Query: Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model

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Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder <=SEP=> Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model that lets the attention depend on the source-side syntax. Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word coverage.1

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks <=SEP=> Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. TreeLSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).

Incorporating Structural Alignment Biases into an Attentional Neural Translation Model <=SEP=> Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. However their modelling formulation is overly simplistic, and omits several key inductive biases built into traditional models. In this paper we extend the attentional neural translation model to include structural biases from word based alignment models, including positional bias, Markov conditioning, fertility and agreement over translation directions. We show improvements over a baseline attentional model and standard phrase-based model over several language pairs, evaluating on difficult languages in a low resource setting.

Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks <=SEP=> Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntaxaware versions on the English–German language pair.

Improved Neural Machine Translation with Source Syntax <=SEP=> Neural Machine Translation (NMT) based on the encoder-decoder architecture has recently achieved the state-of-the-art performance. Researchers have proven that extending word level attention to phrase level attention by incorporating source-side phrase structure can enhance the attention model and achieve promising improvement. However, word dependencies that can be crucial to correctly understand a source sentence are not always in a consecutive fashion (i.e. phrase structure), sometimes they can be in long distance. Phrase structures are not the best way to explicitly model long distance dependencies. In this paper we propose a simple but effective method to incorporate source-side long distance dependencies into NMT. Our method based on dependency trees enriches each source state with global dependency structures, which can better capture the inherent syntactic structure of source sentences. Experiments on Chinese-English and English-Japanese translation tasks show that our proposed method outperforms state-of-the-art SMT and NMT baselines.

Diversified Visual Attention Networks for Fine-Grained Object Classification <=SEP=> Fine-grained object classification attracts increasing attention in multimedia applications. However, it is a quite challenging problem due to the subtle interclass difference and large intraclass variation. Recently, visual attention models have been applied to automatically localize the discriminative regions of an image for better capturing critical difference, which have demonstrated promising performance. Unfortunately, without consideration of the diversity in attention process, most of existing attention models perform poorly in classifying fine-grained objects. In this paper, we propose a diversified visual attention network (DVAN) to address the problem of fine-grained object classification, which substantially relieves the dependency on strongly supervised information for learning to localize discriminative regions com-pared with attention-less models. More importantly, DVAN explicitly pursues the diversity of attention and is able to gather discriminative information to the maximal extent. Multiple attention canvases are generated to extract convolutional features for attention. An LSTM recurrent unit is employed to learn the attentiveness and discrimination of attention canvases. The proposed DVAN has the ability to attend the object from coarse to fine granularity, and a dynamic internal representation for classification is built up by incrementally combining the information from different locations and scales of the image. Extensive experiments conducted on CUB-2011, Stanford Dogs, and Stanford Cars datasets have demonstrated that the pro-posed DVAN achieves competitive performance compared to the state-of-the-art approaches, without using any prior knowledge, user interaction, or external resource in training and testing.

Performance evaluation of local colour invariants <=SEP=> In this paper, we compare local colour descriptors to grey-value descriptors. We adopt the evaluation framework of Mikolayzcyk and Schmid. We modify the framework in several ways. We decompose the evaluation framework to the level of local grey-value invariants on which common region descriptors are based. We compare the discriminative power and invariance of grey-value invariants to that of colour invariants. In addition, we evaluate the invariance of colour descriptors to photometric events such as shadow and highlights. We measure the performance over an extended range of common recording conditions including significant photometric variation. We demonstrate the intensity-normalized colour invariants and the shadow invariants to be highly distinctive, while the shadow invariants are more robust to both changes of the illumination colour, and to changes of the shading and shadows. Overall, the shadow invariants perform best: they are most robust to various imaging conditions while maintaining discriminative power. When plugged into the SIFT descriptor, they show to outperform other methods that have combined colour information and SIFT. The usefulness of C-colour-SIFT for realistic computer vision applications is illustrated for the classification of object categories from the VOC challenge, for which a significant improvement is reported. 2008 Elsevier Inc. All rights reserved.

Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction <=SEP=> Web search result clustering aims to facilitate information search on the Web. Rather than the results of a query being presented as a flat list, they are grouped on the basis of their similarity and subsequently shown to the user as a list of clusters. Each cluster is intended to represent a different meaning of the input query, thus taking into account the lexical ambiguity (i.e., polysemy) issue. Existing Web clustering methods typically rely on some shallow notion of textual similarity between search result snippets, however. As a result, text snippets with no word in common tend to be clustered separately even if they share the same meaning, whereas snippets with words in common may be grouped together even if they refer to different meanings of the input query.In this article we present a novel approach to Web search result clustering based on the automatic discovery of word senses from raw text, a task referred to as Word Sense Induction. Key to our approach is to first acquire the various senses (i.e., meanings) of an ambiguous query and then cluster the search results based on their semantic similarity to the word senses induced. Our experiments, conducted on data sets of ambiguous queries, show that our approach outperforms both Web clustering and search engines.

Towards multi-cue urban curb recognition <=SEP=> This paper presents a multi-cue approach to curb recognition in urban traffic. We propose a novel texture-based curb classifier using local receptive field (LRF) features in conjunction with a multi-layer neural network. This classification module operates on both intensity images and on three-dimensional height profile data derived from stereo vision. We integrate the proposed multi-cue curb classifier as an additional measurement module into a state-of-the-art Kaiman filter-based urban lane recognition system. Our experiments involve a challenging real-world dataset captured in urban traffic with manually labeled ground-truth. We quantify the benefit of the proposed multi-cue curb classifier in terms of the improvement in curb localization accuracy of the integrated system. Our results indicate a 25% reduction of the average curb localization error at real-time processing speeds.

An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market <=SEP=> This paper evaluates the performance of a number of modelling approaches for future mortgage default status. Boosted regression trees, random forests, penalised linear and semi-parametric logistic regression models are applied to four portfolios of over 300,000 Irish owner-occupier mortgages. The main findings are that the selected approaches have varying degrees of predictive power and that boosted regression trees significantly outperform logistic regression. This suggests that boosted regression trees can be a useful addition to the current toolkit for mortgage credit risk assessment by banks and regulators.

Can Personality Be Changed ? The Role of Beliefs in Personality and Change <=SEP=> Using recent research, I argue that beliefs lie at the heart of personality and adaptive functioning and that they give us unique insight into how personality and functioning can be changed. I focus on two classes of beliefs—beliefs about the malleability of self-attributes and expectations of social acceptance versus rejection—and show how modest interventions have brought about important real-world changes. I conclude by suggesting that beliefs are central to the way in which people package their experiences and carry them forward, and that beliefs should play a more central role in the study of personality. KEYWORDS—personality; personality theories; change; self-beliefs; interventions

Business Intelligence and Business Process Management in Banking Operations <=SEP=> Success of banking operations is strongly correlated with the quality of customer relations and efficacy of banks processes. Banks seek means for efficient analysis of vast amount of gathered data from their IT systems. They are exploiting business intelligence (BI) technology to analyze every aspect of their data to understand behavior of their clients, striving to satisfy client's needs, in an endless race for a competitive advantage in the market. The intention of this paper is to review and discuss the most significant applications of business intelligence in banking as well as point to new technology trends that will affect bank's development.

Pitch Extraction and Fundamental Frequency: History and Current Techniques <=SEP=> Pitch extraction (also called fundamental frequency estimation) has been a popular topic in many fields of research since the age of computers. Yet in the course of some 50 years of study, current techniques are still not to a desired level of accuracy and robustness. When presented with a single clean pitched signal, most techniques do well, but when the signal is noisy, or when there are multiple pitch streams, many current pitch algorithms still fail to perform well. This report presents a discussion of the history of pitch detection techniques, as well as a survey of the current state of the art in pitch detection technology.

High Performance Methods for Linked Open Data Connectivity Analytics <=SEP=> The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or “slices” (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.

