An Evaluation of the Potential of Task Analysis in the Evolution of Interactive Work Systems

Developed forms of task analysis allow designers to focus on both utility and usability issues in the development of interactive work systems. The models they generate represent aspects of the human, computer and domain elements of an interactive work system. Many interactive work systems are embedded in an organisational context. Pressure for changes are present in this context and provide impetus to stakeholders to change work tasks and the supporting tools. Interactive work systems also provide evolutionary pressures of their own, changing the very task they were designed to support. One approach to coping with change has been to evolve interactive work systems. Currently none of these techniques place focus on the performance of tasks as central, and consideration of usability is minimal. However, an evolutionary design approach forces an evolutionary experience upon users, and we cannot be sure whether this approach enhances the user’s experience or degrades their performance. Given the strength of task analysis it is likely that it will be applied within evolutionary contexts. Yet, little work has been undertaken to examine whether its role will, or could be different. We ask how we can move task analysis towards being used in a principled manner in the evolution of interactive work systems. This paper examines a number of features of the approach called task knowledge structures that may be useful in evolving interactive work systems. We look at tasks and their representativeness, roles, goals, objects (their attributes, relationships, typicality and centrality) and actions. We present a developing framework for examining other task analysis approaches for their utility in supporting interactive work systems evolution. Finally, we discuss future work within the area of applying task analysis in the evolution of interactive work systems.     

Machine learning for vessel trajectories using compression,alignments and domain knowledge

In this paper we present a machine learning framework to analyze moving object trajectories from maritime vessels. Within this framework we perform the tasks of clustering, classification and outlier detection with vessel trajectory data. First, we apply a piecewise linear segmentation method to the trajectories to compress them. We adapt an existing technique to better retain stop and move information and show the better performance of our method with experimental results. Second, we use a similarity based approach to perform the clustering, classification and outlier detection tasks using kernel methods. We present experiments that investigate different alignment kernels and the effect of piecewise linear segmentation in the three different tasks. The experimental results show that compression does not negatively impact task performance and greatly reduces computation time for the alignment kernels. Finally, the alignment kernels allow for easy integration of geographical domain knowledge. In experiments we show that this added domain knowledge enhances performance in the clustering and classification tasks.     

Analyzing the capabilities of crowdsourcing services for text summarization

This paper presents a detailed analysis of the use of crowdsourcing services for the Text Summarization task in the context of the tourist domain. In particular, our aim is to retrieve relevant information about a place or an object pictured in an image in order to provide a short summary which will be of great help for a tourist. For tackling this task, we proposed a broad set of experiments using crowdsourcing services that could be useful as a reference for others who want to rely also on crowdsourcing. From the analysis carried out through our experimental setup and the results obtained, we can conclude that although crowdsourcing services were not good to simply gather gold-standard summaries (i.e., from the results obtained for experiments 1, 2 and 4), the encouraging results obtained in the third and sixth experiments motivate us to strongly believe that they can be successfully employed for finding some patterns of behaviour humans have when generating summaries, and for validating and checking other tasks. Furthermore, this analysis serves as a guideline for the types of experiments that might or might not work when using crowdsourcing in the context of text summarization.     

Learning domain ontologies for semantic Web service descriptions

High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains.

Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis is likely to lead to better results. Finally, the evaluation metrics indicate that good results can be achieved using only relatively simple, off the shelf techniques. Indeed, the novelty of our work is not in the used natural language processing methods but rather in the way they are put together in a generic framework specialized for the context of Web services.     

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.     

Qualitative design support for engineering and architecture

Conventional design support software tools cannot effectively manage the complex, heterogeneous information used in engineering and architecture (EA) tasks. Crucially, despite uncertainty being an inherent quality of EA information particularly in the early stages of a design project, current tools solely rely on numerical approaches which do not support such incomplete and vague information. In this paper, we establish a complete framework for developing qualitative support tools that directly address these shortcomings. Our framework is application oriented and addresses the broader issues surrounding the actual use of qualitative methods. It provides design principles and strategies that allow a software engineer to develop custom qualitative software tools according to their specific EA task specifications. Our framework also provides the engineer with practical theory and guidelines for implementing their custom qualitative model and validating their system using context specific test data. We demonstrate the validity of our framework by presenting a case study in architectural lighting in which a prototype qualitative reasoning engine successfully automates qualitative logic about the subjective impressions of a lighting installation.     

