Collaboration based Multi-Label Learning

It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels. Besides, label correlations are normally used to regularize the hypothesis space, while the final predictions are not explicitly correlated. In this paper, we suggest that for each individual label, the final prediction involves the collaboration between its own prediction and the predictions of other labels. Based on this assumption, we first propose a novel method to learn the label correlations via sparse reconstruction in the label space. Then, by seamlessly integrating the learned label correlations into model training, we propose a novel multi-label learning approach that aims to explicitly account for the correlated predictions of labels while training the desired model simultaneously. Extensive experimental results show that our approach outperforms the state-of-the-art counterparts.Comment: Accepted by AAAI-1

Distributed Data Retrieval for Real-Time Decision-Making under Freshness Constraints

This paper describes a distributed data retrieval algorithm for crowd-sensing application, which aims to collect data with minimized bandwidth cost while satisfying data freshness constraints. In a resource-limited setting, data loses freshness very fast. For instance, the condition of a road during a rush hour may be dynamic due to the rapid change of the traffic. In order to schedule an optimized route to a destination from a given location, we have to know its real-time condition. The protocol we design is to exploit logic dependencies among data by using and-or tree to reduce the overhead of the network and handle concurrent requests at the same time. Meanwhile, we further modify the centralized system into a distributed form so that each node in this network is able to calculate the best retrieval order locally. Furthermore, we integrate some ideas of other literature to let each node store the retrieved data locally due to the fact that the price of storage is lower and lower these days. Finally, we implement part of the algorithms and test the efficiency of using varying probabilities and earliest deadline first to sort queries.Ope

Subtitling Humour from the Perspective of Relevance Theory: The Office in Traditional Chinese

Subtitling the scenes containing humorous utterances in cinematic-televisual productions encounters a myriad of challenges, because the subtitler has to face the technical constraints that characterise the professional subtitling environment and the cultural barriers when reproducing humorous utterances for viewers inhabiting another culture. Past studies tend to explore more limited humour-related areas, which means that a more comprehensive picture of this specialised field is missing. The current research investigates the subtitling of humour, drawing on the framework of relevance theory and the British sitcom The Office, translated from English dialogue into Traditional Chinese subtitles. This research enquires into whether or not relevance theory can explain the subtitling strategies activated to deal with various humorous utterances in the sitcom, and, if so, to what extent. The English-Chinese Corpus of The Office (ECCO), which contains sample texts, media files and annotations, has been constructed to perform an empirical study. To enrich the corpus with valuable annotations, a typology of humour has been developed based on the concept of frame, and a taxonomy of subtitling strategies has also been proposed. The quantitative analysis demonstrates that the principle of relevance is the main benchmark for the choice of a subtitling micro-strategy within any given macro-strategy. With the chi-square test, it further proves the existence of a statistically significant association between humour types/frames and subtitling strategies at the global level. The qualitative analysis shows that the principle of relevance can operate in a subtle way, in which the subtitler invests more cognitive efforts to enhance the acceptability of subtitles. It also develops three levels of mutual dependency between the two variables, from strong, weak to null, to classify different examples. Overall, this study improves our understanding of humour translation and can facilitate a change in the curricula of translator training