Context‐based email classification model

Context‐based email classification requires understanding of semantic and structural attributes of email. Most of the research has focused on generating semantic properties through structural components of email. By viewing emails as events (as a major subset of class of email), a rich contextual test‐bed representation for understanding of the semantic attributes of emails has been devised. The event‐ based emails have traditionally been studied based on simple structural properties. In this paper, we present a novel approach by first representing such class of emails as graphs, followed by heuristically applying graph mining and matching algorithm to pick templates representing contextual and semantic attributes that help classify emails. The classification templates used three key event classes: social, personal and professional. Results show that our graph mining and matching supported template‐based approach performs consistently well over event email data set with high accuracy.     

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.     

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.     

Dynamic Insets for Context‐Aware Graph Navigation

Maintaining both overview and detail while navigating in graphs, such as road networks, airline route maps, or social networks, is difficult, especially when targets of interest are located far apart. We present a navigation technique called Dynamic Insets that provides context awareness for graph navigation. Dynamic insets utilize the topological structure of the network to draw a visual inset for off‐screen nodes that shows a portion of the surrounding area for links leaving the edge of the screen. We implement dynamic insets for general graph navigation as well as geographical maps. We also present results from a set of user studies that show that our technique is more efficient than most of the existing techniques for graph navigation in different networks.     

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.     

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.     

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.     

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.     

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.     

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.     

Quasi‐Time‐Dependent Controller for Discrete‐Time Switched Linear Systems With Mode‐Dependent Average Dwell‐Time

This paper is concerned with the stability and stabilization problem of a class of discrete‐time switched systems with mode‐dependent average dwell time (MDADT). A novel Lyapunov function, which is both mode‐dependent (MD) and quasi‐time‐dependent (QTD), is established. The new established Lyapunov function is allowed to increase at some certain time instants. A QTD controller is designed such that the system is globally uniformly asymptotically stable (GUAS) and has a guaranteed performance index. The new QTD robust controller designed in this paper is less conservative than the mode independent one which is frequently considered in literatures. Finally, a numerical example and a practical example are provided to illustrate the effectiveness of the developed results.      

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.     

A Survey of Real‐Time Crowd Rendering

In this survey we review, classify and compare existing approaches for real‐time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level‐of‐detail (LoD) rendering of animated characters, including polygon‐based, point‐based, and image‐based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo‐instancing, palette skinning, and dynamic key‐pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.     

The PAG Crowd: A Graph Based Approach for Efficient Data‐Driven Crowd Simulation

We present a data‐driven method for the real‐time synthesis of believable steering behaviours for virtual crowds. The proposed method interlinks the input examples into a structure we call the perception‐action graph (PAG) which can be used at run‐time to efficiently synthesize believable virtual crowds. A virtual character's state is encoded using a temporal representation, the Temporal Perception Pattern (TPP). The graph nodes store groups of similar TPPs whereas edges connecting the nodes store actions (trajectories) that were partially responsible for the transformation between the TPPs. The proposed method is being tested on various scenarios using different input data and compared against a nearest neighbours approach which is commonly employed in other data‐driven crowd simulation systems. The results show up to an order of magnitude speed‐up with similar or better simulation quality.     

频域基于灰度共生矩阵的人群密度估计

人群密度估计对于大型公共设施的设计和人群的安全十分重要.本文提出了一种基于二维快速傅立叶变换和灰度共生矩阵的人群密度特征提取方法.主要思想是依据不同密度的人群图像在其频谱图上的不同,将频谱图视为纹理图像,利用纹理分析的方法提取能够表现这些不同的纹理特征.进而利用Adaboost实现人群密度级别的分类.实验结果表明本文提出的方法在具有不逊于其他算法的准确率的情况下,计算速度明显提高.     

GM‐PHD Filter with State‐Dependent Clutter

In traditional filtering methods, clutter is often assumed to obey a uniform distribution over the entire monitoring area. For many sensors, however, clutter may concentrate in target‐containing regions. Under this condition, the performance of the traditional multi‐target tracking filter can be degraded. In an effort to solve this problem, this paper proposes an improved algorithm based on a Gaussian Mixture probability hypothesis density (GM‐PHD) filter to deal with state‐dependent clutter. First, the relationship between state and clutter is modeled using the uniform distribution centered on the target state. Then, the clutter intensity is calculated according to the distribution of clutter in the whole monitoring area and is used to update the filter. The simulation results show that the improved filter can track targets’ trajectories more effectively in an environment of state‐dependent clutter than the standard GM‐PHD filter.     

Organizational Identification: A Context‐Specific Mitigating Resource of Work–Family Conflict

The tension between work and non‐work life remains a critical issue in contemporary careers. This study explores the role of organizational identification (OI) in reducing work–family conflict (WFC) within demanding and high‐stakes jobs in dynamic, uncertain and potentially dangerous contexts (e.g., firefighting). Survey data from 341 firefighters suggest that, congruent with conservation of resources theory and scarcity theory, OI may serve as a resource that mitigates WFC in these contexts. Additionally, the data suggest that the negative relationship between OI and WFC is stronger when trauma is low. For practice, this study provides important implications for employees in similar contexts concerning potential ways to mitigate WFC as well as recommendations concerning exposure to trauma.     

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.     

A Data‐Driven Framework for Visual Crowd Analysis

We present a novel approach for analyzing the quality of multi‐agent crowd simulation algorithms. Our approach is data‐driven, taking as input a set of user‐defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state‐of‐the‐art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per‐agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real‐time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely‐used methods, including a simulation from a commercial game.     

Packet‐Loss‐Dependent Stabilization of Model‐Based Networked Control Systems

In this paper, the stabilization problem and controller design of model‐based networked control systems (MB‐NCSs) with both arbitrary and Markovian packet dropouts are discussed via the switched system approach. Different from the common way of using the last successfully transmitted information, the approximate state produced by the explicit plant model is applied to deal with the packet loss problem in our method. Based on the Lyapunov functional methodology and inequality techniques, some sufficient stabilization conditions are derived and stabilizing state feedback controllers are constructed. Moreover, by using the cone complementary linearation (CCL) method, a non‐linear minimization problem subject to some linear matrix inequalities (LMIs) is provided here to help find a sub‐optimal solution. Numerical examples and accompanying simulations illustrate the effectiveness and validity of our techniques, and also evidence of improvements over the existing literature.