Suggesting Accurate Method and Class Names

Descriptive names are a vital part of readable, and hence maintain-able, code. Recent progress on automatically suggesting names for local variables tantalizes with the prospect of replicating that success with method and class names. However, suggesting names for meth-ods and classes is much more difficult. This is because good method and class names need to be functionally descriptive, but suggesting such names requires that the model goes beyond local context. We introduce a neural probabilistic language model for source code that is specifically designed for the method naming problem. Our model learns which names are semantically similar by assigning them to locations, called embeddings, in a high-dimensional contin-uous space, in such a way that names with similar embeddings tend to be used in similar contexts. These embeddings seem to contain semantic information about tokens, even though they are learned only from statistical co-occurrences of tokens. Furthermore, we introduce a variant of our model that is, to our knowledge, the first that can propose neologisms, names that have not appeared in the training corpus. We obtain state of the art results on the method, class, and even the simpler variable naming tasks. More broadly, the continuous embeddings that are learned by our model have the potential for wide application within software engineering

Semantic Embedding Space for Zero-Shot Action Recognition

The number of categories for action recognition is growing rapidly. It is thus becoming increasingly hard to collect sufficient training data to learn conventional models for each category. This issue may be ameliorated by the increasingly popular 'zero-shot learning' (ZSL) paradigm. In this framework a mapping is constructed between visual features and a human interpretable semantic description of each category, allowing categories to be recognised in the absence of any training data. Existing ZSL studies focus primarily on image data, and attribute-based semantic representations. In this paper, we address zero-shot recognition in contemporary video action recognition tasks, using semantic word vector space as the common space to embed videos and category labels. This is more challenging because the mapping between the semantic space and space-time features of videos containing complex actions is more complex and harder to learn. We demonstrate that a simple self-training and data augmentation strategy can significantly improve the efficacy of this mapping. Experiments on human action datasets including HMDB51 and UCF101 demonstrate that our approach achieves the state-of-the-art zero-shot action recognition performance.Comment: 5 page

A cascaded approach to normalising gene mentions in biomedical literature

Linking gene and protein names mentioned in the literature to unique identifiers in referent genomic databases is an essential step in accessing and integrating knowledge in the biomedical domain. However, it remains a challenging task due to lexical and terminological variation, and ambiguity of gene name mentions in documents. We present a generic and effective rule-based approach to link gene mentions in the literature to referent genomic databases, where pre-processing of both gene synonyms in the databases and gene mentions in text are first applied. The mapping method employs a cascaded approach, which combines exact, exact-like and token-based approximate matching by using flexible representations of a gene synonym dictionary and gene mentions generated during the pre-processing phase. We also consider multi-gene name mentions and permutation of components in gene names. A systematic evaluation of the suggested methods has identified steps that are beneficial for improving either precision or recall in gene name identification. The results of the experiments on the BioCreAtIvE2 data sets (identification of human gene names) demonstrated that our methods achieved highly encouraging results with F-measure of up to 81.20%

Evolutionary history of LINE-1 in the major clades of placental mammals

BACKGROUND: LINE-1 constitutes an important component of mammalian genomes. It has a dynamic evolutionary history characterized by the rise, fall and replacement of subfamilies. Most data concerning LINE-1 biology and evolution are derived from the human and mouse genomes and are often assumed to hold for all placentals. METHODOLOGY: To examine LINE-1 relationships, sequences from the 3′ region of the reverse transcriptase from 21 species (representing 13 orders across Afrotheria, Xenarthra, Supraprimates and Laurasiatheria) were obtained from whole genome sequence assemblies, or by PCR with degenerate primers. These sequences were aligned and analysed. PRINCIPAL FINDINGS: Our analysis reflects accepted placental relationships suggesting mostly lineage-specific LINE-1 families. The data provide clear support for several clades including Glires, Supraprimates, Laurasiatheria, Boreoeutheria, Xenarthra and Afrotheria. Within the afrotherian LINE-1 (AfroLINE) clade, our tree supports Paenungulata, Afroinsectivora and Afroinsectiphillia. Xenarthran LINE-1 (XenaLINE) falls sister to AfroLINE, providing some support for the Atlantogenata (Xenarthra+Afrotheria) hypothesis. SIGNIFICANCE: LINEs and SINEs make up approximately half of all placental genomes, so understanding their dynamics is an essential aspect of comparative genomics. Importantly, a tree of LINE-1 offers a different view of the root, as long edges (branches) such as that to marsupials are shortened and/or broken up. Additionally, a robust phylogeny of diverse LINE-1 is essential in testing that site-specific LINE-1 insertions, often regarded as homoplasy-free phylogenetic markers, are indeed unique and not convergent

Context-aware person identification in personal photo collections

Identifying the people in photos is an important need for users of photo management systems. We present MediAssist, one such system which facilitates browsing, searching and semi-automatic annotation of personal photos, using analysis of both image content and the context in which the photo is captured. This semi-automatic annotation includes annotation of the identity of people in photos. In this paper, we focus on such person annotation, and propose person identification techniques based on a combination of context and content. We propose language modelling and nearest neighbor approaches to context-based person identification, in addition to novel face color and image color content-based features (used alongside face recognition and body patch features). We conduct a comprehensive empirical study of these techniques using the real private photo collections of a number of users, and show that combining context- and content-based analysis improves performance over content or context alone

Automatic Generation of Text Descriptive Comments for Code Blocks

We propose a framework to automatically generate descriptive comments for source code blocks. While this problem has been studied by many researchers previously, their methods are mostly based on fixed template and achieves poor results. Our framework does not rely on any template, but makes use of a new recursive neural network called Code-RNN to extract features from the source code and embed them into one vector. When this vector representation is input to a new recurrent neural network (Code-GRU), the overall framework generates text descriptions of the code with accuracy (Rouge-2 value) significantly higher than other learning-based approaches such as sequence-to-sequence model. The Code-RNN model can also be used in other scenario where the representation of code is required.Comment: aaai 201