2,428 research outputs found

    Improving the translation environment for professional translators

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    When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

    Indexing Metric Spaces for Exact Similarity Search

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    With the continued digitalization of societal processes, we are seeing an explosion in available data. This is referred to as big data. In a research setting, three aspects of the data are often viewed as the main sources of challenges when attempting to enable value creation from big data: volume, velocity and variety. Many studies address volume or velocity, while much fewer studies concern the variety. Metric space is ideal for addressing variety because it can accommodate any type of data as long as its associated distance notion satisfies the triangle inequality. To accelerate search in metric space, a collection of indexing techniques for metric data have been proposed. However, existing surveys each offers only a narrow coverage, and no comprehensive empirical study of those techniques exists. We offer a survey of all the existing metric indexes that can support exact similarity search, by i) summarizing all the existing partitioning, pruning and validation techniques used for metric indexes, ii) providing the time and storage complexity analysis on the index construction, and iii) report on a comprehensive empirical comparison of their similarity query processing performance. Here, empirical comparisons are used to evaluate the index performance during search as it is hard to see the complexity analysis differences on the similarity query processing and the query performance depends on the pruning and validation abilities related to the data distribution. This article aims at revealing different strengths and weaknesses of different indexing techniques in order to offer guidance on selecting an appropriate indexing technique for a given setting, and directing the future research for metric indexes

    Using evidence to improve Psychological Therapies Services

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    Psychological therapy services offer help to clients with many different sorts of mental health problems using a variety of therapies provided by a range of different professional groups and are supported by a large amount of research evidence. However, applying evidence-based practice in routine clinical settings presents particular challenges. This paper outlines some of the difficulties applying research findings to routine settings and argues for a more inclusive approach to linking evidence with practice. It describes a systematic approach to service evaluation and practice based evidence within a large psychological therapies service. This approach is integrated into the service delivery. It enables clinicians to become engaged in the process of reflecting on evidence in a non-threatening way and allows innovative ways of enhancing reflective practice by linking evidence with practice in routine settings

    Multi-match Packet Classification on Memory-Logic Trade-off FPGA-based Architecture

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    Packet processing is becoming much more challenging as networks evolve towards a multi-service platform. In particular, packet classification demands smaller processing times as data rates increase. To successfully meet this requirement, hardware-based classification architectures have become an area of extensive research. Even if Field Programmable Logic Arrays (FPGAs) have emerged as an interesting technology for implementing these architectures, existing proposals either exploit maximal concurrency with unbounded resource consumption, or base the architecture on distributed RAM memory-based schemes which strongly undervalues FPGA capabilities. Moreover, most of these proposals target best-match classification and are not suited for high-speed updates of classification rulesets. In this paper, we propose a new approach which exploits rich logic resources available in modern FPGAs while reducing memory consumption. Our architecture is conceived for multi-match classification, and its mapping methodology is naturally suited for high-speed, simple updating of the classification ruleset. Analytical evaluation and implementation results of our architecture are promising, demonstrating that it is suitable for line speed processing with balanced resource consumption. With additional optimizations, our proposal has the potential to be integrated into network processing architectures demanding all aforementioned features.http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6602301Fil: Zerbini, Carlos A. Universidad TecnolĂłgica Nacional. Departamento de IngenierĂ­a ElectrĂłnica; Argentina.Fil: Finochietto, Jorge M. Universidad Nacional de CĂłrdoba. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Laboratorio de Comunicaciones Digitales; Argentina.IngenierĂ­a de Sistemas y Comunicacione

    The False Promise of Presidential Indexation

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    The Trump Administration faces mounting pressure from conservative thinkers and activists—including calls from its own National Economic Council director—to promulgate a U.S. Treasury Department regulation that indexes capital gains for inflation. Proponents of such a move—which is sometimes called “presidential indexation”—make three principal arguments in favor of the proposal: (1) that inflation indexing would be an economic boon; (2) that the President and his Treasury Department have legal authority to implement inflation indexing without further congressional authorization; and (3) that in any event, it is unlikely that anyone would have standing to challenge such an action in court. This Article evaluates the proponents’ three arguments and concludes that all are faulty. First, whatever the merits of comprehensive legislation that adjusts the taxation of capital gains and various other elements of the Internal Revenue Code for inflation, rifle-shot regulatory action that targets only the capital gains tax would be costly and regressive, would open a number of large loopholes that allow for rampant tax arbitrage, and would be unlikely to significantly enhance growth. Second, the legal authority for presidential indexation simply does not exist. The Justice Department under the first President Bush reached the conclusion in 1992 that the Executive Branch cannot implement inflation indexing unilaterally, and doctrinal developments in the last quarter century have—if anything—strengthened that conclusion. Third, a number of potential plaintiffs—including a Democrat-controlled House of Representatives, certain states, brokers subject to statutory basis reporting requirements, and investment funds whose tax liability could rise as a result of the regulation—would likely have standing to challenge presidential indexation in federal court. In sum, the promise of presidential indexation turns out too hollow, and calls for unilateral action should be spurned

    LifeLogging: personal big data

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    We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self

    Congenial Web Search : A Conceptual Framework for Personalized, Collaborative, and Social Peer-to-Peer Retrieval

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    Traditional information retrieval methods fail to address the fact that information consumption and production are social activities. Most Web search engines do not consider the social-cultural environment of users' information needs and the collaboration between users. This dissertation addresses a new search paradigm for Web information retrieval denoted as Congenial Web Search. It emphasizes personalization, collaboration, and socialization methods in order to improve effectiveness. The client-server architecture of Web search engines only allows the consumption of information. A peer-to-peer system architecture has been developed in this research to improve information seeking. Each user is involved in an interactive process to produce meta-information. Based on a personalization strategy on each peer, the user is supported to give explicit feedback for relevant documents. His information need is expressed by a query that is stored in a Peer Search Memory. On one hand, query-document associations are incorporated in a personalized ranking method for repeated information needs. The performance is shown in a known-item retrieval setting. On the other hand, explicit feedback of each user is useful to discover collaborative information needs. A new method for a controlled grouping of query terms, links, and users was developed to maintain Virtual Knowledge Communities. The quality of this grouping represents the effectiveness of grouped terms and links. Both strategies, personalization and collaboration, tackle the problem of a missing socialization among searchers. Finally, a concept for integrated information seeking was developed. This incorporates an integrated representation to improve effectiveness of information retrieval and information filtering. An integrated information retrieval process explores a virtual search network of Peer Search Memories in order to accomplish a reputation-based ranking. In addition, the community structure is considered by an integrated information filtering process. Both concepts have been evaluated and shown to have a better performance than traditional techniques. The methods presented in this dissertation offer the potential towards more transparency, and control of Web search

    Getting Past the Language Gap: Innovations in Machine Translation

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    In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

    Automatic Food Intake Assessment Using Camera Phones

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    Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user\u27s memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors
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