Classification of dog barks: a machine learning approach

In this study we analyzed the possible context-specific and individual-specific features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six different communicative situations was used as the sound sample. The algorithm's task was to learn which acoustic features of the barks, which were recorded in different contexts and from different individuals, could be distinguished from another. The program conducted this task by analyzing barks emitted in previously identified contexts by identified dogs. After the best feature set had been obtained (with which the highest identification rate was achieved), the efficiency of the algorithm was tested in a classification task in which unknown barks were analyzed. The recognition rates we found were highly above chance level: the algorithm could categorize the barks according to their recorded situation with an efficiency of 43% and with an efficiency of 52% of the barking individuals. These findings suggest that dog barks have context-specific and individual-specific acoustic features. In our opinion, this machine learning method may provide an efficient tool for analyzing acoustic data in various behavioral studies.     

Portrayal of body weight on children's television sitcoms: a content analysis

Television programs portray characters with idealized body types that for most viewers are unattainable. These body types have become a common source of comparison for many young viewers who evaluate their own self-worth and bodies based on the models they see on television. This study examines body weight, both in terms of frequency and portrayals, focusing on how preadolescent and adolescent characters’ bodies are presented on the sitcoms from three children's television networks. The results indicate that while children's situation comedies have relatively few overweight characters, this small percentage closely parallels national figures. Male and female characters do not differ in their distribution across weight categories. However, when character ethnicity is factored, significant shifts occur. Surprisingly, no differences were observed in the portrayal of physical and mental attributes among weight categories except in social popularity where overweight characters were not as embedded in social networks. Compared to other relevant studies, this research provides some evidence that the treatment and portrayal of overweight characters in children-targeted sitcoms is more positive, equitable, and less stereotypical than in other programming venues and that children are experiencing body type diversity in these fictional portrayals. Nonetheless, above average weight characters still suffer some social marginality and ethnic misrepresentation.     

Output‐sensitive complexity of multiobjective combinatorial optimization

We study output‐sensitive algorithms and complexity for multiobjective combinatorial optimization problems. In this computational complexity framework, an algorithm for a general enumeration problem is regarded efficient if it is output‐sensitive, that is, its running time is bounded by a polynomial in the input and the output size. We provide both practical examples of multiobjective combinatorial optimization problems for which such an efficient algorithm exists as well as problems for which no efficient algorithm exists under mild complexity theoretic assumptions.     

粗糙集在项目认知属性标定中的应用

认知诊断是新一代测量理论的核心, 对形成性教学评估具有重要意义。项目认知属性标定是认知诊断中一项基础而重要的工作,现有的项目认知属性辅助标定方法的研究工作很少, 并且在应用上存在诸多局限。课堂评估是认知诊断应用的理想场所,但课堂评估中项目的选取具有随意性, 教师难以在短时间内准确标识项目认知属性。本研究首次提出采用粗糙集方法对项目认知属性进行标定, 该方法无需太多被试和项目, 亦无需已知项目参数, 且能当场诊断出结果, 适于采用纸笔测验的课堂评估。通过Monte Carlo模拟研究表明：采用粗糙集方法能迅速地对项目认知属性进行标定, 并具有较高的标定准确率; 而且, 项目认知属性越少、或被试估计判准率越高、或失误率越小则项目认知属性标定的准确率越高。粗糙集方法的引入, 对拓展认知诊断的应用范围, 真正实现其辅助性教学功能, 具有重要作用。     

On Evans's Vague Object from Set Theoretic Viewpoint

Gareth Evans proved that if two objects are indeterminately equal then they are different in reality. He insisted that this contradicts the assumption that there can be vague objects. However we show the consistency between Evans's proof and the existence of vague objects within classical logic. We formalize Evans's proof in a set theory without the axiom of extensionality, and we define a set to be vague if it violates extensionality with respect to some other set. There exist models of set theory where the axiom of extensionality does not hold, so this shows that there can be vague objects.     

Universes of Fuzzy Sets and Axiomatizations of Fuzzy Set Theory. Part I: Model-Based and Axiomatic Approaches

For classical sets one has with the cumulative hierarchy of sets, with axiomatizations like the system ZF, and with the category SET of all sets and mappings standard approaches toward global universes of all sets. We discuss here the corresponding situation for fuzzy set theory.Our emphasis will be on various approaches toward (more or less naively formed)universes of fuzzy sets as well as on axiomatizations, and on categories of fuzzy sets. What we give is a (critical)survey of quite a lot of such approaches which have been offered in the last approximately 35 years. The present Part I is devoted to model based and to axiomatic approaches; the forth-coming Part II will be devoted to category theoretic approaches. This paper is a version of the invited talk given by the author at the conference Trends in Logic III, dedicated to the memory of A. MOSTOWSKI, H. RASIOWA and C. RAUSZER, and held in Warsaw and Ruciane-Nida from 23rd to 25th September 2005. Presented by Jacek Malinowski     

驾驶员为何对道路关键事件“视而不见”?——“自下而上”和“自上而下”加工机制的交互作用

驾驶员“视而不见”错误是指驾驶员的视线虽然指向道路上的危险目标, 却没有知觉到其存在, 对交通安全构成威胁。本文首先在注意计算框架内, 分别从“自下而上”和“自上而下”两种加工机制探讨诱发驾驶员“视而不见”错误的主要因素, 该框架解释力不足的原因在于没有充分考虑实践经验和动机对驾驶员预期和注意定势的影响。由此提出注意计算框架扩展模型, 通过“自下而上”和“自上而下”的交互作用机制对驾驶员“视而不见”错误提出综合解释。     

The watershed years of 1958-1962 in the Harvard Pigeon Lab

During the years 1958-1962, the final years of support by the National Science Foundation for B. F. Skinner's Pigeon Lab in Memorial Hall at Harvard University, 20 or so pigeon experiments (plus some with other organisms) ran concurrently 7 days a week. The research style emphasized experimental analyses, exploratory procedures, and the parametric exploration of variables. This reminiscence describes some features of the laboratory, the context within which it operated, and the activities of some of those who participated in it.