Information Technology, Materiality, and Organizational Change: A Professional Odyssey <=SEP=> We begin with a retrospective reflection on the first author’s research career, which in large part is devoted to research about the implications of information technology (IT) for organizational change. Although IT has long been associated with organizational change, our historical review of the treatment of technology in organization theory demonstrates how easily the material aspects of organizations can disappear into the backwaters of theory development. This is an unfortunate result since the material characteristics of IT initiatives distinguish them from other organizational change initiatives. Our aim is to restore materiality to studies of IT impact by tracing the reasons for its disappearance and by offering options in which IT’s materiality plays a more central theoretical role. We adopt a socio-technical perspective that differs from a strict sociomaterial perspective insofar as we wish to preserve the ontological distinction between material artifacts and their social context of use. Our analysis proceeds using the concept of “affordance” as a relational concept consistent with the socio-technical perspective. We then propose extensions of organizational routines theory that incorporate material artifacts in the generative system known as routines. These contributions exemplify two of the many challenges inherent in adopting materiality as a new research focus in the study of IT’s organizational impacts.

Electronic travel aids for the blind based on sensory substitution <=SEP=> With the development of advanced human-machine interface, effective information processing algorithms, and more powerful microprocessors, it is possible to enable the blind to achieve additional perception of the environment. In 1970s, the blind-assistant facility based on sensory substitution was introduced[1]. It called as ETA (short for Electronic Travel Aids), bases on the other natural senses of the blind, such as hearing, touch, smell, feeling and etc. Then the research of vision aids has been broadly extended. After introducing the traditional methods for guiding blind, two typical input modes of travel aid are presented in this paper. One is sonar-based ETAs and the other technique is camera-based ETAs. Its principle of each modes is discussed in detail. Besides these, comparisons between the above two techniques have also been indicated in this paper.

0-knowledge fuzzing <=SEP=> Nowadays fuzzing is a pretty common technique used both by attackers and software developers. Currently known techniques usually involve knowing the protocol/format that needs to be fuzzed and having a basic understanding of how the user input is processed inside the binary. In the past since fuzzing was little-used obtaining good results with a small amount of effort was possible. Today finding bugs requires digging a lot inside the code and the user-input as common vulnerabilies are already identified and fixed by developers. This paper will present an idea on how to effectively fuzz with no knowledge of the userinput and the binary. Specifically the paper will demonstrate how techniques like code coverage, data tainting and inmemory fuzzing allow to build a smart fuzzer with no need to instrument it.

Personal Attributes Extraction in Chinese Text Based on Recurrent Neural Network <=SEP=> Personal Attributes Extraction in Chinese Text Task is designed to extract person specific attributes, for example, spouse/husband, children, education, title etc. from unstructured Chinese texts. According to the characteristics of the personal attributes extraction, we propose a new method based on deep learning to extract attributes from the documents in this paper. Experiments show that this method can improve the accuracy of personal attributes extraction effectively.

Strategic Human Resources Management : Where Do We Go From Here ? † <=SEP=> The authors identify the key challenges facing strategic human resource management (SHRM) going forward and discuss several new directions in both the scholarship and practice of SHRM. They focus on a clearer articulation of the “black box” between HR and firm performance, emphasizing the integration of strategy implementation as the central mediating variable in this relationship. There are direct implications for the nature of fit and contingencies in SHRM. They also highlight the significance of a differentiated HR architecture not just across firms but also within firms.

Edlines: Real-time line segment detection by Edge Drawing (ed) <=SEP=> We propose a linear time line segment detector that gives accurate results, requires no parameter tuning, and runs up to 11 times faster than the fastest known line segment detection algorithm in the literature; namely, the LSD by Gioi et al. The proposed algorithm also includes a line validation step due to the Helmholtz principle, which lets it control the number of false detections. Our detector makes use of the clean, contiguous (connected) chain of edge pixels produced by our novel edge detector, the Edge Drawing (ED) algorithm; hence the name EDLines. With its accurate results and blazing speed, EDLines will be very suitable for the next generation real-time computer vision applications.

