Showing papers on "Context (language use) published in 2013"

A public domain dataset for human activity recognition using smartphones

Abstract: Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository. Results, obtained on the dataset by exploiting a multiclass Support Vector Machine (SVM), are also acknowledged.

Language control in bilinguals: The adaptive control hypothesis

Abstract: Speech comprehension and production are governed by control processes. We explore their nature and dynamics in bilingual speakers with a focus on speech production. Prior research indicates that individuals increase cognitive control in order to achieve a desired goal. In the adaptive control hypothesis we propose a stronger hypothesis: Language control processes themselves adapt to the recurrent demands placed on them by the interactional context. Adapting a control process means changing a parameter or parameters about the way it works (its neural capacity or efficiency) or the way it works in concert, or in cascade, with other control processes (e.g., its connectedness). We distinguish eight control processes (goal maintenance, conflict monitoring, interference suppression, salient cue detection, selective response inhibition, task disengagement, task engagement, opportunistic planning). We consider the demands on these processes imposed by three interactional contexts (single language, dual language, and dense code-switching). We predict adaptive changes in the neural regions and circuits associated with specific control processes. A dual-language context, for example, is predicted to lead to the adaptation of a circuit mediating a cascade of control processes that circumvents a control dilemma. Effective test of the adaptive control hypothesis requires behavioural and neuroimaging work that assesses language control in a range of tasks within the same individual.

Challenging 'Resistance to Change'

Abstract: This article examines the origins of one of the most widely accepted mental models that drives organizational behavior: The idea that there is resistance to change and that managers must overcome it. This mental model, held by employees at all levels, interferes with successful change implementation. The authors trace the emergence of the term resistance to change and show how it became received truth. Kurt Lewin introduced the term as a systems concept, as a force affecting managers and employees equally. Because the terminology, but not the context, was carried forward, later uses increasingly cast the problem as a psychological concept, personalizing the issue as employees versus managers. Acceptance of this model confuses an understanding of change dynamics. Letting go of the term — and the model it has come to embody — will make way for more useful models of change dynamics. The authors conclude with a discussion of alternatives to resistance to change.

Creating a False Memory in the Hippocampus

Abstract: Memories can be unreliable. We created a false memory in mice by optogenetically manipulating memory engram-bearing cells in the hippocampus. Dentate gyrus (DG) or CA1 neurons activated by exposure to a particular context were labeled with channelrhodopsin-2. These neurons were later optically reactivated during fear conditioning in a different context. The DG experimental group showed increased freezing in the original context, in which a foot shock was never delivered. The recall of this false memory was context-specific, activated similar downstream regions engaged during natural fear memory recall, and was also capable of driving an active fear response. Our data demonstrate that it is possible to generate an internally represented and behaviorally expressed fear memory via artificial means.

Redox-active ligands in catalysis

Abstract: Odd-electron, redox-active ligands are discussed in the context of catalysis. We focus on ligand-based, non-singlet state intermediates and their participation in catalytic processes and related stoichiometric transformations.

The Future of Emotion Regulation Research: Capturing Context.

Abstract: Emotion regulation has been conceptualized as a process by which individuals modify their emotional experiences, expressions, and physiology and the situations eliciting such emotions in order to produce appropriate responses to the ever-changing demands posed by the environment. Thus, context plays a central role in emotion regulation. This is particularly relevant to the work on emotion regulation in psychopathology, because psychological disorders are characterized by rigid responses to the environment. However, this recognition of the importance of context has appeared primarily in the theoretical realm, with the empirical work lagging behind. In this review, the author proposes an approach to systematically evaluate the contextual factors shaping emotion regulation. Such an approach consists of specifying the components that characterize emotion regulation and then systematically evaluating deviations within each of these components and their underlying dimensions. Initial guidelines for how to combi...

The local turn in peace building: A critical agenda for peace

Abstract: This article unpacks the renaissance of interest in ‘the local’ in peace building. It pays increased attention to local dimensions of peace in a wider context of increased assertiveness by local actors as well as a loss of confidence by major actors behind international peace-support actors. The article sees the ‘local turn’ in peace building as part of a wider critical turn in the study of peace and conflict, and focuses on the epistemological consequences of the recourse to localism in the conceptualisation and execution of peace building. The local turn has implications for the nature and location of power in peace building. This article is largely conceptual and theoretical in nature but it is worth noting that the local turn is based on reactions to real-world events.

Role of affect in decision making.

Abstract: Emotion plays a major role in influencing our everyday cognitive and behavioral functions, including decision making. We introduce different ways in which emotions are characterized in terms of the way they influence or elicited by decision making. This chapter discusses different theories that have been proposed to explain the role of emotions in judgment and decision making. We also discuss incidental emotional influences, both long-duration influences like mood and short-duration influences by emotional context present prior to or during decision making. We present and discuss results from a study with emotional pictures presented prior to decision making and how that influences both decision processes and postdecision experience as a function of uncertainty. We conclude with a summary of the work on emotions and decision making in the context of decision-making theories and our work on incidental emotions.

Document-level sentiment classification: An empirical comparison between SVM and ANN

Abstract: Document-level sentiment classification aims to automate the task of classifying a textual review, which is given on a single topic, as expressing a positive or negative sentiment. In general, supervised methods consist of two stages: (i) extraction/selection of informative features and (ii) classification of reviews by using learning models like Support Vector Machines (SVM) and Nai@?ve Bayes (NB). SVM have been extensively and successfully used as a sentiment learning approach while Artificial Neural Networks (ANN) have rarely been considered in comparative studies in the sentiment analysis literature. This paper presents an empirical comparison between SVM and ANN regarding document-level sentiment analysis. We discuss requirements, resulting models and contexts in which both approaches achieve better levels of classification accuracy. We adopt a standard evaluation context with popular supervised methods for feature selection and weighting in a traditional bag-of-words model. Except for some unbalanced data contexts, our experiments indicated that ANN produce superior or at least comparable results to SVM's. Specially on the benchmark dataset of Movies reviews, ANN outperformed SVM by a statistically significant difference, even on the context of unbalanced data. Our results have also confirmed some potential limitations of both models, which have been rarely discussed in the sentiment classification literature, like the computational cost of SVM at the running time and ANN at the training time.

Pleiotropic actions of insulin resistance and inflammation in metabolic homeostasis

Abstract: Metabolism and immunity are inextricably linked both to each other and to organism-wide function, allowing mammals to adapt to changes in their internal and external environments. In the modern context of obesogenic diets and lifestyles, however, these adaptive responses can have deleterious consequences. In this Review, we discuss the pleiotropic actions of inflammation and insulin resistance in metabolic homeostasis and disease. An appreciation of the adaptive context in which these responses arose is useful for understanding their pathogenic actions in disease.

Resilience Processes in Development: Four Waves of Research on Positive Adaptation in the Context of Adversity

Abstract: How do children and adolescents “make it” when their development is threatened by poverty, neglect, maltreatment, war, violence, or exposure to oppression, racism, and discrimination? What protects them when their parents are disabled by substance abuse, mental illness, or serious physical illness? How do we explain the phenomenon of resilience—children succeeding in spite of serious challenges to their development—and put this knowledge to work for the benefit of children and society? The scientific study of resilience emerged around 1970 when a group of pioneering researchers began to notice the phenomenon of positive adaptation among subgroups of children who were considered “at risk” for developing later psychopathology (Masten, 2001, 2012).

System and method for a context layer switch

Abstract: In accordance with an embodiment, a network device has an input port for receiving input packets, and an output port for sending output packets, where the input packets and output packets have context layer information. The network device also includes a processor configured to process the input packets and output packets using a network protocol having a context layer.

Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions

Abstract: The context in which language is used provides a strong signal for learning to recover its meaning. In this paper, we show it can be used within a grounded CCG semantic parsing approach that learns a joint model of meaning and context for interpreting and executing natural language instructions, using various types of weak supervision. The joint nature provides crucial benefits by allowing situated cues, such as the set of visible objects, to directly influence learning. It also enables algorithms that learn while executing instructions, for example by trying to replicate human actions. Experiments on a benchmark navigational dataset demonstrate strong performance under differing forms of supervision, including correctly executing 60% more instruction sets relative to the previous state of the art.

Software fault prediction metrics

Abstract: ContextSoftware metrics may be used in fault prediction models to improve software quality by predicting fault location. ObjectiveThis paper aims to identify software metrics and to assess their applicability in software fault prediction. We investigated the influence of context on metrics' selection and performance. MethodThis systematic literature review includes 106 papers published between 1991 and 2011. The selected papers are classified according to metrics and context properties. ResultsObject-oriented metrics (49%) were used nearly twice as often compared to traditional source code metrics (27%) or process metrics (24%). Chidamber and Kemerer's (CK) object-oriented metrics were most frequently used. According to the selected studies there are significant differences between the metrics used in fault prediction performance. Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics. Process metrics seem to be better at predicting post-release faults compared to any static code metrics. ConclusionMore studies should be performed on large industrial software systems to find metrics more relevant for the industry and to answer the question as to which metrics should be used in a given context.

PANDA: Pose Aligned Networks for Deep Attribute Modeling

Abstract: We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion. Convolutional Neural Nets (CNN) have been shown to perform very well on large scale object recognition problems. In the context of attribute classification, however, the signal is often subtle and it may cover only a small part of the image, while the image is dominated by the effects of pose and viewpoint. Discounting for pose variation would require training on very large labeled datasets which are not presently available. Part-based models, such as poselets and DPM have been shown to perform well for this problem but they are limited by shallow low-level features. We propose a new method which combines part-based models and deep learning by training pose-normalized CNNs. We show substantial improvement vs. state-of-the-art methods on challenging attribute classification tasks in unconstrained settings. Experiments confirm that our method outperforms both the best part-based methods on this problem and conventional CNNs trained on the full bounding box of the person.

Knowledge, attitude, and concussion-reporting behaviors among high school athletes: A preliminary study

Abstract: Context: Many athletes continue to participate in practices and games while experiencing concussion-related symptoms, potentially predisposing them to subsequent and more complicated brain injuries...

Gender and entrepreneurship

Abstract: Purpose – Women‐owned businesses are frequently described as under‐performing in that the majority remain small and marginal. The authors dispute this description; within this paper, it is argued that such performance profiles reflect the constrained performance of most small firms. The assertion that women owned firms under‐perform reflects a gendered bias within the entrepreneurial discourse where femininity and deficit are deemed coterminous. In addition, women‐owned firms are expected to under‐perform given expectations of female weakness in the context of male normativity and superiority. Accordingly, the aim of this paper is to critically evaluate the association between gender and business performance suggesting that this critique has implications for the broader development of our understanding of entrepreneuring behaviours.Design/methodology/approach – This is a conceptual research note which explores the notion of performance and under‐performance in the context of gender.Findings – It is argued...

Global multiregional input-output framework: An introduction and outlook introduction

Abstract: This review is the introduction to a special issue of Economic Systems Research on the topic of global multiregional input–output (GMRIO) tables, models, and analysis. It provides a short historical context of GMRIO development and its applications (many of which deal with environmental extensions) and presents the rationale for the major database projects presented in this special issue. Then the six papers are briefly introduced. This is followed by a concluding comparison of the characteristics of the main GMRIO databases developed thus far and an outlook of potential further developments.

Entrepreneurship in Innovation Ecosystems: Entrepreneurs' Self‐Regulatory Processes and Their Implications for New Venture Success

Abstract: Innovation ecosystems have emerged as an important context for entrepreneurship. Ecosystem entrepreneurs, however, face a unique set of challenges associated with the need to balance the goals and ...