Domain analysis for software reuse

A theory of domain knowledge is proposed that consists of ‘grounded domains' that model a set of cooperating objects that achieve a purpose. Grounded domains have spatial presence in the real world and contain agents that act on objects within a context of structures. More complex meta-domains use grounded domains as their subject matter and describe education, management, etc. The third component of the theory, generic tasks, describes problem solving activity such as diagnosis, searching, planning and scheduling. Generic tasks describe the behavioural components in both grounded and meta-domains. The reusable library of generic models is applied to the design of interactive systems by reusing the models as templates, and to reuse design knowledge in the form of associated design rationale. A process for recognising generic models is described with recognition heuristics structured in a walkthrough type of analysis for identifying key abstractions in new applications. The design process is illustrated with an information retrieval case study developed as a decision support system for emergency management, reusing information searching services. The discussion reviews the prospects for reusable patterns in interactive systems design, and similar approaches in software and knowledge engineering.     

Interactive image completion with perspective correction

We present an interactive system for fragment-based image completion which exploits information about the approximate 3D structure in a scene in order to estimate and apply perspective corrections when copying a source fragment to a target position. Even though implicit 3D information is used, the interaction is strictly 2D, which makes the user interface very simple and intuitive. We propose different interaction metaphors in our system for providing 3D information interactively. Our search and matching procedure is done in the Fourier domain, and hence it is very fast and it allows us to use large fragments and multiple source images with high resolution while still obtaining interactive response times. Our image completion technique also takes user-specified structure information into account where we generalize the concept of feature curves to arbitrary sets of feature pixels. We demonstrate our technique on a number of difficult completion tasks.     

Creating a model of the dynamics of socio-technical groups

Individuals participating in technologically mediated forms of organization often have difficulty recognizing when groups emerge, and how the groups they take part in evolve. This paper contributes an analytical framework that improves awareness of these virtual group dynamics through analysis of electronic trace data from tasks and interactions carried out by individuals in systems not explicitly designed for context adaptivity, user modeling or user personalization. We discuss two distinct cases to which we have applied our analytical framework. These two cases provide a useful contrast of two prevalent ways for analyzing social relations starting from electronic trace data: either artifact-mediated or direct person-to-person interactions. Our case study integrates electronic trace data analysis with analysis of other, triangulating data specific to each application. We show how our techniques fit in a general model of group informatics, which can serve to construct group context, and be leveraged by future tool development aimed at augmenting context adaptivity with group context and a social dimension. We describe our methods, data management strategies and technical architecture to support the analysis of individual user task context, increased awareness of group membership, and an integrated view of social, information and coordination contexts.     

Robotic Control with Partial Visual Information

We consider a robotic setting and a class of control tasks that rely on partial visual information. These tasks are difficult in the sense that at every given moment, the available information is insufficient for the control task. This implies that the image Jacobian, which relates the image space and the control space, is no longer of full rank. However, the amount of information collected throughout the control process is still large and thus seems sufficient for carrying out the task. Such situations commonly arise when the object is frequently occluded from one of the cameras in a stereo pair or when only one moving camera is available. We propose a generic control rule for such tasks and characterize the conditions required for the success of the task. The analysis is based on the observation that mathematically the behavior of such systems is related to a class of row-action optimization algorithms which are special cases of POCS (Projection On Convex Sets) algorithms. In the second part of the paper we focus on one particular task from this class: position and orientation control with a single rotating camera. We show that this task can be carried out, in principle, for any camera rotation and suggest efficient control and camera moving strategies. We substantiate our claims by simulations and experiments. Interestingly, it seems that the advisable control law is not consistent with simple intuition.     

SPIRE: a digital library for scientific information

In this paper we describe the architecture and implementation of a digital library framework for scientific data, particularly imagery, with a focus on support for content-based search. Content is specified by the user at one or more of the following abstraction levels: pixel, feature, and semantic. An object-definition mechanism has been developed that supports example-based and constraint-based specification of both simple and complex query targets.This framework incorporates a methodology yielding a computationally efficient implementation of image processing algorithms, thus allowing the interactive, real-time extraction and manipulation of user-specified features and content during the execution of queries. The framework is well-suited for searching scientific databases, including satellite imagery, and medical and seismic data repositories, where the richness of the information does not allow the a priori generation of exhaustive indexes.     

Multitask Learning

Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better. This paper reviews prior work on MTL, presents new evidence that MTL in backprop nets discovers task relatedness without the need of supervisory signals, and presents new results for MTL with k-nearest neighbor and kernel regression. In this paper we demonstrate multitask learning in three domains. We explain how multitask learning works, and show that there are many opportunities for multitask learning in real domains. We present an algorithm and results for multitask learning with case-based methods like k-nearest neighbor and kernel regression, and sketch an algorithm for multitask learning in decision trees. Because multitask learning works, can be applied to many different kinds of domains, and can be used with different learning algorithms, we conjecture there will be many opportunities for its use on real-world problems.     

Multi-task learning to rank for web search

Both the quality and quantity of training data have significant impact on the accuracy of rank functions in web search. With the global search needs, a commercial search engine is required to expand its well tailored service to small countries as well. Due to heterogeneous intrinsic of query intents and search results on different domains (i.e., for different languages and regions), it is difficult for a generic ranking function to satisfy all type of queries. Instead, each domain should use a specific well tailored ranking function. In order to train each ranking function for each domain with a scalable strategy, it is critical to leverage existing training data to enhance the ranking functions of those domains without sufficient training data. In this paper, we present a boosting framework for learning to rank in the multi-task learning context to attack this problem. In particular, we propose to learn non-parametric common structures adaptively from multiple tasks in a stage-wise way. An algorithm is developed to iteratively discover super-features that are effective for all the tasks. The estimation of the regression function for each task is then learned as linear combination of those super-features. We evaluate the accuracy of multi-task learning methods for web search ranking using data from multiple domains from a commercial search engine. Our results demonstrate that multi-task learning methods bring significant relevance improvements over existing baseline method.     

A tree-based context model for object recognition

There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. A context model can rule out some unlikely combinations or locations of objects and guide detectors to produce a semantically coherent interpretation of a scene. However, the performance benefit of context models has been limited because most of the previous methods were tested on data sets with only a few object categories, in which most images contain one or two object categories. In this paper, we introduce a new data set with images that contain many instances of different object categories, and propose an efficient model that captures the contextual information among more than a hundred object categories using a tree structure. Our model incorporates global image features, dependencies between object categories, and outputs of local detectors into one probabilistic framework. We demonstrate that our context model improves object recognition performance and provides a coherent interpretation of a scene, which enables a reliable image querying system by multiple object categories. In addition, our model can be applied to scene understanding tasks that local detectors alone cannot solve, such as detecting objects out of context or querying for the most typical and the least typical scenes in a data set.     

Countering security information overload through alert and packet visualization

This article presents a framework for designing network security visualization systems as well as results from the end-to-end design and implementation of two highly interactive systems. In this article, we provide multiple contributions: we present the results of our survey of security professionals, the design framework, and lessons learned from the design of our systems as well as an evaluation of their effectiveness. Our results indicate that both systems effectively present significantly more information when compared to traditional textual approaches. We believe that the interactive, graphical techniques that we present will have broad applications in other domains seeking to deal with information overload.     

Cross‐layer resource control and scheduling for improving interactivity in Android

Android smartphones are often reported to suffer from sluggish user interactions due to poor interactivity. This is partly because Android and its task scheduler, the completely fair scheduler (CFS), may incur perceptibly long response time to user‐interactive tasks. Particularly, the Android framework cannot systemically favor user‐interactive tasks over other background tasks since it does not distinguish between them. Furthermore, user‐interactive tasks can suffer from high dispatch latency due to the non‐preemptive nature of CFS. To address these problems, this paper presents framework‐assisted task characterization and virtual time‐based CFS. The former is a cross‐layer resource control mechanism between the Android framework and the underlying Linux kernel. It identifies user‐interactive tasks at the framework‐level, by using the notion of a user‐interactive task chain. It then enables the kernel scheduler to selectively promote the priorities of worker tasks appearing in the task chain to reduce the preemption latency. The latter is a cross‐layer refinement of CFS in terms of interactivity. It allows a task to be preempted at every predefined period. It also adjusts the virtual runtimes of the identified user‐interactive tasks to ensure that they are always scheduled prior to the other tasks in the run‐queue when they wake up. As a result, the dispatch latency of a user‐interactive task is reduced to a small value. We have implemented our approach into Android 4.1.2 running with Linux kernel 3.0.31. Experimental results show that the response time of a user interaction is reduced by up to 77.35% while incurring only negligible overhead. Copyright © 2014 John Wiley & Sons, Ltd.     

ClearView: An interactive context preserving hotspot visualization technique

Volume rendered imagery often includes a barrage of 3D information like shape, appearance and topology of complex structures, and it thus quickly overwhelms the user. In particular, when focusing on a specific region a user cannot observe the relationship between various structures unless he has a mental picture of the entire data. In this paper we present ClearView, a GPU-based, interactive framework for texture-based volume ray-casting that allows users which do not have the visualization skills for this mental exercise to quickly obtain a picture of the data in a very intuitive and user-friendly way. ClearView is designed to enable the user to focus on particular areas in the data while preserving context information without visual clutter. ClearView does not require additional feature volumes as it derives any features in the data from image information only. A simple point-and-click interface enables the user to interactively highlight structures in the data. ClearView provides an easy to use interface to complex volumetric data as it only uses transparency in combination with a few specific shaders to convey focus and context information.     

A survey of hierarchical classification across different application domains

In this survey we discuss the task of hierarchical classification. The literature about this field is scattered across very different application domains and for that reason research in one domain is often done unaware of methods developed in other domains. We define what is the task of hierarchical classification and discuss why some related tasks should not be considered hierarchical classification. We also present a new perspective about some existing hierarchical classification approaches, and based on that perspective we propose a new unifying framework to classify the existing approaches. We also present a review of empirical comparisons of the existing methods reported in the literature as well as a conceptual comparison of those methods at a high level of abstraction, discussing their advantages and disadvantages.     

单幅图像去雨数据集和深度学习算法的联合评估与展望

雨天会影响室外图像捕捉的质量,进而引起户外视觉任务性能下降。基于深度学习的单幅图像去雨研究因算法性能优越而引起了大家的关注,并且聚焦点集中在数据集的质量、图像去雨方法、单幅图像去雨后续高层任务的研究和性能评价指标等方面。为了方便研究者快速全面了解该领域,本文从上述4个方面综述了基于深度学习的单幅图像去雨的主流文献。依据数据集的构建方式将雨图数据集分为4类：基于背景雨层简单加和、背景雨层复杂融合、生成对抗网络 (generative adversarial network,GAN)数据驱动合成的数据集,以及半自动化采集的真实数据集。依据任务场景、采取的学习机制以及网络设计对主流算法分类总结。综述了面向单任务和联合任务的去雨算法,单任务即雨滴、雨纹、雨雾和暴雨的去除;联合任务即雨滴和雨纹、所有噪声去除。综述了学习机制和网络构建方式(比如:卷积神经网络 (convolutional neural network,CNN)结构多分支组合,GAN的生成结构,循环和多阶段结构,多尺度结构,编解码结构,基于注意力,基于Transformer)以及数据模型双驱动的构建方式。综述了单幅图像去雨后续高层任务的研究文献和图像去雨算法性能的评价指标。通过合成数据集和真实数据集上的综合实验对比,证实了领域知识隐式引导网络构建可以有效提升算法性能,领域知识显式引导正则化网络的学习有潜力进一步提升算法的泛化性。最后,指出单幅图像去雨工作目前面临的挑战和未来的研究方向。