Who does what during a code review? Datasets of OSS peer review repositories <=SEP=> We present four datasets that are focused on the general roles of OSS peer review members. With data mined from both an integrated peer review system and code source repositories, our rich datasets comprise of peer review data that was automatically recorded. Using the Android project as a case study, we describe our extraction methodology, the datasets and their application used for three separate studies. Our datasets are available online at http://sdlab.naist.jp/reviewmining/.

HEXHOOP: Modular Templates for Converting a Hex-Dominant Mesh to an ALL-Hex Mesh <=SEP=> This paper presents a new mesh conversion template called HEXHOOP, which fully automates a con-version from a hex-dominant mesh to an all-hex mesh. A HEXHOOP template subdivides a hex/prism/pyramid element into a set of smaller hex elements while main-taining the topological conformity with neighboring elements. A HEXHOOP template is constructed by assembling sub-templates, cores and caps. A dicing template for a hex and a prism is constructed by choosing the appropriate combination of a core and caps. A template that dices a pyramid without losing conformity to the adjacent element is derived from a HEXHOOP template. Some experimental results show that the HEXHOOP templates successfully convert a hex-dominant mesh into an all-hex mesh.

Chat Speed OP : Practices of Coherence in Massive Twitch Chat <=SEP=> Paste the appropriate copyright statement here. ACM now supports three different copyright statements: • ACM copyright: ACM holds the copyright on the work. This is the historical approach. • License: The author(s) retain copyright, but ACM receives an exclusive publication license. • Open Access: The author(s) wish to pay for the work to be open access. The additional fee must be paid to ACM. This text field is large enough to hold the appropriate release statement assuming it is single spaced in a sans-serif 7 point font. Every submission will be assigned their own unique DOI string to be included here. Abstract Twitch.tv, a streaming platform known for video game content, has grown tremendously since its inception in 2011. We examine communication practices in Twitch chats for the popular game Hearthstone, comparing massive chats with at least 10,000 concurrent viewers and small chats with fewer than 2,000 concurrent viewers. Due to the large scale and fast pace of massive chats, communication patterns no longer follow models developed in previous studies of computer-mediated communication. Rather than what other studies have described as communication breakdowns and information overload, participants in massive chats communicate in what we call “crowdspeak.”

A Mobile Transaction Model That Captures Both the Data and Movement Behavior <=SEP=> Unlike distributed transactions, mobile transactions do not originate and end at the same site. The implication of the movement of such transactions is that classical atomicity, concurrency and recovery solutions must be revisited to capture the movement behavior. As an effort in this direction, we define a model of mobile transactions by building on the concepts of split transactions and global transactions in a multidatabase environment. Our view of mobile transactions, called Kangaroo Transactions, incorporates the property that transactions in a mobile computing system hop from one base station to another as the mobile unit moves through cells. Our model is the first to capture this movement behavior as well as the data behavior which reflects the access to data located in databases throughout the static network. The mobile behavior is dynamic and is realized in our model via the use of split operations. The data access behavior is captured by using the idea of global and local transactions in a multidatabase system.

Birds of a ‘ ‘ bad ’ ’ feather flock together : The Dark Triad and mate choice <=SEP=> Previous research on the Dark Triad traits (i.e., Machiavellianism, psychopathy, and narcissism) has focused solely on the role the Dark Triad traits played in mate choice of actors. The current study (N = 336) extends this by manipulating the apparent levels of Dark Triad traits in targets and correlating mate choice in these targets with individual differences in the Dark Triad traits in actors. As expected, both sexes preferred partners low in the Dark Triad traits for long-term mating, while those high in these traits were preferred for one-night stands. However, women high in psychopathy considered the Dark Triad traits in potential male partners more physically attractive and desirable for an one-night stand, as well as a potential husband. Men who were high on psychopathy were likewise attracted to psychopathy in potential mothers. Our findings are discussed from an evolutionary personality paradigm. 2015 Elsevier Ltd. All rights reserved.

