Query: Modeling Coverage for Neural Machine Translation

Relevant passages:
On Using Very Large Target Vocabulary for Neural Machine Translation <=SEP=> Neural machine translation, a recently proposed approach to machine translation based purely on neural networks, has shown promising results compared to the existing approaches such as phrasebased statistical machine translation. Despite its recent success, neural machine translation has its limitation in handling a larger vocabulary, as training complexity as well as decoding complexity increase proportionally to the number of target words. In this paper, we propose a method based on importance sampling that allows us to use a very large target vocabulary without increasing training complexity. We show that decoding can be efficiently done even with the model having a very large target vocabulary by selecting only a small subset of the whole target vocabulary. The models trained by the proposed approach are empirically found to match, and in some cases outperform, the baseline models with a small vocabulary as well as the LSTM-based neural machine translation models. Furthermore, when we use an ensemble of a few models with very large target vocabularies, we achieve performance comparable to the state of the art (measured by BLEU) on both the English→German and English→French translation tasks of WMT’14.

Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation <=SEP=> The attentional mechanism has proven to be effective in improving end-to-end neural machine translation. However, due to the intricate structural divergence between natural languages, unidirectional attention-based models might only capture partial aspects of attentional regularities. We propose agreement-based joint training for bidirectional attention-based end-to-end neural machine translation. Instead of training source-to-target and target-to-source translation models independently, our approach encourages the two complementary models to agree on word alignment matrices on the same training data. Experiments on ChineseEnglish and English-French translation tasks show that agreement-based joint training significantly improves both alignment and translation quality over independent training.

Long Short-Term Memory <=SEP=> Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

Alignment by Agreement <=SEP=> We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions. Disciplines Computer Sciences Comments Percy Liang, Ben Taskar, and Dan Klein. 2006. Alignment by agreement. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL '06). Association for Computational Linguistics, Stroudsburg, PA, USA, 104-111. DOI=10.3115/1220835.1220849 http://dx.doi.org/10.3115/1220835.1220849 © ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, {(2006)} http://doi.acm.org/10.3115/1220835.1220849" Email permissions@acm.org This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/533 Alignment by Agreement Percy Liang UC Berkeley Berkeley, CA 94720 pliang@cs.berkeley.edu Ben Taskar UC Berkeley Berkeley, CA 94720 taskar@cs.berkeley.edu Dan Klein UC Berkeley Berkeley, CA 94720 klein@cs.berkeley.edu

Statistical Phrase-Based Translation <=SEP=> We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy wordlevel alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems.

Fan-Beam Millimeter-Wave Antenna Design Based on the Cylindrical Luneberg Lens <=SEP=> A new design of two-dimensional cylindrical Luneberg lens is introduced based on TE10 mode propagation between parallel plates, with special focus on ease of manufacturing. The parallel plates are partially filled with low cost polymer material (Rexolite epsivr = 2.54) to match Luneberg's law. A planar linear tapered slot antenna (LTSA) is inserted into the air region between the parallel plates at the edge of the Luneberg lens as a feed antenna, with fine positioning to the focal point of the Luneberg lens to optimize the antenna system performance. A combined ray-optics/diffraction method is used to obtain the radiation pattern of the system and results are compared with predictions of a time domain numerical solver. Measurements done on a 10-cm Luneberg lens designed for operation at 30 GHz agree very well with predictions. For this prototype, 3-dB E- and if-plane beamwidths of 6.6deg and 54deg respectively were obtained, and the sidelobe level in the E-plane was -17.7-dB. Although the parallel plate configuration should lead to a narrow band design due to the dispersion characteristics of the TE10 mode, the measurement results demonstrate broadband characteristics with radiation efficiencies varying between 43% and 72% over the tested frequency band of 26.5-37 GHz. The designed cylindrical Luneberg lens can be used to launch multiple beams by implementing an arc array of planar LTSA elements at the periphery of the lens, and can be easily extended to higher mm-wave frequencies.

