基于专利分析的人工智能技术发展态势研究

人工智能技术作为引领新一代科技革命的前沿性、战略性力量,已被广泛应用于生物医药、机器人、能源、材料、自动驾驶等众多跨学科领域,并形成了多个研究分支。本文对Innography专利数据库收录的相关专利数据进行挖掘分析,并从专利及专利家族的申请及授权年度趋势、专利技术来源地域、专利技术目标市场地域、专利分类体系分布以及研究机构的角度,分析人工智能技术的发展态势、最新突破以及技术布局情况等。研究发现:中国人工智能研究始于1985年,从2007年进入快速增长期,目前美国是最主要的人工智能技术来源国和目标市场,研究机构以企业为主,但在通用技术方面的布局仍需加强。

A survey on rough set theory and its applications

After probability theory, fuzzy set theory and evidence theory, rough set theory is a new mathematical tool for dealing with vague, imprecise, inconsistent and uncertain knowledge. In recent years, the research and applications on rough set theory have attracted more and more researchers＇ attention. And it is one of the hot issues in the artificial intelligence field. In this paper, the basic concepts, operations and characteristics on the rough set theory are introduced firstly, and then the extensions of rough set model, the situation of their applications, some application software and the key problems in applied research for the rough set theory are presented.

Exploiting structural similarity of log files in fault diagnosis for Web service composition

With increasing deployment of Web services, the research on the dependability and availability of Web service composition becomes more and more active. Since unexpected faults of Web service composition may occur in different levels at runtime, log analysis as a typical data- driven approach for fault diagnosis is more applicable and scalable in various architectures. Considering the trend that more and more service logs are represented using XML or JSON format which has good flexibility and interoperability, fault classification problem of semi-structured logs is considered as a challenging issue in this area. However, most existing approaches focus on the log content analysis but ignore the structural information and lead to poor performance. To improve the accuracy of fault classification, we exploit structural similarity of log files and propose a similarity based Bayesian learning approach for semi-structured logs in this paper. Our solution estimates degrees of similarity among structural elements from heterogeneous log data, constructs combined Bayesian network （CBN）, uses similarity based learning algorithm to compute probabilities in CBN, and classifies test log data into most probable fault categories based on the generated CBN. Experimental results show that our approach outperforms other learning approaches on structural log datasets.

Density peaks clustering based integrate framework for multi-document summarization

We present a novel unsupervised integrated score framework to generate generic extractive multi- document summaries by ranking sentences based on dynamic programming （DP） strategy. Considering that cluster-based methods proposed by other researchers tend to ignore informativeness of words when they generate summaries, our proposed framework takes relevance, diversity, informativeness and length constraint of sentences into consideration comprehensively. We apply Density Peaks Clustering （DPC） to get relevance scores and diversity scores of sentences simultaneously. Our framework produces the best performance on DUC2004, 0.396 of ROUGE-1 score, 0.094 of ROUGE-2 score and 0.143 of ROUGE-SU4 which outperforms a series of popular baselines, such as DUC Best, FGB [7], and BSTM [10].

Salient pairwise spatio-temporal interest points for real-time activity recognition

Real-time Human action classification in complex scenes has applications in various domains such as visual surveillance, video retrieval and human robot interaction. While, the task is challenging due to computation efficiency, cluttered backgrounds and intro-variability among same type of actions. Spatio-temporal interest point （STIP） based methods have shown promising results to tackle human action classification in complex scenes efficiently. However, the state-of-the-art works typically utilize bag-of-visual words （BoVW） model which only focuses on the word distribution of STIPs and ignore the distinctive character of word structure. In this paper, the distribution of STIPs is organized into a salient directed graph, which reflects salient motions and can be divided into a time salient directed graph and a space salient directed graph, aiming at adding spatio-temporal discriminant to BoVW. Generally speaking, both salient directed graphs are constructed by labeled STIPs in pairs. In detail, the ＂directional co-occurrence＂ property of different labeled pairwise STIPs in same frame is utilized to represent the time saliency, and the space saliency is reflected by the ＂geometric relationships＂ between same labeled pairwise STIPs across different frames. Then, new statistical features namely the Time Salient Pairwise feature （TSP） and the Space Salient Pairwise feature （SSP） are designed to describe two salient directed graphs, respectively. Experiments are carried out with a homogeneous kernel SVM classifier, on four challenging datasets KTH, ADL and UT-Interaction. Final results confirm the complementary of TSP and SSP, and our multi-cue representation TSP ＋ SSP ＋ BoVW can properly describe human actions with large intro-variability in real-time.

Research progress of artificial psychology and artificial emotion in China

Since the concept of artificial psychology and artificial emotion was first presented, it has become a topic of interest in academic circles and enterprises. In this article, we first briefly introduce the basic concepts and principles of artificial psychology and artificial emotion, analyzing the unified macro-model of the cross-disciplinary system architecture against the need-motivation-behavior framework. Second, we discuss the origin of artificial psychology and artificial emotion, its course of development, and its present situation in China. We also present a review of the published papers and research endeavors of Chinese universities and research institutions and the technical engineering applications of artificial psychology and artificial emotion. Finally, we summarize the challenges to the further development of artificial psychology and artificial emotion and our recommendations for improving the cognitive computing model of psychological states and developing reliable and accurate humanoid interaction and cooperation technology, robot platforms with emotions and humanoid interaction and cooperation capabilities, and humanoid robots for the elderly and the disabled in smart homes. We believe that, with intensive research, artificial psychology and artificial emotion may be developed further and may eventually reach maturity.

Frame interpolation with pixel-level motion vector field and mesh based hole filling

Most of the traditional methods are based on block motion compensation tending to involve heavy blocking artifacts in the interpolated frames. In this paper, a new frame interpolation method with pixel-level motion vector field （MVF） is proposed. Our method consists of the following four steps： （i） applying the pixel-level motion vectors （MVs） estimated by optical flow algorithm to eliminate blocking artifacts （ii） motion post-processing and super-sampling anti-aliasing to solve the problems caused by pixel-level MVs （iii） robust warping method to address collisions and holes caused by occlusions （iv） a new holes filling method using triangular mesh （HFTM） to reduce the artifacts caused by holes. Experimental results show that the proposed method can effectively alleviate the holes and blocking artifacts in interpolated frames, and outperforms existing methods both in terms of objective and subjective performances, especially for sequences with complex motions.