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Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems behaviour accordingly. Especially in combination with mobile devices, these mechanisms are of high value and are used to increase usability tremendously. In this paper, we present common architecture principles of context-aware systems and derive a layered conceptual design framework to explain the different elements common to most context-aware architectures. Based on these design principles, we introduce various existing context-aware systems focusing on context-aware middleware and frameworks, which ease the development of context-aware applications. We discuss various approaches and analyse important aspects in context-aware computing on the basis of the presented systems.

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

This paper describes systems that examine and react to an individual's changing context. Such systems can promote and mediate people's interactions with devices, computers, and other people, and they can help navigate unfamiliar places. We believe that a limited amount of information covering a person's proximate environment is most important for this form of computing since the interesting part of the world around us is what we can see, hear, and touch. In this paper we define context-aware computing, and describe four catagories of context-aware applications: proximate selection, automatic contextual reconfiguration, contextual information and commands, and contex-triggered actions. Instances of these application types have been prototyped on the PARCTAB, a wireless, palm-sized computer.

I argue that the impact of context on organizational behavior is not sufficiently recognized or appreciated by researchers. I define context as situational opportunities and constraints that affect the occurrence and meaning of organizational behavior as well as functional relationships between variables, and I propose two levels of analysis for thinking about context–one grounded in journalistic practice and the other in classic social psychology. Several means of contextualizing research are considered.

Most theories dealing with ill-defined concepts assume that performance is based on category level information or a mixture of category level and specific item information. A context theory of classificatio n is described in which judgments are assumed to derive exclusively from stored exemplar information. The main idea is that a probe item acts as a retrieval cue to access information associated with stimuli similar to the probe. The predictions of the context theory are contrasted with those of a class of theories (including prototype theory) that assume that the information entering into judgments can be derived from an additive combination of information from component cue dimensions. Across four experiments using both geometric forms and schematic faces as stimuli, the context theory consistently gave a better account of the data. The relation of the context theory to other theories and phenomena associated with ill-defined concepts is discussed in detail. One of the major components of cognitive behavior concerns abstracting rules and forming concepts. Our entire system of naming objects and events, talking about them, and interacting with them presupposes the ability to group experiences into appropriate classes. Young children learn to tell the difference between dogs and cats, between clocks and fans, and between stars and street lights. Since few concepts are formally taught, the evolution of concepts from experience with exemplars must be a fundamental learning phenomenon. The focus of the present article is to explore how such conceptual achievements emerge from individual instances.

We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.

Interviews hold a prominent place among the various research methods in the social and behavioral sciences. This book presents a powerful critique of current views and techniques, and proposes a new approach to interviewing. At the heart of Elliot Mishler's argument is the notion that an interview is a type of discourse, a speech event: it is a joint product, shaped and organized by asking and answering questions. This view may seem self-evident, yet it does not guide most interview research. In the mainstream tradition, the discourse is suppressed. Questions and answers are regarded as analogues to stimuli and responses rather than as forms of speech; questions and the interviewer's behavior are standardized so that all respondents will receive the same "stimulus"; respondents' social and personal contexts of meaning are ignored. While many researchers now recognize that context must be taken into account, the question of how to do so effectively has not been resolved. This important book illustrates how to implement practical alternatives to standard interviewing methods. Drawing on current work in sociolinguistics as well as on his own extensive experience conducting interviews, Mishler shows how interviews can be analyzed and interpreted as narrative accounts. He places interviewing in a sociocultural context and examines the effects on respondents of different types of interviewing practice. The respondents themselves, he believes, should be granted a more extensive role as participants and collaborators in the research process. The book is an elegant work of synthesis-clearly and persuasively written, and supported by concrete examples of both standard interviewing and alternative methods. It will be of interest to both scholars and clinicians in all the various fields for which the interview is an essential tool