SINGO: A single-end-operative and genderless connector for self-reconfiguration, self-assembly and self-healing <=SEP=> Flexible and reliable connection is critical for self-reconfiguration, self-assembly, or self-healing. However, most existing connection mechanisms suffer from a deficiency that a connection would seize itself if one end malfunctions or is out of service. To mitigate this limitation on self-healing, this paper presents a new SINGO connector that can establish or disengage a connection even if one end of the connection is not operational. We describe the design and the prototype of the connector and demonstrate its performance by both theoretical analysis and physical experimentations.

Content matters: A study of hate groups detection based on social networks analysis and web mining <=SEP=> In recent years, with rapid growth of social networking websites, users are very active in these platforms and large amount of data are aggregated. Among those social networking websites, Facebook is the most popular website that has most users. However, in Facebook, the abusing problem is a very critical issue, such as Hate Groups. Therefore, many researchers are devoting on how to detect potential hate groups, such as using the techniques of social networks analysis. However, we believe content is also a very important factors for hate groups detection. Thus, in this paper, we will propose an architecture to for hate groups detection which is based on the technique of Social Networks Analysis and Web Mining (Text Mining; Natural Language Processing). From the experiment result, it shows that content plays an critical role for hate groups detection and the performance is better than the system that just applying social networks analysis.

Occupational risks and challenges of seafaring. <=SEP=> UNLABELLED
Seafarers are exposed to a high diversity of occupational health hazards onboard ships.


OBJECTIVE
The aim of this article is to present a survey of the current, most important hazards in seafaring including recommendations on measures how to deal with these problems.


METHODS
The review is based on maritime expert opinions as well a PubMed analysis related to the occupational risks of seafaring.


RESULTS
Despite recent advances in injury prevention, accidents due to harmful working and living conditions at sea and of non-observance of safety rules remain a main cause of injury and death. Mortality in seafaring from cardiovascular diseases (CVD) is mainly caused by increased risks and impaired treatment options of CVD at sea. Further, shipboard stress and high demand may lead to fatigue and isolation which have an impact on the health of onboard seafarers. Communicable diseases in seafaring remain an occupational problem. Exposures to hazardous substances and UV-light are important health risks onboard ships. Because of harsh working conditions onboard including environmental conditions, sufficient recreational activities are needed for the seafarers' compensation both onboard and ashore. However, in reality there is often a lack of leisure time possibilities.


DISCUSSION
Seafaring is still an occupation with specific work-related risks. Thus, a further reduction of occupational hazards aboard ships is needed and poses a challenge for maritime health specialists and stakeholders. Nowadays, maritime medicine encompasses a broad field of workplaces with different job-related challenges.

IoT Security: Ongoing Challenges and Research Opportunities <=SEP=> The Internet of Things (IoT) opens opportunities for wearable devices, home appliances, and software to share and communicate information on the Internet. Given that the shared data contains a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we begin with general information security background of IoT and continue on with information security related challenges that IoT will encountered. Finally, we will also point out research directions that could be the future work for the solutions to the security challenges that IoT encounters.

Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization <=SEP=> In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., backpropagation for multilabel learning, is proposed. It is derived from the popular backpropagation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms

Investigating Information Systems with Action Research <=SEP=> Action research is an established research method in use in the social and medical sciences since the mid-twentieth century, and has increased in importance for information systems toward the end of the 1990s. Its particular philosophic context is couched in strongly post-positivist assumptions such as idiographic and interpretive research ideals. Action research has developed a history within information systems that can be explicitly linked to early work by Lewin and the Tavistock Institute. Action research varies in form, and responds to particular problem domains. The most typical form is a participatory method based on a five-step model, which is exemplified by published IS research.

