Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure

Low Resistance to Resettlement in Singapore, 1965-1985: A Clumsy Approach that overcame a Wicked Problem

Master'sMASTER OF SOCIAL SCIENCE

Advances in Simultaneous Translation

Simultaneous translation, which translates concurrently with the source language speech, is widely used in many scenarios including multilateral organizations. However, it is well known to be one of the most challenging tasks for humans due to the simultaneous perception and production in two languages. On the other hand, simultaneous translation is also notoriously difficult for machines and has remained one of the holy grails of AI. The key challenge is the word order difference between the source and target languages. There have been efforts towards genuine simultaneous translation, but all these efforts have the following major limitations: (a) none of them can achieve any arbitrary given latency; (b) their base translation model is still trained on full sentences; and (c) their systems are complicated, involving many components and are difficult to train. In this thesis, we start by introducing several simultaneous translation approaches with two orthogonal categories: fixed or adaptive latency policies; trained on full sentences or not. Then, we investigate how to improve simultaneous translation with beam search which is universally used in full-sentence translation but non-trivial to be applied in simulta- neous translation. Finally, we explore speech-to-speech simultaneous interpretation by incorporating streaming ASR and incremental TTS