Abstract Management literature has suggested that contextual factors may present strong inertial forces within organizations that inhibit implementations that appear technically rational [R.R. Nelson, S.G. Winter, An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, MA, 1982]. This paper examines the effects of three contextual factors, plant size, plant age and unionization status, on the likelihood of implementing 22 manufacturing practices that are key facets of lean production systems. Further, we postulate four “bundles” of inter‐related and internally consistent practices; these are just‐in‐time (JIT), total quality management (TQM), total preventive maintenance (TPM), and human resource management (HRM). We empirically validate our bundles and investigate their effects on operational performance. The study sample uses data from IndustryWeek ’s Census of Manufacturers. The evidence provides strong support for the influence of plant size on lean implementation, whereas the influence of unionization and plant age is less pervasive than conventional wisdom suggests. The results also indicate that lean bundles contribute substantially to the operating performance of plants, and explain about 23% of the variation in operational performance after accounting for the effects of industry and contextual factors.

Research on men's help seeking yields strategies for enhancing men's use of mental and physical health resources. Analysis of the assumptions underlying existing theory and research also provides a context for evaluating the psychology of men and masculinity as an evolving area of social scientific inquiry. The authors identify several theoretical and methodological obstacles that limit understanding of the variable ways that men do or do not seek help from mental and physical health care professionals. A contextual framework is developed by exploring how the socialization and social construction of masculinities transact with social psychological processes common to a variety of potential help-seeking contexts. This approach begins to integrate the psychology of men and masculinity with theory and methodology from other disciplines and suggests innovative ways to facilitate adaptive help seeking.

The relationship between the self and the collective is discussed from the perspective of self-categorization theory. Self-categorization theory makes a basic distinction between personal and social identity as different levels of self-categorization. It shows how the emergent properties of group processes can be explained in terms of a shift in self perception from personal to social identity. It also elucidates how self-categorization varies with the social context. It argues that self-categorizing is inherently variable, fluid, and context dependent, as sedf-categories are social comparative and are always relative to a frame of reference. This notion has major implications for accepted ways of thinking about the self: The variability of self-categorizing provides the perceiver with behavioral and cognitive flexibility and ensures that cognition is always shaped by the social context in which it takes place.

This review collates and examines critically a theoretically convergent but widely dispersed body of research on the influence of external environments on the functioning of families as contexts of human development. Investigations falling within this expanding domain include studies of the interaction of genetics and environment in family processes; transitions and linkages between the family and other major settings influencing development, such as hospitals, day care, peer groups, school, social networks, the world of work (both for parents and children), and neighborhoods and communities; and public policies affecting families and children. A second major focus is on the patterning of environmental events and transitions over the life course as these affect and are affected by intrafamilial processes. Special emphasis is given to critical research gaps in knowledge and priorities for future investigation. The purpose of this article is to document and delineate promising lines of research on external influences that affect the capacity of families to foster the healthy development of their children. The focus differs from that of most studies of the family as a context of human development, because the majority have concentrated on intrafamilial processes of parent-child interaction, a fact that is reflected in Maccoby and Martin's (1983) recent authoritative review of research on family influences on development. By contrast, the focus of the present analysis can be described as once removed. The research question becomes: How are intrafamilial processes affected by extrafamilial conditions? Paradigm Parameters In tracing the evolution of research models in developmental science, Bronfenbrenner and Crouter (1983) distinguished a series of progressively more sophisticated scientific paradigms for investigating the impact of environment on development. These paradigms provide a useful framework for ordering and analyzing studies bearing on the topic of this review. At the most general level, the research models vary simultaneously along two dimensions. As applied to the subject at hand, the first pertains

PART I: The individual and the sociocultural context: Conceiving the relationship of the social world and the individual The sociocultural context of cognitive activity PART II: Processes of guided participation: Providing bridges from known to new Structuring situations and transferring responsibility Cultural universals and variations in guided participation PART III: Cognitive development through interaction with adults and peers: Explanations for cognitive development through social interaction: Vygotsky and Piaget Evidence of learning from guided participation with adults Peer interaction and cognitive development Shared thinking and guided participation.