Fast Metric Tracking by Detection System: Radar Blob and Camera Fusion <=SEP=> This article proposes a system that fuses radar and monocular vision sensor data in order to detect and classify on-road obstacles, like cars or not cars (other obstacles). The obstacle detection process and classification is divided into three stages, the first consist in reading radar signals and capturing the camera data, the second stage is the data fusion, and the third step is the classify the obstacles, aiming to differentiate the obstacles types identified by the radar and confirmed by the computer vision. In the detection task it is important to locate, measure, and rank the obstacles to be able to take adequate decisions and actions (e.g. Generate alerts, autonomously control the vehicle path), so the correct classification of the obstacle type and position, is very important, also avoiding false positives and/or false negatives in the classification task.

Learning Distributed Representations of Texts and Entities from Knowledge Base <=SEP=> We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.

Automatic removal of manually induced artefacts in ultrasound images of thyroid gland <=SEP=> Manually induced artefacts, like caliper marks and anatomical labels, render an ultrasound (US) image incapable of being subjected to further processes of Computer Aided Diagnosis (CAD). In this paper, we propose a technique to remove these artefacts and restore the image as accurately as possible. The technique finds application as a pre-processing step when developing unsupervised segmentation algorithms for US images that deal with automatic estimation of the number of segments and clustering. The novelty of the algorithm lies in the image processing pipeline chosen to automatically identify the artefacts and is developed based on the histogram properties of the artefacts. The algorithm was able to successfully restore the images to a high quality when it was executed on a dataset of 18 US images of the thyroid gland on which the artefacts were induced manually by a doctor. Further experiments on an additional dataset of 10 unmarked US images of the thyroid gland on which the artefacts were simulated using Matlab showed that the restored images were again of high quality with a PSNR > 38dB and free of any manually induced artefacts.

Comparison of automatic shot boundary detection algorithms <=SEP=> Various methods of automatic shot boundary detection have been proposed and claimed to perform reliably. A the detection of edits is fundamental to any kind of video analysis since it segments a video into its basic com the shots, only few comparative investigations on early shot boundary detection algorithms have been pu These investigations mainly concentrate on measuring the edit detection performance, however, do not con algorithms’ ability to classify the types and to locate the boundaries of the edits correctly. This paper extend comparative investigations. More recent algorithms designed explicitly to detect specific complex editing ope such as fades and dissolves are taken into account, and their ability to classify the types and locate the boun such edits are examined. The algorithms’ performance is measured in terms of hit rate, number of false hits, a rate for hard cuts, fades, and dissolves over a large and diverse set of video sequences. The experiments while hard cuts and fades can be detected reliably, dissolves are still an open research issue. The false hit rat solves is usually unacceptably high, ranging from 50% up to over 400%. Moreover, all algorithms seem to fai roughly the same conditions.

Bayesian Classification (AutoClass): Theory and Results <=SEP=> We describe AutoClass, an approach to unsupervised classiication based upon the classical mixture model, supplemented by a Bayesian method for determining the optimal classes. We include a moderately detailed exposition of the mathematics behind the AutoClass system. We emphasize that no current unsupervised classiication system can produce maximally useful results when operated alone. It is the interaction between domain experts and the machine searching over the model space, that generates new knowledge. Both bring unique information and abilities to the database analysis task, and each enhances the others' eeectiveness. We illustrate this point with several applications of AutoClass to complex real world databases, and describe the resulting successes and failures. 6.1 Introduction This chapter is a summary of our experience in using an automatic classiication program (AutoClass) to extract useful information from databases. It also gives an outline of the principles that underlie automatic classiication in general, and AutoClass in particular. We are concerned with the problem of automatic discovery of classes in data (sometimes called clustering, or unsupervised learning), rather than the generation of class descriptions from labeled examples (called supervised learning). In some sense, automatic classiication aims at discovering the \natural" classes in the data. These classes reeect basic causal mechanisms that makes some cases look more like each other than the rest of the cases. The causal mechanisms may be as boring as sample biases in the data, or could reeect some major new discovery in the domain. Sometimes, these classes were well known to experts in the eld, but unknown to AutoClass, and other times