Rational Value of Information Estimation for Measurement Selection

Computing value of information (VOI) is a crucial task in various aspects of decision-making under uncertainty, such as in meta-reasoning for search; in selecting measurements to make, prior to choosing a course of action; and in managing the exploration vs. exploitation tradeoff. Since such applications typically require numerous VOI computations during a single run, it is essential that VOI be computed efficiently. We examine the issue of anytime estimation of VOI, as frequently it suffices to get a crude estimate of the VOI, thus saving considerable computational resources. As a case study, we examine VOI estimation in the measurement selection problem. Empirical evaluation of the proposed scheme in this domain shows that computational resources can indeed be significantly reduced, at little cost in expected rewards achieved in the overall decision problem.Comment: 7 pages, 2 figures, presented at URPDM2010; plots fixe

Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies

Intelligent algorithm acts as one of the most important solutions to path planning problem. In order to solve the problems of poor real-time and low accuracy of the heuristic optimization algorithm in 3D path planning, this paper proposes a novel heuristic intelligent algorithm derived from the Beetle Antennae Search (BAS) algorithm. The algorithm proposed in this paper has the advantages of wide search range and high search accuracy, and can still maintain a low time complexity when multiple mechanisms are introduced. This paper combines the BAS algorithm with three non-trivial mechanisms proposed to solve the problems of low search efficiency and poor convergence accuracy in 3D path planning. The algorithm contains three non-trivial mechanisms, including local fast search, aco initial path generation, and searching information orientation. At first, local fast search mechanism presents a specific bounded area and add fast iterative exploration to speed up the convergence of path finding. Then aco initial path generation mechanism is initialized by Ant Colony Optimization (ACO) as a pruning basis. The initialization of the ACO algorithm can quickly obtain an effective path. Using the exploration trend of this path, the algorithm can quickly obtain a locally optimal path. Thirdly, searching information orientation mechanism is employed for BAS algorithm to guarantee the stability of the path finding, thereby avoiding blind exploration and reducing wasted computing resources. Simulation results show that the algorithm proposed in this paper has higher search accuracy and exploration speed than other intelligent algorithms, and improves the adaptability of the path planning algorithms in different environments. The effectiveness of the proposed algorithm is verified in simulation

Explorative search of distributed bio-data to answer complex biomedical questions

Background The huge amount of biomedical-molecular data increasingly produced is providing scientists with potentially valuable information. Yet, such data quantity makes difficult to find and extract those data that are most reliable and most related to the biomedical questions to be answered, which are increasingly complex and often involve many different biomedical-molecular aspects. Such questions can be addressed only by comprehensively searching and exploring different types of data, which frequently are ordered and provided by different data sources. Search Computing has been proposed for the management and integration of ranked results from heterogeneous search services. Here, we present its novel application to the explorative search of distributed biomedical-molecular data and the integration of the search results to answer complex biomedical questions. Results A set of available bioinformatics search services has been modelled and registered in the Search Computing framework, and a Bioinformatics Search Computing application (Bio-SeCo) using such services has been created and made publicly available at http://www.bioinformatics.deib.polimi.it/bio-seco/seco/. It offers an integrated environment which eases search, exploration and ranking-aware combination of heterogeneous data provided by the available registered services, and supplies global results that can support answering complex multi-topic biomedical questions. Conclusions By using Bio-SeCo, scientists can explore the very large and very heterogeneous biomedical-molecular data available. They can easily make different explorative search attempts, inspect obtained results, select the most appropriate, expand or refine them and move forward and backward in the construction of a global complex biomedical query on multiple distributed sources that could eventually find the most relevant results. Thus, it provides an extremely useful automated support for exploratory integrated bio search, which is fundamental for Life Science data driven knowledge discovery

Image multi-level-thresholding with Mayfly optimization

Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization(BFO), firefly-algorithm(FA), bat algorithm (BA), cuckoo search(CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this wor

QueryTogether: Enabling entity-centric exploration in multi-device collaborative search

Collaborative and co-located information access is becoming increasingly common. However, fairly little attention has been devoted to the design of ubiquitous computing approaches for spontaneous exploration of large information spaces enabling co-located collaboration. We investigate whether an entity-based user interface provides a solution to support co-located search on heterogeneous devices. We present the design and implementation of QueryTogether, a multi-device collaborative search tool through which entities such as people, documents, and keywords can be used to compose queries that can be shared to a public screen or specific users with easy touch enabled interaction. We conducted mixed-methods user experiments with twenty seven participants (nine groups of three people), to compare the collaborative search with QueryTogether to a baseline adopting established search and collaboration interfaces. Results show that QueryTogether led to more balanced contribution and search engagement. While the overall s-recall in search was similar, in the QueryTogether condition participants found most of the relevant results earlier in the tasks, and for more than half of the queries avoided text entry by manipulating recommended entities. The video analysis demonstrated a more consistent common ground through increased attention to the common screen, and more transitions between collaboration styles. Therefore, this provided a better fit for the spontaneity of ubiquitous scenarios. QueryTogether and the corresponding study demonstrate the importance of entity based interfaces to improve collaboration by facilitating balanced participation, flexibility of collaboration styles and social processing of search entities across conversation and devices. The findings promote a vision of collaborative search support in spontaneous and ubiquitous multi-device settings, and better linking of conversation objects to searchable entities

Efficient Computation of Map-scale Continuous Mutual Information on Chip in Real Time

Exploration tasks are essential to many emerging robotics applications, ranging from search and rescue to space exploration. The planning problem for exploration requires determining the best locations for future measurements that will enhance the fidelity of the map, for example, by reducing its total entropy. A widely-studied technique involves computing the Mutual Information (MI) between the current map and future measurements, and utilizing this MI metric to decide the locations for future measurements. However, computing MI for reasonably-sized maps is slow and power hungry, which has been a bottleneck towards fast and efficient robotic exploration. In this paper, we introduce a new hardware accelerator architecture for MI computation that features a low-latency, energy-efficient MI compute core and an optimized memory subsystem that provides sufficient bandwidth to keep the cores fully utilized. The core employs interleaving to counter the recursive algorithm, and workload balancing and numerical approximations to reduce latency and energy consumption. We demonstrate this optimized architecture with a Field-Programmable Gate Array (FPGA) implementation, which can compute MI for all cells in an entire 201-by-201 occupancy grid ({\em e.g.}, representing a 20.1m-by-20.1m map at 0.1m resolution) in 1.55 ms while consuming 1.7 mJ of energy, thus finally rendering MI computation for the whole map real time and at a fraction of the energy cost of traditional compute platforms. For comparison, this particular FPGA implementation running on the Xilinx Zynq-7000 platform is two orders of magnitude faster and consumes three orders of magnitude less energy per MI map compute, when compared to a baseline GPU implementation running on an NVIDIA GeForce GTX 980 platform. The improvements are more pronounced when compared to CPU implementations of equivalent algorithms

Information retrieval from civil engineering repositories: the importance of context and granularity

Information about the design and construction of buildings can be structured in a particular way. This is especially correct given the increasing complexity of building product models and the emergence of building information models with project documents linked to them. In addition, engineers usually have distinct information needs. Research shows that engineers working with building information models place particular importance on the understanding of retrieved content before using it or applying it and that exploration of context is essential for this understanding. Both these factors (the nature of engineering content and the information needs of engineers) make general information retrieval techniques for computing relevance and visualizing search results less applicable in civil engineering information retrieval systems. This paper argues that granularity is a fundamental concept that needs to be considered when measuring relevance and visualizing search results in information retrieval systems for repositories of building design and construction content. It is hypothesized that the design of systems with careful regard for granularity would improve engineers’ relevance judgment behavior. To test this hypothesis, a prototype system, called CoMem-XML, was developed and evaluated in terms of the time needed for users to find relevant information, the accuracy of their relevance judgment, and their subjective satisfaction with the prototype. A user study was conducted in which test subjects were asked to complete tasks by using various forms of the prototype, to complete a satisfaction questionnaire, and to be interviewed. The findings show that users perform better and are more satisfied when the search result interface of the CoMem-XML system presents only relevant information in context. On the other hand, interfaces that present the retrieved information out of context (i.e., without highlighting its position in the parts hierarchy) are less effective for participants to judge relevance

Visualize to explore: Towards a different model of scientific information retrieval with emphasis on legume research

In the era of ubiquitous computing and academic hyperproduction, researchers are often encountered with the problem of information overload. Scientific information retrieval systems such as Google Scholar or commercial bibliographic databases, usually display search results as textual lists. This form of representation is suitable for typical browsing tasks, but not for thorough search and exploration. Textual search interfaces usually do not provide users with the appropriate feedback on some relevant aspects of scientific communication, such as the relevance of used terminology, authors' field of expertise, or patterns of collaboration among institutions. This paper presents the basic features of the SCIndeks Visual Search, an alternative information retrieval system which implements different visualization techniques for presenting search results, with a specific emphasis upon publications, scientists and institutions related to legume research. Multidimensional scaling and conceptual maps model were used to visualize the relationships among descriptors and authors of scientific papers dealing with legumes referred in the Serbian Citation Index. The system of visual search was not designed to replace the existing information retrieval model, but rather to enrich the users' information retrieval experience, especially in the case of specific exploration tasks, such as resolving typical vocabulary problems (e.g. polysemy), or analyzing collaboration networks among researchers. Several examples are given to illustrate the advantages of visual search model in the field of legume research and agricultural sciences in general. The authors point out possible benefits of the information visualization as a mediator between the user's query and search results, i.e. between user's information need and information space of the document corpus, anticipating a significant enhancement of mutual awareness of each other’s achievements in various topics of legume research