Search

Current research on Web search has focused on optimizing and evaluating single queries. However, a significant fraction of user queries are part of more complex tasks [Jones and Klinkner 2008] which span multiple queries across one or more search sessions [Liu and Belkin 2010; Kotov et al. 2011]. An ideal search engine would not only retrieve relevant results for a user's particular query but also be able to identify when the user is engaged in amore complex task and aid the user in completing that task [Morris et al. 2008; Agichtein et al. 2012]. Toward optimizing whole-session or task relevance, we characterize and address the problem of intrinsic diversity (ID) in retrieval [Radlinski et al. 2009], a type of complex task that requires multiple interactions with current search engines. Unlike existing work on extrinsic diversity [Carbonell and Goldstein 1998; Zhai et al. 2003; Chen and Karger 2006] that deals with ambiguity in intent across multiple users, ID queries often have little ambiguity in intent but seek content covering a variety of aspects on a shared theme. In such scenarios, the underlying needs are typically exploratory, comparative, or breadth-oriented in nature. We identify and address three key problems for ID retrieval: identifying authentic examples of ID tasks from post-hoc analysis of behavioral signals in search logs; learning to identify initiator queries that mark the start of an ID search task; and given an initiator query, predicting which content to prefetch and rank. [ABSTRACT FROM AUTHOR]

With the development of image search technology, users are no longer satisfied with searching for images using just metadata and textual descriptions. Instead, more search demands are focused on retrieving images based on similarities in their contents (textures, colors, shapes etc.). Nevertheless, one image may deliver rich or complex content and multiple interests. Sometimes users do not sufficiently define or describe their seeking demands for images even when general search interests appear, owing to a lack of specific knowledge to express their intents. A new form of information seeking activity, referred to as exploratory search, is emerging in the research community, which generally combines browsing and searching content together to help users gain additional knowledge and form accurate queries, thereby assisting the users with their seeking and investigation activities. However, there have been few attempts at addressing integrated exploratory search solutions when image browsing is incorporated into the exploring loop. In this work, we investigate the challenges of understanding users' search interests from the images being browsed and infer their actual search intentions. We develop a novel system to explore an effective and efficient way for allowing users to seamlessly switch between browse and search processes, and naturally complete visual-based exploratory search tasks. The system, called Browse-to-Search enables users to specify their visual search interests by circling any visual objects in the webpages being browsed, and then the system automatically forms the visual entities to represent users' underlying intent. One visual entity is not limited by the original image content, but also encapsulated by the textual-based browsing context and the associated heterogeneous attributes. We use large-scale image search technology to find the associated textual attributes from the repository. Users can then utilize the encapsulated visual entities to complete search tasks. The Browse-to-Search system is one of the first attempts to integrate browse and search activities for a visual-based exploratory search, which is characterized by four unique properties: (1) in session--searching is performed during browsing session and search results naturally accompany with browsing content; (2) in context--the pages being browsed provide text-based contextual cues for searching; (3) in focus--users can focus on the visual content of interest without worrying about the difficulties of query formulation, and visual entities will be automatically formed; and (4) intuitiveness--a touch and visual search-based user interface provides a natural user experience. We deploy the Browse-to-Search system on tablet devices and evaluate the system performance using millions of images. We demonstrate that it is effective and efficient in facilitating the user's exploratory search compared to the conventional image search methods and, more importantly, provides users with more robust results to satisfy their exploring experience. [ABSTRACT FROM AUTHOR]

In this paper, we propose a proficient method for knowledge retrieval in edaphology to assist the edaphologists and those who are related with agriculture in a big way. The proposed method mainly consists of two phases of which the first one is to build the knowledge base using XML and the latter part deals with information retrieval using fuzzy search. Initially, the relational database is converted to XML database. This paper discusses two algorithms, one is when the soil characteristics are given as input to have the plant list and in the other, plant names are given as input to have the soil characteristics suited for the plant. While retrieving the query result, the crisp numerical values are converted to fuzzy value using the triangular fuzzy membership function and matched to those in database. Those which satisfy are added to the result list and subsequently, the frequency is found out to rank the result list so as to obtain the final sorted list. Performances metrics are used in order to evaluate the method and compared to baseline paper to identify the number of plants retrieved, ranking efficiency, and computation time and memory usage. Results obtained proved the validity of the method and the method obtained the average computation time of 0.102 seconds and average memory usage of 2486 Kb, which are all far better than our previous method results. [ABSTRACT FROM AUTHOR]

To increase the speed of information retrieval, one message may have multiple replicas in Delay Tolerant Networks (DTN). In this paper, we adopt a discrete time model and focus on the caching policy of stochastic updating information. In particular, the source creates new version in every time slot with certain probability. New version is usually more useful than the older One. We use a utility function to denote the availability of different versions. To constrain the number of replicas, we propose a probabilistic management policy and nodes to discard information with certain probability determined by the version of the information. Our objective is to find the best value of the probability to maximize the total utility value. Because new version is created with certain probability, nodes other than the source may not know whether the information stored in them is the latest version. Therefore, they can make decisions only according to the local state and decisions based on the local state can be seen as focal-policy. We also explore the global-policy, that is, nodes understand the real state. We prove that the optimal policies in both cases conform to the threshold form. Simulations based on both synthetic and real motion traces show the accuracy of our theoretical model. Surprisingly, numerical results show that local-policy is better than the global-policy in some cases. [ABSTRACT FROM AUTHOR]

