Context‐Centric Speech‐Based Human–Computer Interaction

This paper describes research that addresses the problem of dialog management from a strong, context‐centric approach. We further present a quantitative method of measuring the importance of contextual cues when dealing with speech‐based human–computer interactions. It is generally accepted that using context in conjunction with a human input, such as spoken speech, enhances a machine's understanding of the user's intent as a means to pinpoint an adequate reaction. For this work, however, we present a context‐centric approach in which the use of context is the primary basis for understanding and not merely an auxiliary process. We employ an embodied conversation agent that facilitates the seamless engagement of a speech‐based information‐deployment entity by its human end user. This dialog manager emphasizes the use of context to drive its mixed‐initiative discourse model. A typical, modern automatic speech recognizer (ASR) was incorporated to handle the speech‐to‐text translations. As is the nature of these ASR systems, the recognition rate is consistently less than perfect, thus emphasizing the need for contextual assistance. The dialog system was encapsulated into a speech‐based embodied conversation agent platform for prototyping and testing purposes. Experiments were performed to evaluate the robustness of its performance, namely through measures of naturalness and usefulness, with respect to the emphasized use of context. The contribution of this work is to provide empirical evidence of the importance of conversational context in speech‐based human–computer interaction using a field‐tested context‐centric dialog manager.     

Exploring HRM Meta‐Features that Foster Employees' Innovative Work Behaviour in Times of Increasing Work–Life Conflict

Today, employees' innovative work behaviour (IWB) is critical for companies' success. However, employees increasingly experience work–life conflict (WLC) which negatively influences performance at work. Human resource management (HRM) has the potential to foster employees' engagement in innovative activities and to reduce tensions between work and private life simultaneously. Our paper aims to advance understanding under which conditions these relations occur by exploring HRM meta‐features. These are defined as overall characteristics of an HR system helping companies to communicate the content of HR practices in a way that leads to desired interpretations by employees. Using a qualitative, interview study approach, we find that HRM contributes to IWB and diminishes feelings of WLC mainly through the four meta‐features ‘individual orientation’, ‘discretion orientation’, ‘effort orientation’ and ‘expectancy orientation’. We link our findings to extant literature and provide suggestions for managers how these meta‐features can be put into organizational practice.     

Sketch‐to‐Design: Context‐Based Part Assembly

Designing 3D objects from scratch is difficult, especially when the user intent is fuzzy and lacks a clear target form. We facilitate design by providing reference and inspiration from existing model contexts. We rethink model design as navigating through different possible combinations of part assemblies based on a large collection of pre‐segmented 3D models. We propose an interactive sketch‐to‐design system, where the user sketches prominent features of parts to combine. The sketched strokes are analysed individually, and more importantly, in context with the other parts to generate relevant shape suggestions via adesign galleryinterface. As a modelling session progresses and more parts get selected, contextual cues become increasingly dominant, and the model quickly converges to a final form. As a key enabler, we use pre‐learned part‐based contextual information to allow the user to quickly explore different combinations of parts. Our experiments demonstrate the effectiveness of our approach for efficiently designing new variations from existing shape collections.     

Context‐aware scheduling in MapReduce: a compact review

It is a fact that the attention of research community in computer science, business executives, and decision makers is drastically drawn by big data. As the volume of data becomes bigger, it needs performance‐oriented data‐intensive processing frameworks such as MapReduce, which can scale computation on large commodity clusters. Hadoop MapReduce processes data in Hadoop Distributed File System as jobs scheduled according to YARN fair scheduler and capacity scheduler. However, with advancement and dynamic changes in hardware and operating environments, the performance of clusters is greatly affected. Various efforts in literature have been made to address the issues of heterogeneity (i.e., clusters consisting of virtual machines and machines with different hardware), network communication, data locality, better resource utilization, and run‐time scheduling. In this paper, we present a survey to discuss various research efforts made so far to improve Hadoop MapReduce scheduling. We classify scheduling algorithms and techniques proposed in the literature so far based on their addressing areas and present a taxonomy. Furthermore, we also discuss various aspects of open issues and challenges in the scheduling of MapReduce to improve its performance. Copyright © 2015 John Wiley & Sons, Ltd.     

Context‐Based Coding of Adaptive Multiresolution Meshes

Multiresolution meshes provide an efficient and structured representation of geometric objects. To increase the mesh resolution only at vital parts of the object, adaptive refinement is widely used. We propose a lossless compression scheme for these adaptive structures that exploits the parent–child relationships inherent to the mesh hierarchy. We use the rules that correspond to the adaptive refinement scheme and store bits only where some freedom of choice is left, leading to compact codes that are free of redundancy. Moreover, we extend the coder to sequences of meshes with varying refinement. The connectivity compression ratio of our method exceeds that of state‐of‐the‐art coders by a factor of 2–7. For efficient compression of vertex positions we adapt popular wavelet‐based coding schemes to the adaptive triangular and quadrangular cases to demonstrate the compatibility with our method. Akin to state‐of‐the‐art coders, we use a zerotree to encode the resulting coefficients. Using improved context modelling we enhanced the zerotree compression, cutting the overall geometry data rate by 7% below those of the successful Progressive Geometry Compression. More importantly, by exploiting the existing refinement structure we achieve compression factors that are four times greater than those of coders which can handle irregular meshes.     

