定的改善．ＸＲＤ和ＥＰＭＡ分析结果显示，ＴｉＯ２偏聚在ＮｉＣｒ金属相中，不影响ＣａＳＺ的稳定性．     

V型立体纸雕的几何理论与算法的研究

立体书是一种独特的纸张艺术形式,有着精细的几何结构.针对一类特殊的V型立体纸雕进行了系统性的研究,给出了V型立体纸雕可实现性的充分条件,使其可以只借助2张支撑面的力量进行打开和关闭,同时在这个过程中所有的面片都保持刚性并且相互不产生自交.基于这些充分条件,文中分别实现了一个计算机辅助交互V型纸雕设计工具,以及一个基于体方法的自动生成算法.     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

Parallel source separation system for heart and lung sounds

The Journal of Supercomputing - In this paper, we propose a parallel source separation system designed to extract heart and lung sounds from single-channel mixtures. The proposed system is based on...     

From text to graph: a general transition-based AMR parsing using neural network

Neural Computing and Applications - Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing,...     

Customer knowledge discovery from online reviews

The explosive growth of Chinese electronic market has made it possible for companies to better understand consumers?? opinion towards their products in a timely fashion through their online reviews. This study proposes a framework for extracting knowledge from online reviews through text mining and econometric analysis. Specifically, we extract product features, detect topics, and identify determinants of customer satisfaction. An experiment on the online reviews from a Chinese leading B2C (Business-to-Customer) website demonstrated the feasibility of the proposed method. We also present some findings about the characteristics of Chinese reviewers.     

Proactive replication for rare objects in unstructured peer-to-peer networks

Unstructured peer-to-peer (P2P) networks have become a very popular architecture for content distribution in large-scale and dynamic environments. The search efficiency problem in unstructured P2P networks has not been adequately addressed so far, especially concerning search for rare objects. In this paper, we propose a proactive replication strategy to improve search efficiency for rare objects. It uses an object-probing technique for peers to decide whether or not to establish replications for their objects when they join the network. This strategy can effectively increase the popularity of rare objects in order to enhance search efficiency. We also present a rare object search algorithm to reduce the overhead caused by the replication strategy. When a peer forwards a search request, the forward probability is calculated according to its neighbors' degrees and the number of neighbors' objects. Therefore, the search request is forwarded to the peers more likely containing target objects. Simulations show that our proactive replication strategy greatly improves search efficiency for rare objects with moderate communication overhead. The rare object search algorithm not only improves search efficiency for rare objects, but also achieves load balance in search.     

Fuzzy local maximal marginal embedding for feature extraction

In graph-based linear dimensionality reduction algorithms, it is crucial to construct a neighbor graph that can correctly reflect the relationship between samples. This paper presents an improved algorithm called fuzzy local maximal marginal embedding (FLMME) for linear dimensionality reduction. Significantly differing from the existing graph-based algorithms is that two novel fuzzy gradual graphs are constructed in FLMME, which help to pull the near neighbor samples in same class nearer and nearer and repel the far neighbor samples of margin between different classes farther and farther when they are projected to feature subspace. Through the fuzzy gradual graphs, FLMME algorithm has lower sensitivities to the sample variations caused by varying illumination, expression, viewing conditions and shapes. The proposed FLMME algorithm is evaluated through experiments by using the WINE database, the Yale and ORL face image databases and the USPS handwriting digital databases. The results show that the FLMME outperforms PCA, LDA, LPP and local maximal marginal embedding.     

Collapsing and Separating Completeness Notions Under Average-Case and Worst-Case Hypotheses

This paper presents the following results on sets that are complete for NP.

1. If there is a problem in NP that requires $2^{n^{\Omega(1)}}$ time at almost all lengths, then every many-one NP-complete set is complete under length-increasing reductions that are computed by polynomial-size circuits.
2. If there is a problem in co-NP that cannot be solved by polynomial-size nondeterministic circuits, then every many-one NP-complete set is complete under length-increasing reductions that are computed by polynomial-size circuits.
3. If there exist a one-way permutation that is secure against subexponential-size circuits and there is a hard tally language in NP??co-NP, then there is a Turing complete language for NP that is not many-one complete.

Our first two results use worst-case hardness hypotheses whereas earlier work that showed similar results relied on average-case or almost-everywhere hardness assumptions. The use of average-case and worst-case hypotheses in the last result is unique as previous results obtaining the same consequence relied on almost-everywhere hardness results.