Humans are always interested in distinguishing natural and artificial entities although there is no sharp demarcation between the two categories. Surprisingly, things do not improve when the second type of entities is restricted to the arguably more constrained realm of physical technical artifacts. This paper helps to clarify the relationship between natural entities and technical artifacts by developing a conceptual landscape within which to analyze these notions. The framework is developed by studying three definitions of technical artifact which arise from different perspectives. All these perspectives share two intuitions: that technical artifacts are physical objects that exist by human intervention; and that technical artifacts are entities to be contrasted to natural entities. Yet the perspectives are different in the way they spell out these intuitions: the relevant human intervention may range from intentional selection to intentional production; and the contrast between technical artifacts and natural entities may be introduced by a constitution relation or by defining properties that set technical artifacts apart. The three perspectives are compared and their similarities and dissimilarities are explored with the help of ontological analysis. [ABSTRACT FROM AUTHOR]

In order to provide effective security protection for tenants' data in cloud computing applications, a third-party security tenant's data protection model is proposed to solve the security problems of cloud tenants data storage and retrieval. Data relationship hiding method is conducted in this model, that is to say the tenants' data is firstly sliced and then one-way function is employed to confuse the relationship between these different data slices. According to the demand for data privacy as well as the security grade definition, a data security grading scheme is further proposed, and the data storage security can be guaranteed by utilizing some encryption and anonymization on the data in sub-tables segmented by the security grading scheme. Combining with private information retrieval method, a keyword retrieval algorithm on the encrypted data is also proposed to protect the cloud data demand privacy security from tenants. The experimental results show the proposed model can well meet the security requirements of tenants' data storage and retrieval in cloud. [ABSTRACT FROM AUTHOR]

This article investigates the aspects of designing a nanocell based molecular memory. An empirical model for molecular device is developed, based on circuit behavior of nitro-substituted Oligo (Phynylene Ethynylene) molecule (OPE). This device model is subsequently used to design nanocell based 1-bit memory and verified using HSPICE. The approach is extended to train the nanocell for multibit storage capability using external voltage signals. It is observed that to successfully train a 2-bit molecular memory, the number of control signals should be approx. one-fourth of total number of nanoparticles. A computational framework is proposed to compute the probability of retrieving the stored data bits correctly, at the output terminal of the nanocell buffer. This nanocell configuration is simulated by systematically varying number of nanoparticles and molecular switches. It is observed that the probability of the existence of at least one path from input to output approaches close to unity with presence of 20 or more nanoparticles in a nanocell. During memory model validation, 1000 samples of 1-bit memory (consisting of 20 nanoparticles) were generated and verified for read and write operations. The model verification results obtained for this memory cell closely match those obtained using analytical solution of probabilistic graph model. [ABSTRACT FROM AUTHOR]

Stop word identification is one of the most important tasks for many text processing applications such as information retrieval. Stop words occur too frequently in documents in a collection and do not contribute significantly to determining the context or information about the documents. These words are worthless as index terms and should be removed during indexing as well as before querying by an information retrieval system. In this paper, we propose an automatic aggregated methodology based on term frequency, normalized inverse document frequency and information model to extract the light stop words from Persian text. We define a ‘light stop word’ as a stop word that has few letters and is not a compound word. In the Persian language, a complete stop word list can be derived by combining the light stop words. The evaluation results, using a standard corpus, show a good percentage of coincidence between the Persian and English stop words and a significant improvement in the number of index terms. Specifically, the first 32 Persian light stop words have a great impact on the index size reduction and the set of stop words can reduce the number of index terms by about 27%. [ABSTRACT FROM PUBLISHER]

Evaluating the performance of collaborative information seeking (CIS) systems and users can be challenging, often more so than individual information-seeking environments. This can be attributed to the complex and dynamic interactions that take place among various users and systems processes in a CIS environment. While some of the aspects of a CIS system or user could be measured by typical assessment techniques from single-user information retrieval/seeking (IR/IS), one often needs to go beyond them to provide a meaningful evaluation, helping to provide not only a sense of performance, but also insights into design decisions (regarding systems) and behavioural trends (regarding users). This article first provides an overview of existing methods and techniques for evaluating CIS (synthesis). It then extracts valuable directives and advice from the literature that inform evaluation choices (suggestions). Finally, the article presents a framework for CIS evaluation with two major parts: system-based and user-based (structure). The proposed framework incorporates various instruments taken from computer and social sciences literature as applicable to CIS evaluations. The lessons from the literature and the framework could serve as important starting points for designing experiments and systems, as well as evaluating system and user performances in CIS and related research areas. [ABSTRACT FROM PUBLISHER]

Context-aware systems based on location open up new possibilities to users in terms of acquiring custom services by gathering context information, especially in systems where the high mobility of users increases their usability. In this context, this article presents a privacy-preserving solution offering context-aware services based on location in MCC. We propose a middleware, called PRECISE, which provides users with custom context-aware recommendations. These recommendations are given by considering the context information, and the users' locations, privacy policies, and previously visited places. MCC plays a key role in this solution, moving the data processing and storage needs to the cloud, as well as further advantages such as elasticity and load balancing. A thorough discussion when comparing PRECISE with other related works confirms that our solution improves the most relevant proposals so far. [ABSTRACT FROM PUBLISHER]