Chapter 2 Depression and a Stepped Care Model <=SEP=> Given the public health significance of depression and the limited resources available for providing evidence-based treatment, there is a need to develop effective models of care to reduce the personal and societal costs of the disorder. Within stepped care service provisions, all patients presenting with symptoms of depression generally are first offered the lowest intensity and least intrusive intervention deemed necessary following assessment and triage. Only when patients do not show improvement do they move to higher, more intensive levels of care. However, stepped care models also provide information to aid clinicians in decision making regarding selection of treatment strategies that are most appropriate for an individual patient. For some individuals, lower levels of care would never be appropriate or may not be preferred by the consumer. Thus, stepped interventions offer a variety of treatment options to match the intensity of the patient’s presenting problem as well as potential patient preference. In this chapter, we discuss various strategies for treating depression consistent with a stepped model of care beginning with least intensive treatment and then moving up through the hierarchy of steps of care. While this is not a comprehensive review of all available treatments for depression, the chapter is designed to make clinicians aware of specific strategies for addressing depressive symptoms and to provide guidance about resources available at the various levels of care.

vSlicer: latency-aware virtual machine scheduling via differentiated-frequency CPU slicing <=SEP=> Recent advances in virtualization technologies have made it feasible to host multiple virtual machines (VMs) in the same physical host and even the same CPU core, with fair share of the physical resources among the VMs. However, as more VMs share the same core/CPU, the CPU access latency experienced by each VM increases substantially, which translates into longer I/O processing latency perceived by I/O-bound applications. To mitigate such impact while retaining the benefit of CPU sharing, we introduce a new class of VMs called latency-sensitive VMs (LSVMs), which achieve better performance for I/O-bound applications while maintaining the same resource share (and thus cost) as other CPU-sharing VMs. LSVMs are enabled by vSlicer, a hypervisor-level technique that schedules each LSVM more frequently but with a smaller micro time slice. vSlicer enables more timely processing of I/O events by LSVMs, without violating the CPU share fairness among all sharing VMs. Our evaluation of a vSlicer prototype in Xen shows that vSlicer substantially reduces network packet round-trip times and jitter and improves application-level performance. For example, vSlicer doubles both the connection rate and request processing throughput of an Apache web server; reduces a VoIP server's upstream jitter by 62%; and shortens the execution times of Intel MPI benchmark programs by half or more.

CENTURION: Incentivizing multi-requester mobile crowd sensing <=SEP=> The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations.

FEATURE EXTRACTION USING SURF ALGORITHM FOR OBJECT RECOGNITION <=SEP=> Video surveillance is active research topic in computer vision research area for humans & vehicles, so it is used over a great extent. Multiple images generated using a fixed camera contains various objects, which are taken under different variations, illumination changes after that the object’s identity and orientation are provided to the user. This scheme is used to represent individual images as well as various objects classes in a single, scale and rotation invariant model.The objective is to improve object recognition accuracy for surveillance purposes & to detect multiple objects with sufficient level of scale invariance.Multiple objects detection& recognition is important in the analysis of video data and higher level security system. This method can efficiently detect the objects from query images as well as videos by extracting frames one by one. When given a query image at runtime, by generating the set of query features and it will find best match it to other sets within the database. Using SURF algorithm find the database object with the best feature matching, then object is present in the query image. Keywords— Image recognition, Query image, Local feature, Surveillance system, SURF algorithm.

Challenges and business models for mobile location-based services and advertising <=SEP=> Mobile advertising will become more pervasive and profitable, but not before addressing key technical and business challenges.

Efficient Hierarchical Identity-Based Signature With Batch Verification for Automatic Dependent Surveillance-Broadcast System <=SEP=> The automatic-dependent surveillance-broad-cast (ADS-B) is generally regarded as the most important module in air traffic surveillance technology. To obtain better airline security, ADS-B system will be deployed in most airspace by 2020, where aircraft will be equipped with an ADS-B device that periodically broadcasts messages to other aircraft and ground station controllers. Due to the open communication environment, the ADS-B system is subject to a broad range of attacks. To simultaneously implement both integrity and authenticity of messages transmitted in the ADS-B system, Yang et al. proposed a new authentication frame based on the three-level hierarchical identity-based signature (TLHIBS) scheme with batch verification, as well as constructing two schemes for the ADS-B system. However, neither TLHIBS schemes are sufficiently lightweight for practical deployment due to the need for complex hash-to-point operation or expensive certification management. In this paper, we construct an efficient TLHIBS scheme with batch verification for the ADS-B system. Our scheme does not require hash-to-point operation or (expensive) certification management. We then prove the TLHIBS scheme secure in the random oracle model. We also demonstrate the practicality of the scheme using experiments, whose findings indicate that the TLHIBS scheme supports attributes required by the ADS-B system without the computation cost in Chow et al.'s scheme and Yang et al.'s TLHIBS schemes.

