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.

A CORPUS-BASED ANALYSIS OF THE VERB "DO" USED BY CHINESE LEARNERS OF ENGLISH

The present study reports on the use of the high-frequency verb do in written English performance based on the Chinese Learner English Corpus (CLEC). The native English corpus for comparison is the Louvain Corpus of Native English Essays (LOCNESS). A corpus-based Contrastive Interlanguage Analysis (CIA) approach has been adopted in the study. A comparison is made between Band 4 and 8 English majors' writings in CLEC and the native college students' writings in LOCNESS. Results indicate that as far as the overall frequency of do is concerned, there is no significant difference between Band 8 English majors and the native speakers, but Band 4 English majors use less than native speakers. With regard to the different uses of do, marked differences have been found between Chinese learners and native speakers, especially when do is used as an auxiliary verb or a delexical verb. To be specific, Chinese learners show a strong tendency to underuse do as an auxiliary verb to form an interrogative, negative or inverted sentence. Moreover, they tend to overuse do as a delexical verb, allowing it more freedom to collocate with a wider range of nouns. The underlying reasons might be mother-tongue interference, intralingual transfer, and overgeneralization, etc. In conclusion, the pedagogical implications of the study are discussed and suggestions made for adopting corpus-based exercises as a way of raising learners' awareness of the use of high-frequency words.

Technology Innovation Management:Topic Evolutions and Research Trends from 1968 to 2022

The technology innovation management(TIM)field attracts an increasing amount of attention.This paper takes a retrospective look at high-quality publication output in the TIM field over the 55 years from 1968 to 2022,revealing topics,their evolutions,and research trends.A total of 31,498 articles and proceeding papers published during this period are analyzed.The paper first extracts the fine-grained topic words using the tool ITGInsight.Then Linlog algorithm is used to cluster topics based on the cooccurrence of the topic words.Time is integrated within the topic cluster results so that topic evolutions and research trends are analyzed.The TIM field has four main topic clusters:technology research,product research,firm research,and future research.In every topic cluster,there are many fine-sorted macro-topics and micro-topics.There is an obvious increase in diversity in the topic clusters of technology research and firm research.Especially,the evolution of technology research has been closely connected with society.In contrast,product research has declined in its topic size.At the same time,future research maintains a certain stability of its scientific publications.The research predicts that all the four topics will retain their popularity,and play an important role in the TIM field.Among them,technology research will continue to expand and enrich the TIM field.The other three topics will deepen their research for a better development of the TIM field.The paper also proposes some advice for industry professionals,policymakers,and researchers.