CBDL: Context‐based distance learning for categorical attributes

Distance learning is an important notion and has played a critical role in success of various machine learning algorithms. Any learning algorithm that requires dissimilarity/similarity measures has to assume some forms of distance functions, either explicitly or implicitly. Hence, in recent years a considerable amount of research has been devoted to distance learning. Despite great achievements in this field, a number of important issues need to be further explored for real world datasets mainly containing categorical attributes. Based on these considerations, the current research presents a Context‐Based Distance Learning approach (CBDL) to advance the state of the art existing researches on distance metric learning for categorical datasets. CBDL is designed and developed based on the idea that distance between two values of a given categorical attribute can be estimated by using information inherently exists within subset of attributes called context. CBDL composes of two main components: context extraction component and distance learning component. Context extraction component is responsible for extracting the relevant subset of feature set for a given attribute, while distance learning component tries to learn distance between each pair of values based on the extracted context. To have a comprehensive analysis, we conduct wide range of experiments in both supervised and unsupervised environments in the presence of noise. Our experimental results reveal that CBDL is the method of choice distance learning approach by offering a comparable or better performance compared to the state of the art existing distance learning schemes according to studied evaluation measures. © 2011 Wiley Periodicals, Inc.     

C‐LOD: Context‐aware Material Level‐of‐Detail applied to Mobile Graphics

Attention‐based Level‐Of–Detail (LOD) managers downgrade the quality of areas that are expected to go unnoticed by an observer to economize on computational resources. The perceptibility of lowered visual fidelity is determined by the accuracy of the attention model that assigns quality levels. Most previous attention based LOD managers do not take into account saliency provoked by context, failing to provide consistently accurate attention predictions. In this work, we extend a recent high level saliency model with four additional components yielding more accurate predictions: an object‐intrinsic factor accounting for canonical form of objects, an object‐context factor for contextual isolation of objects, a feature uniqueness term that accounts for the number of salient features in an image, and a temporal context that generates recurring fixations for objects inconsistent with the context. We conduct a perceptual experiment to acquire the weighting factors to initialize our model. We design C‐LOD, a LOD manager that maintains a constant frame rate on mobile devices by dynamically re‐adjusting material quality on secondary visual features of non‐attended objects. In a proof of concept study we establish that by incorporating C‐LOD, complex effects such as parallax occlusion mapping usually omitted in mobile devices can now be employed, without overloading GPU capability and, at the same time, conserving battery power.     

COPERNICUS: Context‐Preserving Engine for Route Navigation with Interactive User‐modifiable Scaling

In this paper, we present an automated system for generating context‐preserving route maps that depict navigation routes as a path between nodes and edges inside a topographic network. Our application identifies relevant context information to support navigation and orientation, and generates customizable route maps according to design principles that communicate all relevant context information clearly visible on one single page. Interactive scaling allows seamless transition between the original undistorted map and our new map design, and supports user‐specified scaling of regions of interest to create personalized driving directions according to the drivers needs.     

A Work‐In‐Process Literature Review: Incorporating Social Media in Risk and Crisis Communication

This work‐in‐process literature review gives an overview of recent insight in the incorporation of social media in risk and crisis communication. By marrying literature and examples of social media use with best practices in risk and crisis communication, this study demonstrates how communicators can embrace social media tools to better manage a risk or crisis. Best practices in risk and crisis communication are summarized, examples of social media tools used to manage risks and crises are expounded, and recommendations for practitioners are provided to incorporate social media tools in risk and crisis communication.     

Context‐based compression of binary images in parallel

Binary images can be compressed efficiently using context‐based statistical modeling and arithmetic coding. However, this approach is fully sequential and therefore additional computing power from parallel computers cannot be utilized. We attack this problem and show how to implement the context‐based compression in parallel. Our approach is to segment the image into non‐overlapping blocks, which are compressed independently by the processors. We give two alternative solutions about how to construct, distribute and utilize the model in parallel, and study the effect on the compression performance and execution time. We show by experiments that the proposed approach achieves speedup that is proportional to the number of processors. The work efficiency exceeds 50% with any reasonable number of processors. Copyright © 2002 John Wiley & Sons, Ltd.     

SmartCanvas: Context‐inferred Interpretation of Sketches for Preparatory Design Studies

In early or preparatory design stages, an architect or designer sketches out rough ideas, not only about the object or structure being considered, but its relation to its spatial context. This is an iterative process, where the sketches are not only the primary means for testing and refining ideas, but also for communicating among a design team and to clients. Hence, sketching is the preferred media for artists and designers during the early stages of design, albeit with a major drawback: sketches are 2D and effects such as view perturbations or object movement are not supported, thereby inhibiting the design process. We present an interactive system that allows for the creation of a 3D abstraction of a designed space, built primarily by sketching in 2D within the context of an anchoring design or photograph. The system is progressive in the sense that the interpretations are refined as the user continues sketching. As a key technical enabler, we reformulate the sketch interpretation process as a selection optimization from a set of context‐generated canvas planes in order to retrieve a regular arrangement of planes. We demonstrate our system (available at http:/geometry.cs.ucl.ac.uk/projects/2016/smartcanvas/ ) with a wide range of sketches and design studies.     