Workplace harassment: double jeopardy for minority women. <=SEP=> To date there have been no studies of how both sex and ethnicity might affect the incidence of both sexual and ethnic harassment at work. This article represents an effort to fill this gap. Data from employees at 5 organizations were used to test whether minority women are subject to double jeopardy at work, experiencing the most harassment because they are both women and members of a minority group. The results supported this prediction. Women experienced more sexual harassment than men, minorities experienced more ethnic harassment than Whites, and minority women experienced more harassment overall than majority men, minority men, and majority women.

Exposure of Children and Adolescents to Alcohol Marketing on Social Media Websites <=SEP=> AIMS
In 2011, online marketing became the largest marketing channel in the UK, overtaking television for the first time. This study aimed to describe the exposure of children and young adults to alcohol marketing on social media websites in the UK.


METHODS
We used commercially available data on the three most used social media websites among young people in the UK, from December 2010 to May 2011. We analysed by age (6-14 years; 15-24 years) and gender the reach (proportion of internet users who used the site in each month) and impressions (number of individual pages viewed on the site in each month) for Facebook, YouTube and Twitter. We further analysed case studies of five alcohol brands to assess the marketer-generated brand content available on Facebook, YouTube and Twitter in February and March 2012.


RESULTS
Facebook was the social media site with the highest reach, with an average monthly reach of 89% of males and 91% of females aged 15-24. YouTube had a similar average monthly reach while Twitter had a considerably lower usage in the age groups studied. All five of the alcohol brands studied maintained a Facebook page, Twitter page and YouTube channel, with varying levels of user engagement. Facebook pages could not be accessed by an under-18 user, but in most cases YouTube content and Twitter content could be accessed by those of all ages.


CONCLUSION
The rise in online marketing of alcohol and the high use of social media websites by young people suggests that this is an area requiring further monitoring and regulation.

An implementation of the FP-growth algorithm <=SEP=> The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. In this paper I describe a C implementation of this algorithm, which contains two variants of the core operation of computing a projection of an FP-tree (the fundamental data structure of the FP-growth algorithm). In addition, projected FP-trees are (optionally) pruned by removing items that have become infrequent due to the projection (an approach that has been called FP-Bonsai). I report experimental results comparing this implementation of the FP-growth algorithm with three other frequent item set mining algorithms I implemented (Apriori, Eclat, and Relim).

An analytical method for diseases prediction using machine learning techniques <=SEP=> 

Deep Reinforcement Learning for General Video Game AI <=SEP=> The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search. In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.

Working Memory Capacity as Executive Attention <=SEP=> Performance on measures of working memory (WM) capacity predicts performance on a wide range of real-world cognitive tasks. I review the idea that WM capacity (a) is separable from short-term memory, (b) is an important component of general fluid intelligence, and (c) represents a domainfree limitation in ability to control attention. Studies show that individual differences in WM capacity are reflected in performance on antisaccade, Stroop, and dichotic-listening tasks. WM capacity, or executive attention, is most important under conditions in which interference leads to retrieval of response tendencies that conflict with the current task.

Characterizing Horn Antenna Signals for Breast Cancer Detection <=SEP=> An in-depth analysis of signals from horn antennas used in breast cancer detection research has been carried out. It was found that following the excitation signal, both the double-ridged and the quad-ridged ultra-wideband horn antennas radiate additional unwanted signals that persistently oscillate. Undesirable signal oscillations sources were identified as the horn antenna cavity resonance and inherent antenna LC resonance. These signals interfere with the tumor’s signal response and need to be eliminated for successful detection of the cancerous growth. This paper proposes solutions to remove or minimize these signals without affecting antenna parameters such as bandwidth, gain, ports isolation, and polarization isolation. Modification of the antenna cavity successfully suppressed the unwanted cavity oscillation. Modification of the antenna waveguide reduced inductance and consequently mitigated LC oscillation. The resulting time and frequency domain horn antenna signals demonstrate the effectiveness of the proposed methods. Finally, a breast phantom with a tumor is simulated using signals from the original and a modified horn antenna. The delay and sum method is used to create images. The breast images demonstrate enhanced image quality through the reduction of clutter using the proposed techniques. The signal-to-clutter ratios are 0.448 and 1.6823 dB for the images produced by using the original and modified antennas, respectively.