The Higher Infinite: Large Cardinals in Set Theory from Their Beginnings

Abstract: The theory of large cardinals is currently a broad mainstream of modern set theory, the main area of investigation for the analysis of the relative consistency of mathematical propositions and possible new axioms for mathematics. The first of a projected multi-volume series, this book provides a comprehensive account of the theory of large cardinals from its beginnings and some of the direct outgrowths leading to the frontiers of contemporary research. A "genetic" approach is taken, presenting the subject in the context of its historical development. With hindsight the consequential avenues are pursued and the most elegant or accessible expositions given. With open questions and speculations provided throughout the reader should not only come to appreciate the scope and coherence of the overall enterprise but also become prepared to pursue research in several specific areas by studying the relevant sections.

A single parameter representation of hygroscopic growth and cloud condensation nucleus activity – Part 3: Including surfactant partitioning

Abstract: Atmospheric particles can serve as cloud condensation nuclei in the atmosphere The presence of surface active compounds in the particle may affect the critical supersaturation that is required to activate a particle Modelling surfactants in the context of Kohler theory, however, is difficult because surfactant enrichment at the surface implies that a stable radial concentration gradient must exist in the droplet In this study, we introduce a hybrid model that accounts for partitioning between the bulk and surface phases in the context of single parameter representations of cloud condensation nucleus activity The presented formulation incorporates analytical approximations of surfactant partitioning to yield a set of equations that maintain the conceptual and mathematical simplicity of the single parameter framework The resulting set of equations allows users of the single parameter model to account for surfactant partitioning by applying minor modifications to already existing code

A Person-by-Situation Approach to Emotion Regulation Cognitive Reappraisal Can Either Help or Hurt, Depending on the Context

Abstract: Emotion regulation is central to psychological health. For instance, cognitive reappraisal (reframing an emotional situation) is generally an adaptive emotion-regulation strategy (i.e., it is associated with increased psychological health). However, a person-by-situation approach suggests that the adaptiveness of different emotion-regulation strategies depends on the context in which they are used. Specifically, reappraisal may be adaptive when stressors are uncontrollable (when the person can regulate only the self) but maladaptive when stressors can be controlled (when the person can change the situation). To test this prediction, we measured cognitive-reappraisal ability, the severity of recent life stressors, stressor controllability, and level of depression in 170 participants. For participants with uncontrollable stress, higher cognitive-reappraisal ability was associated with lower levels of depression. In contrast, for participants with controllable stress, higher cognitive-reappraisal ability was associated with greater levels of depression. These findings support a theoretical model in which particular emotion-regulation strategies are not adaptive or maladaptive per se; rather, their adaptiveness depends on the context.

The Complexity of Context: A Service Ecosystems Approach for International Marketing:

Abstract: To strengthen the theoretical foundations of international marketing (IM), the authors propose a framework for conceptualizing the complexity of the context that frames international and global exchange systems. In particular, they apply a service ecosystems approach, which is grounded in service-dominant logic and its foundational premise that service is the basis of all exchange. The proposed framework provides insight into the nature of context, a distinguishing feature of IM. The authors argue that the embeddedness of social networks and the multiplicity of institutions within a service ecosystem influence the complexity of context. They articulate the way the (co)creation of value influences and is influenced by the enactment of practices and the integration of resources through various levels (micro, meso, and macro) of interaction and institutions. They introduce the concept of “value in cultural context” to emphasize the influence of the symbolic and social components of context. The arti...

What to do about bad language on the internet

Abstract: The rise of social media has brought computational linguistics in ever-closer contact with bad language: text that defies our expectations about vocabulary, spelling, and syntax. This paper surveys the landscape of bad language, and offers a critical review of the NLP community’s response, which has largely followed two paths: normalization and domain adaptation. Each approach is evaluated in the context of theoretical and empirical work on computer-mediated communication. In addition, the paper presents a quantitative analysis of the lexical diversity of social media text, and its relationship to other corpora.