Short user-generated videos classification using accompanied audio categories

This paper investigates the classification of short user-generated videos (UGVs) using the accompanied audio data since short UGVs accounts for a great proportion of the Internet UGVs and many short UGVs are accompanied by singlecategory soundtracks. We define seven types of UGVs corresponding to seven audio categories respectively. We also investigate three modeling approaches for audio feature representation, namely, single Gaussian (1G), Gaussian mixture (GMM) and Bag-of-Audio-Word (BoAW) models. Then using Support Vector Machine (SVM) with three different distance measurements corresponding to three feature representations, classifiers are trained to categorize the UGVs. The accompanying evaluation results show that these approaches are effective for categorizing the short UGVs based on their audio track. Experimental results show that a GMM representation with approximated Bhattacharyya distance (ABD) measurement produces the best performance, and BoAW representation with chi-square kernel also reports comparable results

A Rejection Principle for Sequential Tests of Multiple Hypotheses Controlling Familywise Error Rates

We present a unifying approach to multiple testing procedures for sequential (or streaming) data by giving sufficient conditions for a sequential multiple testing procedure to control the familywise error rate (FWER), extending to the sequential domain the work of Goeman and Solari (2010) who accomplished this for fixed sample size procedures. Together we call these conditions the "rejection principle for sequential tests," which we then apply to some existing sequential multiple testing procedures to give simplified understanding of their FWER control. Next the principle is applied to derive two new sequential multiple testing procedures with provable FWER control, one for testing hypotheses in order and another for closed testing. Examples of these new procedures are given by applying them to a chromosome aberration data set and to finding the maximum safe dose of a treatment

Detecting complex events in user-generated video using concept classifiers

Automatic detection of complex events in user-generated videos (UGV) is a challenging task due to its new characteristics differing from broadcast video. In this work, we firstly summarize the new characteristics of UGV, and then explore how to utilize concept classifiers to recognize complex events in UGV content. The method starts from manually selecting a variety of relevant concepts, followed byconstructing classifiers for these concepts. Finally, complex event detectors are learned by using the concatenated probabilistic scores of these concept classifiers as features. Further, we also compare three different fusion operations of probabilistic scores, namely Maximum, Average and Minimum fusion. Experimental results suggest that our method provides promising results. It also shows that Maximum fusion tends to give better performance for most complex events

Semantic concept detection in imbalanced datasets based on different under-sampling strategies

Semantic concept detection is a very useful technique for developing powerful retrieval or filtering systems for multimedia data. To date, the methods for concept detection have been converging on generic classification schemes. However, there is often imbalanced dataset or rare class problems in classification algorithms, which deteriorate the performance of many classifiers. In this paper, we adopt three “under-sampling” strategies to handle this imbalanced dataset issue in a SVM classification framework and evaluate their performances on the TRECVid 2007 dataset and additional positive samples from TRECVid 2010 development set. Experimental results show that our well-designed “under-sampling” methods (method SAK) increase the performance of concept detection about 9.6% overall. In cases of extreme imbalance in the collection the proposed methods worsen the performance than a baseline sampling method (method SI), however in the majority of cases, our proposed methods increase the performance of concept detection substantially. We also conclude that method SAK is a promising solution to address the SVM classification with not extremely imbalanced datasets

侶倫與香港文學

侶倫，原名李霖 (1911 - 1988) ，祖籍廣東惠陽橫闕，是香港土生土長的作家，也是香港早期文學的拓荒者。侶倫於1919年開始， 先後入讀新民學校、強亞學校和英文書院，皆未畢業而輟學。1927 年，侶倫到廣州參加北伐。1928年8月，侶倫在有香港 文壇第一燕 之稱的《伴侶》首次發表《試》、《殿薇》、《0的日記》等短篇小說。1931 年，侶倫任香港體育協進會書記，並擔任《南華日報》文藝副刊編輯。1937年加入 香港文化界座談會 成為會員和於電影公司任編劇。侶倫曾經編過《島上》、《時代筆語》、《南風》、《激流》等作品，也創作長、中、短篇小說、散文達二十餘種。侶倫的日記因抗戰而被銷毀，電影劇本則因沒有收入集子中而散佚。侶倫晚年創作不多。在1978年被吸納為中國作家協會廣東省分會會員，並於1988年成為香港作家聯誼會會員。最後刊載的文章是在《香港文學》(第三十七期) 之《把戲》，逝世時享年七十七歲。 一般研究侶倫的評論家，大都以單篇作品分析為主，論述較為詳細的有東瑞與盧瑋鑿，兩者的評論方式各異。[1] 本文企圖以作家本身的經歷、文壇的歷史與社會情況對侶倫的影響，來研究他在不同時期在作品題材和風格上的演變過程。本文的研究範園主要針對侶倫寫作高峰期 (三十至五十年代) 作品之特色及思想轉變，並考察侶倫在香港文學及華文文學中所扮演的角色。本文擬分三個部分。第一部分討論伯倫早期作品的風格特色，選取他的代表作《黑麗拉》及同時期作品作為研究對象。第二部分主要討論侶倫中期的淪陷文學，以《無盡的愛》作為主要的研究對象，並從香港淪陷時期眾本土拓荒作家改變寫作方向，來討論侶倫在香港文壇的獨特與局限性。本文亦會聯繫張吻冰 (望雲) 的《黑俠》等通俗文學作品，來討論侶倫的淪陷文學在大陸的抗戰文學中扮演的角色。第三 部分將研討侶倫在戰後和平時期的文學風格特色，以其作表作品《窮巷》為例。這一時期是政治敏感時代，很多難民紛紛來港，本文將探討侶倫的戰後文學與受美元文化影響的美元文學的異同，且涉及南下作家趙滋蕃的《半下流社會》等美元文學

A Study on the Teaching Mode of Integrating Value Cultivation with Critical Thinking in the Listening and Speaking Course for English Majors

In order to solve the long-standing problems in English listening and speaking class, such as “Centralization of language knowledge and skills”; “Absence of critical thinking,” and “Separation of knowledge and affection”, this research put forward the model of Integrating Value Cultivation with Critical Thinking (IVCCT) (Note 1), which breaks through the traditional “Language-centrism” model. With values education as the core, this mode systematically cultivate the multi-competences in students, such as innovation, research, communication, cooperation and so on, through cross-cultural critical thinking activities, which will help the participants achieve the educational goals of integrating value shaping, knowledge transmission and competence cultivation