A retrieval-based dialogue system utilizing utterance and context embeddings

Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. Recent research aims at finding distributed vector representations (embeddings) for words, such that semantically similar words are relatively close within the vector-space. Encoding the "meaning" of text into vectors is a current trend, and text can range from words, phrases and documents to actual human-to-human conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the vector representation of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system.Comment: A shorter version is accepted at ICMLA2017 conference; acknowledgement added; typos correcte

Dynamic Web Cache Management

Web navigation has been the key issue for information retrieval in e-commerce. Information caching is critical for navigation subject to resource constraints and performance requirement. The research on caching originates from data access to computer memory, to database (e.g. multimedia database), to client/server architecture, and recently to Web navigation. The information access for caching normally is assumed the fixed size of data unit. In this research, we first generalize caching problem for Web navigation by considering information structures. The caching criteria also takes into account Web structure, data usage, and navigation patterns. The preliminary result shows the proposed dynamic caching approach, New Semantics-Based Algorithm (NSA), outperforms the common caching functions and can be applied to broader application domains. Some implications and future directions are discussed in the conclusion

NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review

Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene representation has taken the field of Computer Vision by storm. As a novel view synthesis and 3D reconstruction method, NeRF models find applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. Since the original paper by Mildenhall et al., more than 250 preprints were published, with more than 100 eventually being accepted in tier one Computer Vision Conferences. Given NeRF popularity and the current interest in this research area, we believe it necessary to compile a comprehensive survey of NeRF papers from the past two years, which we organized into both architecture, and application based taxonomies. We also provide an introduction to the theory of NeRF based novel view synthesis, and a benchmark comparison of the performance and speed of key NeRF models. By creating this survey, we hope to introduce new researchers to NeRF, provide a helpful reference for influential works in this field, as well as motivate future research directions with our discussion section

Vision and Action

(Also cross-referenced as CAR-TR-722) Our work on Active Vision has recently focused on the computational modelling of navigational tasks, where our investigations were guided by the idea of approaching vision for behavioral systems in form of modules that are directly related to perceptual tasks. These studies led us to branch in various directions and inquire into the problems that have to be addressed in order to obtain an overall understanding of perceptual systems. In this paper we present our views about the architecture of vision syst ems, about how to tackle the design and analysis of perceptual systems, and promising future research directions. Our suggested approach for understanding behavioral vision to realize the relationship of perception and action builds on two earlier approac hes, the Medusa philosophy 13] and the Synthetic approach [15 The resulting framework calls for synthesizing an artificial vision system by studying vision corr petences of increasing complexity and at the same time pursuing the integration of the percept ual components with action and learning modules. We expect that Computer Vision research in the future will progress in tight collaboration with many other disciplines that are concerned with empirical approaches to vision, i.e. the understanding of biolo gical vision. Throughout the paper we describe biological findings that motivate computational arguments which we believe will influence studies of Computer Vision in the near future