Professional Personae ‐ How Organizational Identification Shapes Online Identity in the Workplace

As organizations become increasingly mediatized, the roles of professionals are reshaped and negotiated, and the boundaries between professional and private relationships are blurred. In this context, the extent to which one identifies with his or her organization might play an important role. This paper investigates how professionals construct their digital identities on social media sites, focusing in particular on their willingness to overlap private and work profiles to create a univocal online persona. Based on a sample of 679 communication and marketing managers, the paper analyzes the self‐representational choices of professionals and demonstrates how organizational identification influences professionals' tendency to combine their domains under one online persona, and their confidence to use social media in a professional context.     

The Psychology of Novelty‐Seeking,Creativity and Innovation: Neurocognitive Aspects Within a Work‐Psychological Perspective

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The Suitability for the Work‐Related Musculoskeletal Disorders Checklist Assessment in the Semiconductor Industry: A Case Study

Musculoskeletal disorders and symptoms are common among workers in the semiconductor and related industries. Ergonomic‐related checklists are used to identify musculoskeletal disorder risk factors in the workplace. This study explores the suitability of the Occupational Safety and Health Administration (OSHA) musculoskeletal disorders (MSDs) checklist for use in the semiconductor manufacturing industry. Six companies were enrolled in the current study, and 122 subjects, ranging in age from 21 to 54 years old, were evaluated through a questionnaire survey and field observation using the OSHA MSDs checklist. The observers were administered an ergonomics‐based checklist training course with a duration of 36 hours. Experimental results demonstrated that nearly 50% of subjects reported physical discomfort, with shoulder symptoms (38.5%) being the most common complaint. In addition, the aggregated sensitivity, specificity, and positive predictive values for the MSDs checklist in the six companies were 47.3%, 62.4%, and 30.0%, respectively. This study concluded that the OSHA MSDs checklist appeared to contain better estimates for capturing musculoskeletal discomfort and found that the OSHA MSDs checklist provided an easily administered, proactive surveillance instrument to assist in early identification of musculoskeletal discomfort. © 2012 Wiley Periodicals, Inc.     

Bubbling Up the Good Ideas: A Two‐Mode Network Analysis of an Intra‐Organizational Idea Challenge

Organizations have been experimenting with intraorganizational crowdsourcing (IOC), yet the mechanisms of IOC production remain an underresearched topic. Drawing on a 2‐mode ERGM, we examine structural mechanisms and individual‐level factors that shape the network structure of idea generation and selection yielded by an IOC idea challenge in a global IT corporation. Results show a Matthew effect leading to 1) highly centralized employee participation around a few “superactive” employees who engage with many ideas and 2) highly centralized idea popularity with a few ideas attracting most employee attention. We find support for shared affiliations among employee‐idea clusters in the first half of the participation, which is, however, less likely in the second half. We also find support for geographic homophily.     

A Learning‐Theoretic Approach To Model‐Set Identification

A new approach to model‐set identification is proposed based on an agnostic learning theory. The squared prediction error is estimated together with its uncertainty uniformly in some parameter region. Based on this estimation, a model set is constructed so as to include the best model. The proposed approach does not require assumptions on the true dynamics or the noise, neither does it need infinite number of input‐output data in order to justify its result. But it guarantees that the size of the identified model set converges to zero as the number of input‐output data increases. Improvement of the precision is considered on the proposed identification method. Generalization of the approach is discussed and a numerical example is presented.     

Organizational Antecedents of Creative Self‐Efficacy

A number of studies have documented a relationship between creative self‐efficacy and creative performance. The main aim of the present study was to investigate organizational factors that may influence such creative self‐efficacy. The examined variables included employees' task type and task autonomy, the quality of relationship between supervisors and subordinates (leader–member exchange [LMX]), as well as perceived levels of collegial support for creativity. The sample included 240 employees in a manufacturing company. The hypotheses were tested using partial least squares analysis. Results showed significant and positive relationships between the study variables and creative self‐efficacy, supporting the hypotheses. Furthermore, the combinations of task autonomy and high‐quality LMX, as well as task autonomy and collegial support for creativity, were positively associated with creative self‐efficacy. The results of this study may give guidance to leaders and consultants who want to enhance the creativity levels of employees through organizational development efforts.     

Organizational creativity‐innovation process and breakthrough under time constraints: Mid‐point transformation

Time, as a constraining resource, is an important factor in organizational creativity processes. Yet we know little about its role in influencing the creative‐innovative process. This paper uses a case study to integrate three process‐oriented theories concerning the importance of process mid‐points and breakthrough as time resources dissipate. The context we examine is the design process of an exhibition of industrial innovations at the Science Museum in Jerusalem. We argue that actors select a course of action at approximately the temporal mid‐point of a process and that this course of action is itself creative and leads to a breakthrough transformation. Two types of mid‐point creative breakthrough transformation are classified as recombining and pruning. We suggest a theoretical integration and further directions for research.