Suboptimal Exposure to Facial Expressions When Viewing Video Messages From a Small Screen: Effects on Emotion, Attention, and Memory.

The authors examined the effects of suboptimally presented facial expressions on emotional and attentional responses and memory among 39 young adults viewing video (business news) messages from a small screen. Facial electromyography (EMG) and respiratory sinus arrhythmia were used as physiological measures of emotion and attention, respectively. Several congruency priming effects were found. In particular, happy facial primes prompted increased (a) pleasure ratings, (b) orbicularis oculi EMG activity, (c) perceived trustworthiness, and (d) recognition memory for video messages with a positive emotional tone. Emotional and other responses to video messages presented on a small screen can be modified with suboptimal affective primes, but even small differences in the emotional tone of the messages should be allowed for. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Frameworks for understanding emotions and their development: Functionalist and dynamic systems approaches.

In recent years, both functionalist and dynamic systems approaches have assumed increasing prominence in the study of emotion and its development, but the similarities and differences between these perspectives remain largely unexplored and open to more systematic examination. In this article, the authors argue that both approaches share a systems view of emotion and regard emotion in relational, process terms. However, each approach adopts a distinct level of analysis and distinct types of explanation for emotion and its development. Whereas the functionalist approach appeals to formal and final causes to explain patterning in emotion at the level of organism-environment relations, the dynamic systems approach appeals to efficient and material causes to explain emotion at the specific content level of behavior in context. Whether these approaches complement or conflict with one another depends on the extent to which the dynamic systems approach admits abstraction into its explanatory framework. A. Fogel's social process theory and M. D. Lewis' approach to emotion self-organization are discussed in this regard. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

High‐error approximate dictionary search using estimate hash comparisons

A method for finding all matches in a pre‐processed dictionary for a query string q and with at most k differences is presented. A very fast constant‐time estimate using hashes is presented. A tree structure is used to minimize the number of estimates made. Practical tests are performed, showing that the estimate can filter out 99% of the full comparisons for 40% error rates and dictionaries of up to four million words. The tree is found to be efficient up to a 50% error rate. Copyright © 2006 John Wiley & Sons, Ltd.     

灵活运用数据库设计理论

简介了关系模型、键、完整性规则、E-R模型、函数依赖、模式分解和关系模式的范式等数据库技术基础和规范化设计理论，从键和完整性规则的实现、数据冗余的特殊情况和字典设计等方面论述了在实际设计工作中如何灵活运用关系数据库设计理论。     

词汇、语法和传情达意--一个湖边餐厅的习作

建筑设计往往是以传达情感为最终目的，因此语言的简单和熟练便显得愈发的重要。这个餐厅的设计尝试以简单的词汇组成直截的语言，力图清晰明确的传递建筑师的情感。     

Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network

The application of cross‐corpus for speech emotion recognition (SER) via domain adaptation methods have gain high acknowledgment for developing good robust emotion recognition systems using different corpora or datasets. However, the issue of cross‐lingual still remains a challenge in SER and needs more attention to resolve the scenario of applying different language types in both training and testing. In this paper, we propose a triple attentive asymmetric convolutional neural network to address the recognition of emotions for cross‐lingual and cross‐corpus speech in an unsupervised approach. The proposed method adopts the joint supervision of softmax loss and center loss to learn high power discriminative feature representations for target domain via the use of high quality pseudo‐labels. The proposed model uses three attentive convolutional neural networks asymmetrically, where two of the networks are used to artificially label unlabeled target samples as a result of their predictions from training on source labeled samples and the other network is used to obtain salient target discriminative features from the pseudo‐labeled target samples. We evaluate our proposed method on three different language types (i.e., English, German, and Italian) data sets. The experimental results indicate that, our proposed method achieves higher prediction accuracy over other state‐of‐the‐art methods.     

Unseen users, unknown systems: Computer design for a scholar's dictionary

The Dictionary of Old English computing systems have provided access since the 1970s to a database of approximately three million running words. These systems, designed for a variety of machines and written in a variety of languages, have until recently been planned with computing center billing algorithms in mind. With personal workstations emphasis has shifted to building more elegant user interfaces and to providing the entire DOE database to editors around the world. While the shift from sequential files to random access files and the provision of extensive development tools have changed some of the design process, error checking and protection of the database against accidental intrusion have remained as central issues.Richard L. Venezky is Unidel Professor of Educational Studies and professor of Computer and Information Sciences at the University of Delaware. He was formerly professor and chair of Computer Sciences at the University of Wisconsin. His research interests include writing systems, literacy, knowledge representation, and computer-assisted instruction. Among his recent publications are The Subtle Danger: Reflections on the Literacy Abilities of America's Young Adults (Princeton, NJ: ETS, 1987), and Steps Towards a Modern History of American Reading Instruction (Review of Research in Education, 1986, vol. 13, 129–70).     

基于可拓设计的驾驶人多模态情绪感知装置研究

为科学决策和评价驾驶人情绪感知装置的最优化多模态组合方案, 本文基于可拓设计多功能产品创造法, 利用基元理论中的发散分析法分析了情绪感知装置的检测功能, 建立了类物元,并生成具体产品物元模型。分析了单一检测装置包含的3种安装位置间的重组关系, 拓展出一系列重组方案, 再利用优度评价法对其进行筛选。 最终确定了一种有效的多模态驾驶人情绪感知装置的组合方案。     

A new approach of audio emotion recognition

A new architecture of intelligent audio emotion recognition is proposed in this paper. It fully utilizes both prosodic and spectral features in its design. It has two main paths in parallel and can recognize 6 emotions. Path 1 is designed based on intensive analysis of different prosodic features. Significant prosodic features are identified to differentiate emotions. Path 2 is designed based on research analysis on spectral features. Extraction of Mel-Frequency Cepstral Coefficient (MFCC) feature is then followed by Bi-directional Principle Component Analysis (BDPCA), Linear Discriminant Analysis (LDA) and Radial Basis Function (RBF) neural classification. This path has 3 parallel BDPCA + LDA + RBF sub-paths structure and each handles two emotions. Fusion modules are also proposed for weights assignment and decision making. The performance of the proposed architecture is evaluated on eNTERFACE’05 and RML databases. Simulation results and comparison have revealed good performance of the proposed recognizer.     

相空间重构在语音情感识别中的研究

为了更为全面地表征语音情感状态,弥补线性情感特征参数在刻画不同情感类型上的不足,将相空间重构理论引入语音情感识别中来,通过分析不同情感状态下的混沌特征,提取Kolmogorov熵和关联维作为新的情感特征参数,并结合传统语音特征使用支持向量机（SVM）进行语音情感识别。实验结果表明,通过引入混沌参数,与传统物理特征进行识别的方案相比,准确率有了一定的提高,为语音情感的识别提供了一个新的研究途径。