Named entity recognition (NER), which focuses on the extraction of semantically meaningful named entities and their semantic classes from text, serves as an indispensable component for several down-stream natural language processing (NLP) tasks such as relation extraction and event extraction. Dependency trees, on the other hand, also convey crucial semantic-level information. It has been shown previously that such information can be used to improve the performance of NER. In this work, we investigate on how to better utilize the structured information conveyed by dependency trees to improve the performance of NER. Specifically, unlike existing approaches which only exploit dependency information for designing local features, we show that certain global structured information of the dependency trees can be exploited when building NER models where such information can provide guided learning and inference. Through extensive experiments, we show that our proposed novel dependency-guided NER model performs competitively with models based on conventional semi-Markov conditional random fields, while requiring significantly less running time.

In Chinese societies, superstition is of paramount importance, and vehicle license plates with desirable numbers can fetch very high prices in auctions. Unlike other valuable items, license plates are not allocated an estimated price before auction. I propose that the task of predicting plate prices can be viewed as a natural language processing (NLP) task, as the value depends on the meaning of each individual character on the plate and its semantics. I construct a deep recurrent neural network (RNN) to predict the prices of vehicle license plates in Hong Kong, based on the characters on a plate. I demonstrate the importance of having a deep network and of retraining. Evaluated on 13 years of historical auction prices, the deep RNN outperforms previous models by a significant margin.

With the advance of acquisition techniques, plentiful higherorder tensor data sets are built up in a great variety of fields such as computer vision, neuroscience, remote sensing and recommender systems. The real-world tensors often contain missing values, which makes tensor completion become a prerequisite to utilize them. Previous studies have shown that imposing a low-rank constraint on tensor completion produces impressive performances. In this paper, we argue that low-rank constraint, albeit useful, is not effective enough to exploit the local smooth and piecewise priors of visual data. We propose integrating total variation into low-rank tensor completion (LRTC) to address the drawback. As LRTC can be formulated by both tensor unfolding and tensor decomposition, we develop correspondingly two methods, namely LRTC-TV-I and LRTC-TVII, and their iterative solvers. Extensive experimental results on color image and medical image inpainting tasks show the effectiveness and superiority of the two methods against state-of-the-art competitors.

The Minimum Weight Dominating Set (MWDS) problem is an important generalization of the Minimum Dominating Set (MDS) problem with extensive applications. This paper proposes a new local search algorithm for the MWDS problem, which is based on two new ideas. The first idea is a heuristic called two-level configuration checking (CC2), which is a new variant of a recent powerful configuration checking strategy (CC) for effectively avoiding the recent search paths. The second idea is a novel scoring function based on the frequency of being uncovered of vertices. Our algorithm is called CC2FS, according to the names of the two ideas. The experimental results show that, CC2FS performs much better than some state-of-the-art algorithms in terms of solution quality on a broad range of MWDS benchmarks.

In machine translation (MT) that involves translating between two languages with significant differences in word order, determining the correct word order of translated words is a major challenge. The dependency parse tree of a source sentence can help to determine the correct word order of the translated words. In this paper, we present a novel reordering approach utilizing a neural network and dependency-based embeddings to predict whether the translations of two source words linked by a dependency relation should remain in the same order or should be swapped in the translated sentence. Experiments on Chinese-to-English translation show that our approach yields a statistically significant improvement of 0.57 BLEU point on benchmark NIST test sets, compared to our prior state-of-the-art statistical MT system that uses sparse dependency-based reordering features.

Exact cover is the problem of finding subfamilies, S* , of a family of sets, S , over universe U , where S* forms a partition of  U .  It is a popular NP-hard problem appearing in a wide range of computer science studies. Knuth's algorithm DLX, a backtracking-based depth-first search implemented with the data structure called dancing links, is known as state-of-the-art for finding all exact covers. We propose a method to accelerate DLX. Our method constructs a Zero-suppressed Binary Decision Diagram (ZDD) that represents the set of solutions while running depth-first search in DLX. Constructing ZDDs enables the efficient use of memo cache to speed up the search. Moreover, our method has a virtue that it outputs ZDDs; we can perform several useful operations with them. Experiments confirm that the proposed method is up to several orders of magnitude faster than DLX.

The problem of automatically fixing programming errors is a very active research topic in software engineering. This is a challenging problem as fixing even a single error may require analysis of the entire program. In practice, a number of errors arise due to programmer's inexperience with the programming language or lack of attention to detail. We call these common programming errors. These are analogous to grammatical errors in natural languages. Compilers detect such errors, but their error messages are usually inaccurate. In this work, we present an end-to-end solution, called DeepFix, that can fix multiple such errors in a program without relying on any external tool to locate or fix them. At the heart of DeepFix is a multi-layered sequence-to-sequence neural network with attention which is trained to predict erroneous program locations along with the required correct statements. On a set of 6971 erroneous C programs written by students for 93 programming tasks, DeepFix could fix 1881 (27%) programs completely and 1338 (19%) programs partially.

With the rise of the Internet and social media, social mobilization - large-scale mobilization manpower for scientific, social, and political activities through crowdsourcing - has become a widespread practice. Despite the success, social mobilization is not without its limitations. Local trapping of diffusion and the dependence on highly connected individuals to mobilize people in distance locations affect the effectiveness of social mobilization. Furthermore, as empirical studies on people's responses to various social mobilization approaches are lacking, it is a significant challenge for artificial intelligence (AI) researchers to design effective and efficient decision support mechanisms to help manage this emerging phenomenon. In my thesis, I conduct large-scale empirical studies to help the AI research community establish baseline personal variabilities in different people's response patterns to social mobilization approaches. Based on the collected dataset, I will further propose computational algorithmic crowdsourcing mechanisms which leverage the empirical evidence to improve the effectiveness and efficiency of social mobilization, towards achieving superlinear productivity. Throughout this process, I will also incorporate human factors into the computational models to benefit social mobilization efforts.

Multi-Robot Task Allocation (MRTA) has no formal framework which could provide solutions covering different domains within the MRTA taxonomy without changing the optimization scheme. This research aims to develop a novel framework using evolutionary computing. The study proposes a modular approach towards developing this framework in which individual problem types of the MRTA taxonomy are solved one at a time. The performance of the framework will be evaluated against the popular approaches suggested for each problem type.

In mobile devices, the limited area of fingerprint sensors brings demand of partial fingerprint matching. Existing fingerprint authentication algorithms are mainly based on minutiae matching. However, their accuracy degrades significantly for partial-to-partial matching due to the lack of minutiae. Optical fingerprint sensor can capture very high-resolution fingerprints (2000dpi) with rich details as pores, scars, etc. These details can cover the shortage of minutiae insufficiency. In this paper, we propose a novel matching algorithm for such fingerprints, namely Deep Joint KNN-Triplet Embedding, by making good use of these subtle features. Our model employs a deep convolutional neural network (CNN) with a well-designed joint loss to project raw fingerprint images into an Euclidean space. Then we can use L2-distance to measure the similarity of two fingerprints. Experiments indicate that our model outperforms several state-of-the-art approaches.