Visualising the structure of architectural open spaces based on shape analysis <=SEP=> Visualisation and interpretation of architectural spaces are intellectually challenging and multifaceted exercises with fairly wide ranging applications and implications. In the context of urban planning, they are most commonly undertaken to evaluate the usage of the architectural space to ensure efficient navigation and accessibility [1]. These exercises clearly assume a certain influence of the built structures on the human cognition. However, what aspect of architectural space affects the human behaviour still remains an open debate. In this respect, it is closely similar to an exercise to identify the unknown visual variables in information visualization. Since a quantitative analysis of the architectural geometric structure on a large scale will be a daunting computational task, the open space bounded by the built structures is studied instead [2]. The study of architectural open spaces essentially involves the computation of the visibility polygon or isovist (space visible all around a viewpoint, see Figure 1a) from a viewpoint and calculating various shape measures of the visibility polygon. The isovist computation involves drawing rays

Design and Implementation of the LogicBlox System <=SEP=> The LogicBlox system aims to reduce the complexity of software development for modern applications which enhance and automate decision-making and enable their users to evolve their capabilities via a ``self-service'' model. Our perspective in this area is informed by over twenty years of experience building dozens of mission-critical enterprise applications that are in use by hundreds of large enterprises across industries such as retail, telecommunications, banking, and government. We designed and built LogicBlox to be the system we wished we had when developing those applications.
 In this paper, we discuss the design considerations behind the LogicBlox system and give an overview of its implementation, highlighting innovative aspects. These include: LogiQL, a unified and declarative language based on Datalog; the use of purely functional data structures; novel join processing strategies; advanced incremental maintenance and live programming facilities; a novel concurrency control scheme; and built-in support for prescriptive and predictive analytics.

Should I use TensorFlow <=SEP=> Google’s Machine Learning framework TensorFlow was opensourced in November 2015 [1] and has since built a growing community around it. TensorFlow is supposed to be flexible for research purposes while also allowing its models to be deployed productively [7]. This work is aimed towards people with experience in Machine Learning considering whether they should use TensorFlow in their environment. Several aspects of the framework important for such a decision are examined, such as the heterogenity, extensibility and its computation graph. A pure Python implementation of linear classification is compared with an implementation utilizing TensorFlow. I also contrast TensorFlow to other popular frameworks with respect to modeling capability, deployment and performance and give a brief description of the current adaption of the framework.

devices and apps for health care professionals : uses and benefits table 1 uses for Mobile devices and apps by health care professionals <=SEP=> 

Robust Abandoned Object Detection Using Dual Foregrounds <=SEP=> As an alternative to the tracking-based approaches that heavily depend on accurate detection of moving objects, which often fail for crowded scenarios, we present a pixelwise method that employs dual foregrounds to extract temporally static image regions. Depending on the application, these regions indicate objects that do not constitute the original background but were brought into the scene at a subsequent time, such as abandoned and removed items, illegally parked vehicles. We construct separate longand short-term backgrounds that are implemented as pixelwise multivariate Gaussian models. Background parameters are adapted online using a Bayesian update mechanism imposed at different learning rates. By comparing each frame with these models, we estimate two foregrounds. We infer an evidence score at each pixel by applying a set of hypotheses on the foreground responses, and then aggregate the evidence in time to provide temporal consistency. Unlike optical flow-based approaches that smear boundaries, our method can accurately segment out objects even if they are fully occluded. It does not require on-site training to compensate for particular imaging conditions. While having a low-computational load, it readily lends itself to parallelization if further speed improvement is necessary.