An Information Architecture is the basis for a comprehensive and integrated information system of any organization, capable of providing answers that meet the challenges posed by the contemporary competitive environment. In this paper we analyse a number of representative methodologies used in Information Architecture definitions and, based on a set of pre-defined criteria, we select the Adapted BSP (Business Systems Planning) Methodology to apply in a local public administration organization in Portugal. This methodology proved to be sufficiently flexible and suited to the goals proposed. [ABSTRACT FROM PUBLISHER]

The aim of this study was to examine the information acquisition strategies of expert and competent non-expert intensive care physicians during two simulated diagnostic scenarios involving respiratory distress in an infant. Specifically, the information acquisition performance of six experts and 12 competent non-experts was examined using an eye-tracker during the initial 90 s of the assessment of the patient. The results indicated that, in comparison to competent non-experts, experts recorded longer mean fixations, irrespective of the scenario. When the dwell times were examined against specific areas of interest, the results revealed that competent non-experts recorded greater overall dwell times on the nurse, where experts recorded relatively greater dwell times on the head and face of the manikin. In the context of the scenarios, experts recorded differential dwell times, spending relatively more time on the head and face during the seizure scenario than during the coughing scenario. The differences evident between experts and competent non-experts were interpreted as evidence of the relative availability of task-specific cues or heuristics in memory that might direct the process of information acquisition amongst expert physicians. The implications are discussed for the training and assessment of diagnostic skills. [ABSTRACT FROM AUTHOR]

In this paper, we initiate the theoretical study of cryptographic protocols where the identity, or other credentials and inputs, of a party are derived from its geographic location. We start by considering the central task in this setting, i.e., securely verifying the position of a device. Despite much work in this area, we show that in the vanilla (or standard) model, the above task (i.e., of secure positioning) is impossible to achieve, even if we assume that the adversary is computationally bounded. In light of the above impossibility result, we then turn to Dziembowski's bounded retrieval model (a variant of Maurer's bounded storage model) and formalize and construct information theoretically secure protocols for two fundamental tasks: secure positioning and position-based key exchange. We then show that these tasks are in fact universal in this setting--we show how we can use them to realize secure multiparty computation. Our main contribution in this paper is threefold: to place the problem of secure positioning on a sound theoretical footing; to prove a strong impossibility result that simultaneously shows the insecurity of previous attempts at the problem; and to present positive results showing that the bounded-retrieval framework is a fruitful one to study the foundations of position-based cryptography. [ABSTRACT FROM AUTHOR]

This paper reports on the information seeking behaviour of computer science graduate students. The following research questions are put forward to address the main research objective on how graduate students seek for information: i) what type of information resource do computer science graduate students use?; ii) how do computer science graduate students seek and obtain information?; iii) what are the problems faced by computer science graduate students while seeking information?; iv) does any relationship exist between the use of different information resources and graduates' demographic information (i.e., age and type of graduate program)?; v) does any relationship exist between the use of different information resources and problems in finding information?and vi) does any relationship exist between the use of different information resources and success in finding information?. A survey was conducted at the Faculty of Computer Science and Information Technology, at the University of Malaya, with a sample size of 217 graduate students. The data gathered was analysed quantitatively using SPSS statistical software. The findings show that even though the Internet Search Engine is the first information resource used by computer science graduate, they are however still in doubt with the trustworthiness of the information they retrieved. This has made the students dissatisfied with their initial findings that led them to use more reliable information resources, such as digital libraries and online databases. This study provides insights into how computer science graduate students seek information that offers improvement implications to the development of available information resources and library services. [ABSTRACT FROM AUTHOR]

The purpose of this empirical study is to assess the retrieval effectiveness of controlled and uncontrolled index terms in bibliographic database. Two types of index terms were tested in a web-based environment using the operational large-scale INSPEC database. 15 query types used in the study were both controlled terms and uncontrolled index terms derived from inverse document frequency weights. The retrieval effectiveness was evaluated using Precision. The main finding indicates there are statistically significant differences in precision arising from the two types of index terms; the uncontrolled index terms demonstrate better precision than the controlled index terms. [ABSTRACT FROM AUTHOR]

Efficient information retrieval is essential to enrich user experience when searching for documents in dataspaces. With the continued growth in the volume and complexity of documents, the efficient information retrieval for searches has become increasingly challenging. To improve users' search experience, faceted search combines direct keyword search methods with faceted browsing using a predefined set of categories (facets). This paper studies a faceted search approach that integrates dynamic facets generation with search. To further enhance the faceted search, alternative indexers based on pre-defined ontology for data repositories within dataspaces are evaluated in terms of execution time and data size. Experimental results suggest that combining the proposed faceted search with appropriate indexers improves search performance enhancing user experience. [ABSTRACT FROM AUTHOR]

The problem of mathematical support development is solved to automate the extraction knowledge as production rules from the training data samples. The object of study is the process of constructing models of non-destructive quality control. The subject of study are methods of production rules extraction for synthesis of quality control models. The purpose of the work is to improve the efficiency of the process of production rules extraction for constructing models of quality control based on training samples. The stochastic method for the decision trees synthesis is proposed, which uses information about the informativeness of features, the complexity of the synthesized tree, as well as the accuracy of its recognition, which allows to form on the initial stage a set of tree structures, characterized by a simple hierarchy and low error recognition, in the process of search to create a new set of solutions with taking into account information about the significance of the features and interpretability of generated trees, which, in turn, provides the possibility of constructing a decision tree with a small number of elements (nodes and branches between them), and an acceptable recognition accuracy and retrieval based on it the most valuable instances. The software implementing proposed method is developed. The experiments to study the properties of the proposed method are conducted. The experimental results allow to recommend the proposed method for use in practice. [ABSTRACT FROM AUTHOR]

View-based 3D object retrieval is an emerging research topic that has numerous geographic-related applications in many fields, such as computer-aided design (CAD) and virtual city navigation. This article briefly introduces the recent research progress in view-based 3D object retrieval by reviewing advances and identifying challenges in this field. [ABSTRACT FROM PUBLISHER]

Purpose – The purpose of this paper is to determine whether the quality and reliability of websites’ content can be assessed through the lens of “wisdom of the crowds”. In particular as a case study the authors examine the information supplied over time on several prominent Israeli real estate websites. Design/methodology/approach – The Israeli real estate market was selected for the study, since there are many large, popular and dynamic real estate websites that feature hundreds of thousands of ads, representing most of the supply of real estate properties in the country. The authors built an automatic, ontology-based system that downloaded advertisements from three selected websites every two weeks for a number of months and checked for changes in these advertisements over time. The authors conjecture that wisdom of the crowds is mostly reflected by the information changes on the websites, since they indicate the anticipated market trends. Hence the authors developed a number of statistical measures to comparatively analyse trends of information changes on these websites, and assess their reliability compared to the actual market data and tendencies. Findings – The primary results suggest similar information change trends amongst all the websites. Surprisingly, although some properties did not sell over time, sellers generally did not lower their asking price and were willing to wait. Sellers even raised their asking price, apparently in anticipation of future price increases. Comparison of recurring trends among the websites with the trends of the real market during the same time period and a few months after reveals that wisdom of the crowds is only partially effective as an indicator and predictor of website content quality: it correctly reflects the fluctuation in demand, but not in the prices. Research limitations/implications – This study was conducted over a limited time period of five months, and only in several cities in Israel. Additionally, since buyers are not explicitly represented in these sites, their information behaviour was not analysed, although it undoubtedly influences information changes performed by the sellers. Practical implications – The practical contribution of this study is the ontology of the real estate world. Its assimilation by real estate websites would promote the development of their sites and user services. It would also enable ad sharing amongst the various websites and enable efficient searches by search engines. In addition the tools and measures that the authors developed will allow continued monitoring and analysis of user information change patterns. Originality/value – To the best of the knowledge this is the first study to examine and compare real estate websites’ quality and evaluate their information reliability as wisdom of the crowds. [ABSTRACT FROM AUTHOR]

Purpose – Informally computer scientists reported they could access free copies of research papers they needed via tools such as Google Scholar. To ascertain whether this perception came from widespread free access or from unnoticed employer-paid access, the purpose of this paper was to locate computer science papers and determine what proportion was freely available using Google Scholar. Design/methodology/approach – A sample of 1,967 conference papers and periodical papers from 2003 to 2010, indexed in the ACM Guide to Computing Literature, was searched for manually in Google Scholar, using the paper or article title and the first author's surname and supplementary searches as needed. Findings – Free full-text versions were found for 52 per cent of the conference papers and 55 per cent of the periodical papers. Documents with older publication dates were more likely to be freely accessible than newer documents, with free versions found for 71 per cent of items published in 2003 and 43 per cent of items published in 2010. Research limitations/implications – Results were limited to the retrieval of known computer science publications via Google Scholar. Future research could examine whether the decline found in this study is specific to Google Scholar or reflects a decrease in the free sharing of research by computer scientists. Originality/value – Previous research for computer science found lower levels of free access than this research determined, but the decline found in this study runs contrary to increases that have been found. This research confirms many computer science papers are available for free but also that subscription holdings are necessary for complete coverage of papers in the field. [ABSTRACT FROM AUTHOR]

Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage botleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions. [ABSTRACT FROM AUTHOR]

Glossing facilitates the creation of correct form-meaning connections, but does not encourage inferring or word meaning retrieving. This study investigates the effects of combining glossing with inferring or meaning retrieval on vocabulary learning. One hundred and eighteen university students read a text with target words occurring three times, either glossed or unglossed, under one of three conditions. In the inference-gloss-gloss condition, participants inferred the word meaning in the first word encounter and were provided with glosses for the subsequent two word encounters. In the gloss-retrieval-gloss condition, participants were provided with glosses for the first and last word encounters but had to retrieve the word meaning in the second word encounter. In the full glossing condition, participants were provided with glosses for all the word encounters. Immediately after the intervention, all participants completed a comprehension test and three vocabulary posttests. They completed delayed vocabulary posttests 2 weeks later. The main results showed that the gloss-retrieval-gloss condition was the most effective at improving vocabulary learning. The study concludes that gloss-retrieval-gloss is facilitative to learning new words from context. [Copyright &y& Elsevier]

This paper presents a new method for antenna pattern retrieval from reflection coefficient measurements. A reflective load with known reflective properties is placed close to the aperture of the antenna under test. The reflection coefficient of the antenna is measured at the antenna feed with multiple different reflective loads. The antenna pattern is then solved from the measurements with an inversion method. This paper derives and verifies the analytical foundations needed to implement the method, and demonstrates the method both by simulations and experiments for a pyramidal horn antenna at 30 GHz. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to explore various types of research materials in Indian institutional repositories. Design/methodology/approach – The repositories are identified from the Registry of Open Access Repositories (ROAR). The repository sites were visited to collect the data necessary for the study. Findings – The present study reveals that barring a few repositories the collections of most of the repositories are very low. The percentage of archived materials is high for journal papers, and moderate for conference papers/thesis. However it is very low for preprints/working papers, teaching resources and patents. Originality/value – The study provides an overview of archived material in institutional repositories in India. [ABSTRACT FROM AUTHOR]

Maximum distance separable (MDS) codes are widely used in storage systems to protect against disk (node) failures. A node is said to have capacity l over some field \BBF, if it can store that amount of symbols of the field. An (n,k,l) MDS code uses n nodes of capacity l to store k information nodes. The MDS property guarantees the resiliency to any n-k node failures. An optimal bandwidth (respectively, optimal access) MDS code communicates (respectively, accesses) the minimum amount of data during the repair process of a single failed node. It was shown that this amount equals a fraction of 1/(n-k) of data stored in each node. In previous optimal bandwidth constructions, l scaled polynomially with k in codes when the asymptotic rate is less than 1. Moreover, in constructions with a constant number of parities, i.e., when the rate approaches 1, l is scaled exponentially with k. In this paper, we focus on the case of linear codes with linear repair operations and constant number of parities n-k=r, and ask the following question: given the capacity of a node l what is the largest number of information disks k in an optimal bandwidth (respectively, access) (k+r,k,l) MDS code? We give an upper bound for the general case, and two tight bounds in the special cases of two important families of codes. The first is a family of codes with optimal update property, and the second is a family with optimal access property. Moreover, the bounds show that in some cases optimal-bandwidth codes have larger k than optimal-access codes, and therefore these two measures are not equivalent. [ABSTRACT FROM PUBLISHER]

Since the seminal work of Stigler in 1962, economists have recognized that information is costly to acquire and leads to "search frictions." Growth in online search has lowered the cost of information acquisition. We analyze the expansion of the website Craigslist, which allows users to post job and housing ads. Exploiting the sharp geographic and temporal variation in the availability of online search induced by Craigslist, we produce three key findings: Craigslist significantly lowered classified job advertisements in newspapers, caused a significant reduction in the apartment and house rental vacancy rate, and had no effect on the unemployment rate. [ABSTRACT FROM AUTHOR]

We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. We first elaborate on well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification (“query by example"). Subsequently, we review current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. Eventually, a discussion about the major open challenges concludes the survey. [ABSTRACT FROM AUTHOR]

The analysis of user search goals for a query can be very useful in improving search engine relevance and user experience. Although the research on inferring user goals or intents for text search has received much attention, little has been proposed for image search. In this paper, we propose to leverage click session information, which indicates high correlations among the clicked images in a session in user click-through logs, and combine it with the clicked images' visual information for inferring user image-search goals. Since the click session information can serve as past users' implicit guidance for clustering the images, more precise user search goals can be obtained. Two strategies are proposed to combine image visual information with the click session information. Furthermore, a classification risk based approach is also proposed for automatically selecting the optimal number of search goals for a query. Experimental results based on a popular commercial search engine demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This study examines American high school students’ school-related information-seeking. Comparing advantaged and disadvantaged students’ practices, the research illuminates three phases of their information-seeking activities: 1) learning-opportunities for digital skill building, 2) information-retrieval tactics, and 3) information-evaluation strategies. The inquiry delineates several distinct categories of practice corresponding to each of these phases. In successful information-seeking, learning-opportunities enhance skill acquisition for effective information-retrieval that is followed by discerning information-evaluation. In unsuccessful information-seeking, inadequate learning-opportunities result in ineffective information-retrieval that is followed by disengaged information-nonevaluation. Significantly, gendered differences emerge in this final part of the sequence. Findings indicate that unskilled female information-searchers are more likely to adopt an overtrusting stance. By contrast, unskilled male information-searchers are more likely to adopt an undertrusting attitude towards online content. Both groups of unsuccessful information-searchers truncate this necessary evaluative stage and end the information-seeking process before it can bear fruit. [ABSTRACT FROM PUBLISHER]

Many document classification applications require human understanding of the reasons for data-driven classification decisions by managers, client-facing employees, and the technical team. Predictive models treat documents as data to be classified, and document data are characterized by very high dimensionality, often with tens of thousands to millions of variables (words). Unfortunately, due to the high dimensionality, understanding the decisions made by document classifiers is very difficult. This paper begins by extending the most relevant prior theoretical model of explanations for intelligent systems to account for some missing elements. The main theoretical contribution is the definition of a new sort of explanation as a minimal set of words (terms, generally), such that removing all words within this set from the document changes the predicted class from the class of interest. We present an algorithm to find such explanations, as well as a framework to assess such an algorithm's performance. We demonstrate the value of the new approach with a case study from a real-world document classification task: classifying web pages as containing objectionable content, with the goal of allowing advertisers to choose not to have their ads appear on those pages. A second empirical demonstration on news-story topic classification shows the explanations to be concise and document-specific, and to be capable of providing understanding of the exact reasons for the classification decisions, of the workings of the classification models, and of the business application itself. We also illustrate how explaining the classifications of documents can help to improve data quality and model performance. [ABSTRACT FROM AUTHOR]

Online user forums for technical support are being widely adopted by IT firms to supplement traditional customer support channels. Customers benefit from having an additional means of product support, while firms benefit by lowering the costs of supporting a large customer base. Typically these forums are populated with content generated by users, consisting of questioners (solution seekers) and solvers (solution providers). While questioners can be expected to keep returning as long as they can find answers, firms must employ different means in order to recognize and encourage the contributions of solvers. We identify and compare the impact of two widely adopted recognition mechanisms on the philanthropic behavior of solvers. In the first mechanism, feedback-based recognition, solver contribution is evaluated by questioners. In the second mechanism, quantity-based recognition, all contributions are weighted equally regardless of questioner feedback. We draw on the pro-social behavior literature to identify four drivers of solver contribution: (1) peer recognition, (2) image motivation, (3) social comparison, and (4) social exposure. We show that the choice of recognition mechanism strongly influences a solver's problem-solving behavior, highlighting the importance of the firm's decision in this regard. We address issues of solvers self-selecting a type of recognition mechanism by using propensity score analysis in order to show that solver behavior is a result of forum conditioning. We also study the impact of the recognition mechanism on forum quality and the effectiveness of support to draw comparative analytics. [ABSTRACT FROM AUTHOR]

In the paper we study how the choice of color space affects the user-friendliness of the graphical user interface of CBIR. The pilot study was carried out on 46 students of the Department of Library and Information Science and Book Studies at the Faculty of Arts at the University of Ljubljana. The study showed that user interfaces based on HSV color space are simple to use even for users who do not possess the theoretical knowledge about colors and do not understand HSV color space, while even reasonably good understanding of RGB color space does not guarantee frustration-free user experience when using RGB user interface. User interfaces offering color choice just by clicking were better received than user interfaces that make use of color sliders. This article provides guidance for designing an interface for selecting color in CBIR systems. [ABSTRACT FROM AUTHOR]

Twitter is a popular microblogging service that acts as a ground-level information news flashes portal where people with different background, age, and social condition provide information about what is happening in front of their eyes. This characteristic makes Twitter probably the fastest information service in the world. In this article, we recognize this role of Twitter and propose a novel, user-aware topic detection technique that permits to retrieve, in real time, the most emerging topics of discussion expressed by the community within the interests of specific users. First, we analyze the topology of Twitter looking at how the information spreads over the network, taking into account the authority/influence of each active user. Then, we make use of a novel term aging model to compute the burstiness of each term, and provide a graph-based method to retrieve the minimal set of terms that can represent the corresponding topic. Finally, since any user can have topic preferences inferable from the shared content, we leverage such knowledge to highlight the most emerging topics within her foci of interest. As evaluation we then provide several experiments together with a user study proving the validity and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

Similarity query is a fundamental problem in database, data mining and information retrieval research. Recently, querying incomplete data has attracted extensive attention as it poses new challenges to traditional querying techniques. The existing work on querying incomplete data addresses the problem where the data values on certain dimensions are unknown. However, in many real-life applications, such as data collected by a sensor network in a noisy environment, not only the data values but also the dimension information may be missing. In this work, we propose to investigate the problem of similarity search on dimension incomplete data. A probabilistic framework is developed to model this problem so that the users can find objects in the database that are similar to the query with probability guarantee. Missing dimension information poses great computational challenge, since all possible combinations of missing dimensions need to be examined when evaluating the similarity between the query and the data objects. We develop the lower and upper bounds of the probability that a data object is similar to the query. These bounds enable efficient filtering of irrelevant data objects without explicitly examining all missing dimension combinations. A probability triangle inequality is also employed to further prune the search space and speed up the query process. The proposed probabilistic framework and techniques can be applied to both whole and subsequence queries. Extensive experimental results on real-life data sets demonstrate the effectiveness and efficiency of our approach. [ABSTRACT FROM PUBLISHER]

Motivated by the privacy issues, curbing the adoption of electronic healthcare systems and the wild success of cloud service models, we propose to build privacy into mobile healthcare systems with the help of the private cloud. Our system offers salient features including efficient key management, privacy-preserving data storage, and retrieval, especially for retrieval at emergencies, and auditability for misusing health data. Specifically, we propose to integrate key management from pseudorandom number generator for unlinkability, a secure indexing method for privacy-preserving keyword search which hides both search and access patterns based on redundancy, and integrate the concept of attribute-based encryption with threshold signing for providing role-based access control with auditability to prevent potential misbehavior, in both normal and emergency cases. [ABSTRACT FROM PUBLISHER]

Research is the backbone of any subject field, not just required to be undertaken for its survival and sustenance but also for the furtherance of subject scope. Most of the research activities undertaken at any level are aimed towards the welfare and betterment of living being and humans being the first consideration. Medical research has always been the supreme fantasy of humans as it has got direct bearing upon human health and longevity of life. In the present study attempt has been made to have an analysis of medical literature produced in four most primer medical and research institutions of India. To undertake the study, data was retrieved from Web of Science, Thomson Reuters on November 08, 2012 and the analysis is being undertaken on those publications only identified on this particular databank The study is undertaken with the view to assess the . general publication trend of medical sciences in India by undertaking four primer medical institutions under study. Study of the related literature has also been undertaken so as to develop better understanding of the concept and thereby help in the furtherance of its scope. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid named entity recognition (NER) approach that takes the advantages of rule-based and machine learning-based approaches in order to improve the overall system performance and overcome the knowledge elicitation bottleneck and the lack of resources for underdeveloped languages that require deep language processing, such as Arabic. The complexity of Arabic poses special challenges to researchers of Arabic NER, which is essential for both monolingual and multilingual applications. We used the hybrid approach to develop an Arabic NER system that is capable of recognizing 11 types of Arabic named entities: Person, Location, Organization, Date, Time, Price, Measurement, Percent, Phone Number, ISBN and File Name. Extensive experiments were conducted using decision trees, Support Vector Machines and logistic regression classifiers to evaluate the system performance. The empirical results indicate that the hybrid approach outperforms both the rule-based and the ML-based approaches when they are processed independently. More importantly, our system outperforms the state-of-the-art of Arabic NER in terms of accuracy when applied to ANERcorp standard dataset, with F-measures 0.94 for Person, 0.90 for Location and 0.88 for Organization. [ABSTRACT FROM PUBLISHER]

A new optical architecture for holographic data storage system which is compatible with a Blu-ray Disc™ (BD) system is proposed. In the architecture, both signal and reference beams pass through a single objective lens with numerical aperture (NA) 0.85 for realizing angularly multiplexed recording. The geometry of the architecture brings a high affinity with an optical architecture in the BD system because the objective lens can be placed parallel to a holographic medium. Through the comparison of experimental results with theory, the validity of the optical architecture was verified and demonstrated that the conventional objective lens motion technique in the BD system is available for angularly multiplexed recording. The test-bed composed of a blue laser system and an objective lens of the NA 0.85 was designed. The feasibility of its compatibility with BD is examined through the designed test-bed. [ABSTRACT FROM AUTHOR]

The aim of the study is to know about the various help features offered in IR Interface of E-journal database systems and also taking feedback from the users regarding use and non-use of those help features in their search related activities. Qualitative feedback also taken from the respondents related to the reasons about not using help mechanism functionality offered in the IR interface of E-journal Database systems. Total 305 respondents participated in the study. The study employs a checklist method and questionnaire method for collecting of information. For qualitative data interview method was preferred. The Findings of the study reveals that users were not expressing their high level perceived importance towards using help features available in IR Interface of E- journal Database systems. [ABSTRACT FROM AUTHOR]

SUMMARY Taking an immediate recall test prior to misinformation exposure can increase eyewitness suggestibility-a finding termed retrieval-enhanced suggestibility. Here, we examined whether retrieval-enhanced suggestibility would occur when participants were administered an immediate Cognitive Interview (CI). The CI is an investigative interviewing technique that consistently elicits more correct details in memory reports than standard interviews. In this study, participants watched a video of a crime and then completed a distractor task (control condition), a free recall test, or the CI. They then heard misinformation presented in a narrative. Participants produced more accurate memory details in the CI than in free recall despite spending equal time on both tasks. However, the CI also increased the later report of misinformation relative to the control condition. These results show that initial retrieval can increase subsequent suggestibility even when such retrieval occurs under relatively ideal conditions. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The aim of the study is to know about the various help features offered in IR Interface of E-journal database systems and also taking feedback from the users regarding use and non-use of those help features in their search related activities. Qualitative feedback also taken from the respondents related to the reasons about not using help mechanism functionality offered in the IR interface of E-journal Database systems. Total 305 respondents participated in the study. The study employs a checklist method and questionnaire method for collecting of information. For qualitative data interview method was preferred. The Findings of the study reveals that users were not expressing their high level perceived importance towards using help features available in IR Interface of E-journal Database systems. [ABSTRACT FROM AUTHOR]

This article reports on the results of a project that aimed to investigate the usability of raw machine translated technical support documentation for a commercial online file storage service. Adopting a user-centered approach, the ISO/TR 16982 definition of usability—goal completion, satisfaction, effectiveness, and efficiency— is utilized and eye-tracking measures that are shown to be reliable indicators of cognitive effort are applied along with a posttask questionnaire. The study investigated these measures for the original user documentation written in English and in four target languages: Spanish, French, German, and Japanese, all of which were translated using a freely available online statistical machine translation engine. Using native speakers for each language, the study found several significant differences between the source and MT output, a finding that indicates a difference in usability between well-formed content and raw machine translated content. One target language in particular, Japanese, was found to have a considerably lower usability level when compared with the original English. [ABSTRACT FROM PUBLISHER]

Modelling the distribution of document scores returned from an information retrieval (IR) system in response to a query is of both theoretical and practical importance. One of the goals of modelling document scores in this manner is the inference of document relevance. There has been renewed interest of late in modelling document scores using parameterised distributions. Consequently, a number of hypotheses have been proposed to constrain the mixture distribution from which document scores could be drawn. In this article, we show how a standard performance measure (i.e., average precision) can be inferred from a document score distribution using labelled data. We use the accuracy of the inference of average precision as a measure for determining the usefulness of a particular model of document scores. We provide a comprehensive study which shows that certain mixtures of distributions are able to infer average precision more accurately than others. Furthermore, we analyse a number of mixture distributions with regard to the recall-fallout convexity hypothesis and show that the convexity hypothesis is practically useful. Consequently, based on one of the best-performing score-distribution models, we develop some techniques for query-performance prediction (QPP) by automatically estimating the parameters of the document score-distribution model when relevance information is unknown. We present experimental results that outline the benefits of this approach to query-performance prediction. [ABSTRACT FROM AUTHOR]

Formulating and processing phrases and other term dependencies to improve query effectiveness is an important problem in information retrieval. However, accessing word-sequence statistics using inverted indexes requires unreasonable processing time or substantial space overhead. Establishing a balance between these competing space and time trade-offs can dramatically improve system performance. In this article, we present and analyze a new index structure designed to improve query efficiency in dependency retrieval models. By adapting a class of (∈, δ)-approximation algorithms originally proposed for sketch summarization in networking applications, we show how to accurately estimate statistics important in term-dependency models with low, probabilistically bounded error rates. The space requirements for the vocabulary of the index is only logarithmically linked to the size of the vocabulary. Empirically, we show that the sketch index can reduce the space requirements of the vocabulary component of an index of n-grams consisting of between 1 and 4 words extracted from the GOV2 collection to less than 0.01% of the space requirements of the vocabulary of a full index. We also show that larger n-gram queries can be processed considerably more efficiently than in current alternatives, such as positional and next-word indexes. [ABSTRACT FROM AUTHOR]

Every act of information processing can in principle be decomposed into the component operations of information storage, transfer, and modification. Yet, while this is easily done for today's digital computers, the application of these concepts to neural information processing was hampered by the lack of proper mathematical definitions of these operations on information. Recently, definitions were given for the dynamics of these information processing operations on a local scale in space and time in a distributed system, and the specific concept of local active information storage was successfully applied to the analysis and optimization of artificial neural systems. However, no attempt to measure the space-time dynamics of local active information storage in neural data has been made to date. Here we measure local active information storage on a local scale in time and space in voltage sensitive dye imaging data from area 18 of the cat. We show that storage reflects neural properties such as stimulus preferences and surprise upon unexpected stimulus change, and in area 18 reflects the abstract concept of an ongoing stimulus despite the locally random nature of this stimulus. We suggest that LAIS will be a useful quantity to test theories of cortical function, such as predictive coding. [ABSTRACT FROM AUTHOR]

Patent documents with sophisticated technical information are valuable for developing new technologies and products. They can be written in almost any language, leading to language barrier problems during retrieval. Traditionally, cross-language information retrieval and cross-language document matching have used text-translation-based or index-set-mapping methods. There are several challenges to the traditional methods, however, such as difficulties with natural language translation, complications owing to bilingual or multi-lingual translations (translating between two or more than two languages), and the unavailability of a parallel dual-language document set. This study offers a new and robust solution to cross-language patent document matching: the International Patent Classification (IPC) based concept bridge approach. The proposed method applies Latent Semantic Indexing to extract concepts from each set of patent documents and utilizes the IPC codes to construct a cross-language mediator that expresses patent documents in different languages. Experiments were carried out to demonstrate the performance of the proposed method. There were 3000 English patents and 3000 Chinese patents gathered as training documents from the United States Patent and Trademark Office and the Taiwan Intellectual Property Office, respectively. Another 30 English patents and another 30 Chinese patents were collected to be query patents. Finally, evaluations using an objective measure and subjective judgement were conducted to prove the feasibility and effectiveness of our method. The results show that our method out-performs the traditional text-translation methods. [ABSTRACT FROM PUBLISHER]

The microblog has become a new global hot spot. Information retrieval (IR) technologies are necessary for accessing the massive amounts of valuable user-generated contents in the microblog sphere. The challenge in searching relevant microblogs is that they are usually very short with sparse vocabulary and may fail to match queries. Pseudo-relevance feedback (PRF) via query-expansion has been proven in previous studies to successfully increase the number of matches in IR. However, a critical problem of PRF is that the pseudo-relevant feedback may not be truly relevant, and thus may introduce noise to query expansion. In this paper, we exploit the dynamic nature of microblogs to address this problem. We first present a novel dynamic PRF technique, which is capable of expanding queries with truly relevant keywords by extracting representative terms based on the query’s temporal profile. Next we present query expansion from external knowledge sources based on negative and positive feedbacks. We further consider that the choice of PRF strategy is query-dependent. A two-level microblog search framework is presented. At the high level, a temporal profile is constructed and categorized for each query; at the low level, hybrid PRF query expansion combining dynamic and external PRF is adopted based on the query category. Experiments on a real data set demonstrate that the proposed method significantly increases the performance of microblog searching, compared with several traditional retrieval models, various query expansion methods and state-of-art recency-based models for microblog searching. [ABSTRACT FROM PUBLISHER]

The suffix tree is a very important data structure in string processing, but typical implementations suffer from huge space consumption. In large-scale applications, compressed suffix trees (CSTs) are therefore used instead. A CST consists of three (compressed) components: the suffix array, the longest common prefix (LCP)-array and data structures for simulating navigational operations on the suffix tree. The LCP-array stores the lengths of the LCPs of lexicographically adjacent suffixes, and it can be computed in linear time. In this article, we present a new LCP-array construction algorithm that is fast and very space efficient. In practice, our algorithm outperforms alternative algorithms. Moreover, we introduce a new compressed representation of LCP-arrays. [ABSTRACT FROM AUTHOR]