This study deals with the linguistic study of texts as a way of understanding how language functions in its immensely varied range of social contexts. The authors adopt a functional approach to language, in which the different registers or functional varieties of a language are explained by reference to the different contexts in which they occur. Their analysis reveals how, on the one hand, each text is unique, while on the other, the way a text is organized and the kinds of coherence it displays are closely related to the place and the value that it has in its social and cultural environment.

Introduction Part One - Setting the Stage: Multicultural Education within a Sociopolitical Context 1 Understanding the Sociopolitical Context of Multicultural Education 2 About Terminology 3 Multicultural Education and School Reform Part Two - Developing a Conceptual Framework for Multicultural Education 4 Racism, Discrimination, and Expectations of Students' Achievement Chapter 4 Case Studies: Linda Howard, Rashaud Kates, Vanessa Mattison 5 Structural and Organizational Issues in Schools Chapter 5 Case Studies: Avi Abramson, Fern Sherman 6 Culture, Identity, and Learning Chapter 6 Case Studies: Yahaira Leon, James Karam, Hoang Vinh, Rebecca Florentina 7 Linguistic Diversity in U.S. Classrooms Chapter 7 Case Studies: Manuel Gomes, Alicia Montejo 8 Toward an Understanding of School Achievement Chapter 8 Case Studies: Paul Chavez, Latrell Elton Part Three - Implications of Diversity for Teaching and Learning in a Multicultural Society 9 Learning From Students Chapter 9 Case Studies: Nadia Bara, Savoun Nouch, Christina Kamau 10 Adapting the Curriculum for Multicultural Classrooms 11 Affirming Diversity: Implications for Teachers, Schools, and Families

Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-ofthe-art results of bpc/perplexity to 0.99 on en-wiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch 1 .

We propose a novel context-dependent (CD) model for large-vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output. The deep belief network pre-training algorithm is a robust and often helpful way to initialize deep neural networks generatively that can aid in optimization and reduce generalization error. We illustrate the key components of our model, describe the procedure for applying CD-DNN-HMMs to LVSR, and analyze the effects of various modeling choices on performance. Experiments on a challenging business search dataset demonstrate that CD-DNN-HMMs can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs, with an absolute sentence accuracy improvement of 5.8% and 9.2% (or relative error reduction of 16.0% and 23.2%) over the CD-GMM-HMMs trained using the minimum phone error rate (MPE) and maximum-likelihood (ML) criteria, respectively.

A motivational science perspective on student motivation in learning and teaching contexts is developed that highlights 3 general themes for motivational research. The 3 themes include the importance of a general scientific approach for research on student motivation, the utility of multidisciplinary perspectives, and the importance of use-inspired basic research on motivation. Seven substantive questions are then suggested as important directions for current and future motivational science research efforts. They include (1) What do students want? (2) What motivates students in classrooms? (3) How do students get what they want? (4) Do students know what they want or what motivates them? (5) How does motivation lead to cognition and cognition to motivation? (6) How does motivation change and develop? and (7) What is the role of context and culture? Each of the questions is addressed in terms of current knowledge claims and future directions for research in motivational science.

High-throughput genome sequencing continues to grow the need for rapid, accurate genome annotation and tRNA genes constitute the largest family of essential, ever-present non-coding RNA genes. Newly developed tRNAscan-SE 2.0 has advanced the state-of-the-art methodology in tRNA gene detection and functional prediction, captured by rich new content of the companion Genomic tRNA Database. Previously, web-server tRNA detection was isolated from knowledge of existing tRNAs and their annotation. In this update of the tRNAscan-SE On-line resource, we tie together improvements in tRNA classification with greatly enhanced biological context via dynamically generated links between web server search results, the most relevant genes in the GtRNAdb and interactive, rich genome context provided by UCSC genome browsers. The tRNAscan-SE On-line web server can be accessed at http://trna.ucsc.edu/tRNAscan-SE/.

This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework [19] and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations.