Dynamic Maps for Exploring and Browsing Shapes

Large datasets of 3D objects require an intuitive way to browse and quickly explore shapes from the collection. We present a dynamic map of shapes where similar shapes are placed next to each other. Similarity between 3D models exists in a high dimensional space which cannot be accurately expressed in a two dimensional map. We solve this discrepancy by providing a local map with pan capabilities and a user interface that resembles an online experience of navigating through geographical maps. As the user navigates through the map, new shapes appear which correspond to the specific navigation tendencies and interests of the user, while maintaining a continuous browsing experience. In contrast with state of the art methods which typically reduce the search space by selecting constraints or employing relevance feedback, our method enables exploration of large sets without constraining the search space, allowing the user greater creativity and serendipity. A user study evaluation showed a strong preference of users for our method over a standard relevance feedback method.     

Learning the Search Range for Evolutionary Optimization in Dynamic Environments

Conventional evolutionary algorithms operate in a fixed search space with limiting parameter range, which is often predefined via a priori knowledge or trial and error in order to ‘guess’ a suitable region comprising the global optimal solution. This requirement is hard, if not impossible, to fulfil in many real-world optimization problems since there is often no clue of where the desired solutions are located in these problems. Thus, this paper proposes an inductive–deductive learning approach for single- and multi-objective evolutionary optimization. The method is capable of directing evolution towards more promising search regions even if these regions are outside the initial predefined space. For problems where the global optimum is included in the initial search space, it is capable of shrinking the search space dynamically for better resolution in genetic representation to facilitate the evolutionary search towards more accurate optimal solutions. Validation results based on benchmark optimization problems show that the proposed inductive–deductive learning is capable of handling different fitness landscapes as well as distributing nondominated solutions uniformly along the final trade-offs in multi-objective optimization, even if there exist many local optima in a high-dimensional search space or the global optimum is outside the predefined search region. Received 15 January 2001 / Revised 8 June 2001 / Accepted in revised form 24 July 2001     

Conjoint analysis to measure the perceived quality in volume rendering

Visualization algorithms can have a large number of parameters, making the space of possible rendering results rather high-dimensional. Only a systematic analysis of the perceived quality can truly reveal the optimal setting for each such parameter. However, an exhaustive search in which all possible parameter permutations are presented to each user within a study group would be infeasible to conduct. Additional complications may result from possible parameter co-dependencies. Here, we will introduce an efficient user study design and analysis strategy that is geared to cope with this problem. The user feedback is fast and easy to obtain and does not require exhaustive parameter testing. To enable such a framework we have modified a preference measuring methodology, conjoint analysis, that originated in psychology and is now also widely used in market research. We demonstrate our framework by a study that measures the perceived quality in volume rendering within the context of large parameter spaces.     

Improving the performance and scalability of Differential Evolution on problems exhibiting parameter interactions

Differential Evolution (DE) is a powerful optimization procedure that self-adapts to the search space, although DE lacks diversity and sufficient bias in the mutation step to make efficient progress on non-separable problems. We present an enhancement to DE that introduces greater diversity while also directing the search to more promising regions. The Combinatorial Sampling Differential Evolution (CSDE) is introduced which can sample vectors in two ways; highly correlated with the search space or around a ‘better’ individual. The CSDE approach can provide a similar number of samples as crossover, without being biased towards the principle coordinate axes of a decision space. This approach to sampling vectors is capable of optimizing problems with extensive parameter interactions. It also demonstrates fast convergence towards the global optimum and is highly scalable in the decision space on a variety of single and multi-objective problems due to the balance between sampling highly directed correlated vectors and non-correlated vectors which contribute to sampling diversity.     

A framework for a feedback process to analyze and personalize a document vector space in a feature extraction model

In this paper, we present a framework for a feedback process to implement a highly accurate document retrieval system. In the system, a document vector space is created dynamically to implement retrieval processing. The retrieval accuracy of the system depends on the vector space. When the vector space is created based on a specific purpose and interest of a user, highly accurate retrieval results can be obtained. In this paper, we present a method for analyzing and personalizing the vector space according to the purposes and interests of users. In order to optimize the document vector space, we defined and implemented functions for the operations of adding, deleting and weighting the terms that were used to create the vector space. By exploiting effectively and dynamically the classified-document information related to the queries, our methods allow users to retrieve relevant documents for their interests and purposes. Even if the search results of the initial retrieval space are not appropriate, by applying the proposed feedback operations, our proposed method effectively improves the search results. We also implemented an experimental search system for semantic document retrieval. Several experimental results including comparisons of our method with the traditional relevance feedback method is presented to clarify how retrieval accuracy was improved by the feedback process and how accurately documents that satisfied the purpose and interests of users were extracted.     

Task-specific information retrieval systems for software engineers

This paper discusses the development of task-specific information retrieval systems for software engineers. We discuss how software engineers interact with information and information retrieval systems and investigate to what extent a domain-specific search and recommendation system can be developed in order to support their work related activities. We have conducted a user study which is based on the “Cognitive Research Framework” to identify the relation between the information objects used during the code development (code snippets and search queries), the tasks users engage in and the associated use of search interfaces. Based on our user studies, a questionnaire and an automated observation of user interactions with the browser and software development environment, we identify that software engineers engage in a finite number of work related tasks and they also develop a finite number of “work practices”/“archetypes of behaviour”. Secondly we identify a group of domain specific behaviours that can successfully be used as a source of strong implicit relevance feedback. Based on our results, we design a snippet recommendation interface, and a code related recommendation interface which are embedded within the standard search engine.     

一种基于用户查询行为模型的案例查询算法

传统的案例查询算法通过被动响应用户的查询请求为用户返回与查询请求相关的案例,忽略了用户查询行为能够对案例查询过程进行指导。提出了一个基于用户查询行为模型的案例查询算法,通过收集用户的查询请求,利用用户查询行为之间的相似度建立用户查询行为的分类模型;分析了用户查询行为的分类算法,重点论述了用户查询行为模型对案例查询过程的指导过程。实验结果表明,该方法能够有效地提高查询结果召回率以及查询成功率。     

Correction to: Can search result summaries enhance the web search efficiency and experiences of the visually impaired users?

There are limited studies that are addressing the challenges of visually impaired (VI) users when viewing search results on a search engine interface by using a screen reader. This study investigates the effect of providing an overview of search results to VI users. We present a novel interactive search engine interface called InteractSE to support VI users during the results exploration stage in order to improve their interactive experience and web search efficiency. An overview of the search results is generated using an unsupervised machine learning approach to present the discovered concepts via a formal concept analysis that is domain-independent. These concepts are arranged in a multi-level tree following a hierarchical order and covering all retrieved documents that share maximal features. The InteractSE interface was evaluated by 16 legally blind users and compared with the Google search engine interface for complex search tasks. The evaluation results were obtained based on both quantitative (as task completion time) and qualitative (as participants’ feedback) measures. These results are promising and indicate that InteractSE enhances the search efficiency and consequently advances user experience. Our observations and analysis of the user interactions and feedback yielded design suggestions to support VI users when exploring and interacting with search results.

     

Supporting Creativity through the Interactive Exploratory Search Paradigm

In creative tasks, there is a need to explore the space of available information in order to come up with diverse views before converging to a solution. In such tasks, typical search engines that follow the direct search paradigm fail to inspire users. It is hypothesized that contrary to typical engines, interactive exploratory search, which aims at revealing latent, alternative directions in the information space enabling user orientation and engagement, is better suited to assist users in their quest for serendipitous discoveries and inspiration. In this study, an interactive exploratory search tool that combines diversification of content and sources with a user interface design that visualizes clues from the social chatter – generated with micro-blogging services such as Twitter – and lets users interactively explore the available information space is presented. A profiling service and recommendation module in charge of delivering personalized social content complements the setting. A pilot and two task-based user studies comparing our system to a query-based baseline indicate that our system significantly improves inspirational discoveries by providing access to more interesting and serendipitous information.     

A hybrid multi-objective optimization algorithm for content based image retrieval

Relevance feedback methods in CBIR (Content Based Image Retrieval) iteratively use relevance information from the user to search the space for other relevant samples. As several regions of interest may be scattered through the space, an effective search algorithm should balance the exploration of the space to find new potential regions of interest and the exploitation of areas around samples which are known relevant. However, many algorithms concentrate the search on areas which are close to the images that the user has marked as relevant, according to a distance function in the (possibly deformed) multidimensional feature space. This maximizes the number of relevant images retrieved at the first iterations, but limits the discovery of new regions of interest and may leave unexplored a large section of the space. In this paper, we propose a novel hybrid approach that uses a scattered search algorithm based on NSGA II (Non-dominated Sorting Genetic Algorithm) only at the first iteration of the relevance feedback process, and then switches to an exploitation algorithm. The combined approach has been tested on three databases and in combination with several other methods. When the hybrid method does not produce better results from the first iteration, it soon catches up and improves both precision and recall.     

Automated design space exploration of multi-cycle transient fault detectable datapath based on multi-objective user constraints for application specific computing

A novel automated design space exploration (DSE) approach of multi-cycle transient fault detectable datapath based on multi-objective user constraints (power and delay) for application specific computing is presented in this paper. To the best of the authors’ knowledge, this is the first work in the literature to solve this problem. The presented approach, driven by bacterial foraging optimization (BFO) algorithm provides easy flexibility to change direction in the design space through tumble/swim actions if a search path is found ineffective. The approach is highly capable of reaching true Pareto optimal curve indicated by the closeness of our non-dominated solutions to the true Pareto front and their uniform distribution over the Pareto curve (implying diversity). The contributions of this paper are as follows: (a) novel exploration approach for generating a high quality fault detectable structure based on user provided requirements of power-delay, which is capable of transient error detection in the datapath; (b) novel fault detectable algorithm for handling single and multi-cycle transient faults.The results of the proposed approach indicated an average improvement in Quality of Results (QoR) of >9% and reduction in hardware usage of >23% compared to recent approaches that are closer in solving a similar objective.     

Conversational Collaborative Recommendation – An Experimental Analysis

Traditionally, collaborative recommender systems have been based on a single-shot model of recommendation where a single set of recommendations is generated based on a user’s (past) stored preferences. However, content-based recommender system research has begun to look towards more conversational models of recommendation, where the user is actively engaged in directing search at recommendation time. Such interactions can range from high-level dialogues with the user, possibly in natural language, to more simple interactions where the user is, for example, asked to indicate a preference for one of k suggested items. Importantly, the feedback attained from these interactions can help to differentiate between the user’s long-term stored preferences, and her current (short-term) requirements, which may be quite different. We argue that such interactions can also be beneficial to collaborative recommendation and provide experimental evidence to support this claim.     

基于用户反馈的搜索引擎排名算法

以Web 2.0中用户行为作为研究对象,通过发掘用户反馈方式,提出用户反馈分值的概念,对用户反馈影响搜索结果排名的具体方法以及相应实现进行研究,提出了一种基于神经网络的网页排序算法。该算法引入BP神经网络模型,根据用户反馈分值选择样本训练神经网络。将传统搜索结果输入到经过训练的神经网络进行计算,根据计算出的结果所表示的网页相关性强弱判断后进行二次排序。该算法利用了神经网络具有的模式识别能力,有效地将用户反馈和搜索引擎结合起来,使得搜索结果更加符合用户的搜索要求。     

网络信息检索中相关反馈技术的Java实现

介绍了相关反馈技术的基本思想,设计了网络信息检索中相关反馈系统的功能与结构,探索如何在现有的公共网络搜索引擎基础上,利用Java语言实现具有相关反馈功能的搜索引擎接口.     

Exploring how children use their hands to think: an embodied interactional analysis

In order to better understand how to design hands-on child-computer interaction, we explore how different styles of interaction facilitate children's thinking while they use their hands to manipulate objects. We present an exploratory study of children solving a spatial puzzle task. We investigate how the affordances of physical, graphical and tangible interfaces may facilitate the development of thinking skills including mental visualisation, problem space exploration and collaboration. We utilise the theory of complementary actions taken from embodied cognition to develop a video coding methodology that allows us to classify behavioural activity and make inferences about thinking skills development. Our findings indicated that the combination of direct hands-on input style with audio-visual feedback facilitated by the tangible user interface enabled a dynamic task completion strategy, which supports the development of mental skills with a slight time cost. The mouse and graphical user interface supported a trial and error approach, which may limit skills development. The physical cardboard puzzle enabled effective task completion but provided less support for social interaction and problem space exploration. We conclude with design recommendations.     

Using haptic feedback as an aid in the design of passive mechanisms

This paper presents a novel investigation of the effectiveness of haptic feedback for designing a class of interconnected multi-body systems such as passive mechanisms. The traditional application of haptic feedback in the design process has been in applications such as parts assembly or mold design. The design of the mechanism discussed in this paper is for applications where the user needs to manipulate the mechanism in order to interact with an environment. The objective of the design is to have the link ratios so that it can allow the user better movement control of the mechanism and thus give a better force amplification when there is a sudden change in the contact reaction force with the application environment. A haptic device is used as a design interface between the designer of such mechanisms and the virtual mechanism model. For this preliminary investigation, we used a four-bar mechanism. In our case study, we choose, as an example, to use the net distance travel of a tool when penetrating inside a model of a deformable surface as the design objective to minimize. The effects on the variation of this distance travelled can then be studied by adjusting some of the key design parameters used in the mechanism. To evaluate our proposed haptic-aided design environment, an informal preliminary user study was conducted, where each subject explored a sampled design space of the mechanism. The results of the user study suggest that the usage of a haptic device in the design of this class of mechanism can expedite the design process.     

一种基于用户标记的搜索结果排序算法

随着计算机网络的快速发展,网络上的信息量也日益纷繁复杂.如何准确、快速地帮助人们从海量网络数据中获取所需信息,这是目前搜索引擎首要解决的问题,为此,各种搜索排序算法应运而生.但是目前,网页信息的表达形式都十分简单,用户描述查询的形式更是十分简单,这就造成了在判断网页内容与用户查询相关性时十分困难.首先对现有的搜索引擎排序算法进行了分类总结,分析它们的优缺点.然后提出了一种基于用户反馈的语义标记的新方法,最后采用多种评估方法与Google搜索结果进行对比分析.实验结果表明,利用该方法所得到的排序结果比Google的排序结果更接近用户需求.     

WebSail: From On-line Learning to Web Search

In this paper we report our research on building WebSail, an intelligent web search engine that is able to perform real-time adaptive learning. WebSail learns from the user's relevance feedback, so that it is able to speed up its search process and to enhance its search performance. We design an efficient adaptive learning algorithm TW2 to search for web documents. WebSail employs TW2 together with an internal index database and a real-time meta-searcher to perform real-time adaptive learning to find desired documents with as little relevance feedback from the user as possible. The architecture and performance of WebSail are also discussed. Received 3 November 2000 / Revised 13 March 2001 / Accepted in revised form 17 April 2001