Integrating understandability in the evaluation of consumer health search engines

In this paper we propose a method that integrates the no- tion of understandability, as a factor of document relevance, into the evaluation of information retrieval systems for con- sumer health search. We consider the gain-discount evaluation framework (RBP, nDCG, ERR) and propose two understandability-based variants (uRBP) of rank biased precision, characterised by an estimation of understandability based on document readability and by different models of how readability influences user understanding of document content. The proposed uRBP measures are empirically contrasted to RBP by comparing system rankings obtained with each measure. The findings suggest that considering understandability along with topicality in the evaluation of in- formation retrieval systems lead to different claims about systems effectiveness than considering topicality alone.

Understanding financial information used to assess small firm performance : an Australian qualitative study

Purpose The purpose of this paper is to investigate the reality of financial and management accounting in a small group of small firms. Specifically, from the owner's perspective, an exploration is undertaken to see what financial information is collected, how it is used (or not) to make business decisions and evaluate the firm's performance, and the role played by the accountant in that process. Design/methodology/approach A phenomenological paradigm underpins this exploratory study. Semi‐structured interviews were undertaken with the owners of ten small firms, where the focus was on understanding what happens in an organisational setting, as opposed to theory and textbook practice. Findings The qualitative data supported prior research in other countries. The in‐depth analysis revealed a very basic understanding of accounting information and problems with the financial literacy amongst these small firm owners. Accounting reports were not widely produced or used, so an informal assessment, such as how much cash was in the bank, was the primary means of assessing business performance. Accountants were used for taxation services, although some owners sought more general business advice. Originality/value An understanding is developed of why there might be a gap between textbook rhetoric and reality of accounting practice in small firms. The conclusion is that accounting textbooks need to include more information about the reality of financial management in small firms.

Locality-sensitive hashing for protein classification

Determination of sequence similarity is a central issue in computational biology, a problem addressed primarily through BLAST, an alignment based heuristic which has underpinned much of the analysis and annotation of the genomic era. Despite their success, alignment-based approaches scale poorly with increasing data set size, and are not robust under structural sequence rearrangements. Successive waves of innovation in sequencing technologies – so-called Next Generation Sequencing (NGS) approaches – have led to an explosion in data availability, challenging existing methods and motivating novel approaches to sequence representation and similarity scoring, including adaptation of existing methods from other domains such as information retrieval. In this work, we investigate locality-sensitive hashing of sequences through binary document signatures, applying the method to a bacterial protein classification task. Here, the goal is to predict the gene family to which a given query protein belongs. Experiments carried out on a pair of small but biologically realistic datasets (the full protein repertoires of families of Chlamydia and Staphylococcus aureus genomes respectively) show that a measure of similarity obtained by locality sensitive hashing gives highly accurate results while offering a number of avenues which will lead to substantial performance improvements over BLAST..

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Towards universal search design

For people with cognitive disabilities, technology is more often thought of as a support mechanism, rather than a source of division that may require intervention to equalize access across the cognitive spectrum. This paper presents a first attempt at formalizing the digital gap created by the generalization of search engines. This was achieved through the development of a mapping of cognitive abilities required by users to execute low- level tasks during a standard Web search task. The mapping demonstrates how critical these abilities are to successfully use search engines with an adequate level of independence. It will lead to a set of design guidelines for search engine interfaces that will allow for the engagement of users of all abilities, and also, more importantly, in search algorithms such as query suggestion and measure of relevance (i.e. ranking).

Tech Ed students strut their stuff: information literacy and a practical assignment

This reports a study that seeks to explore the experience of students majoring in technology and design in an undergraduate education degree. It examines their experiences in finding and using information for a practical assignment. In mapping the variation of the students' experience, the study uses a qualitative, interpretive approach to analyse the data, which was collected via one-to-one interviews. The analysis yielded five themes through which technology education students find and use information: interaction with others; experience (past and new); formal educational learning; the real world; and incidental occurrences. The intentions and strategies that form the students' approaches to finding and using information are discussed. So too are the implications for teaching practice.

Diagnose this if you can: On the effectiveness of search engines in finding medical self-diagnosis information

An increasing amount of people seek health advice on the web using search engines; this poses challenging problems for current search technologies. In this paper we report an initial study of the effectiveness of current search engines in retrieving relevant information for diagnostic medical circumlocutory queries, i.e., queries that are issued by people seeking information about their health condition using a description of the symptoms they observes (e.g. hives all over body) rather than the medical term (e.g. urticaria). This type of queries frequently happens when people are unfamiliar with a domain or language and they are common among health information seekers attempting to self-diagnose or self-treat themselves. Our analysis reveals that current search engines are not equipped to effectively satisfy such information needs; this can have potential harmful outcomes on people’s health. Our results advocate for more research in developing information retrieval methods to support such complex information needs.

Quality-aware review selection based on product feature taxonomy

As of today, user-generated information such as online reviews has become increasingly significant for customers in decision making process. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help the users tackle the information overload problem. In order to extract useful information from overwhelming reviews, considerable work has been proposed such as review summarization and review selection. Particularly, to avoid the redundant information, researchers attempt to select a small set of reviews to represent the entire review corpus by preserving its statistical properties (e.g., opinion distribution). However, one significant drawback of the existing works is that they only measure the utility of the extracted reviews as a whole without considering the quality of each individual review. As a result, the set of chosen reviews may consist of low-quality ones even its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we proposed a review selection method which takes review quality into consideration during the selection process. Specifically, we examine the relationships between product features based upon a domain ontology to capture the review characteristics based on which to select reviews that have good quality and preserve the opinion distribution as well. Our experimental results based on real world review datasets demonstrate that our proposed approach is feasible and able to improve the performance of the review selection effectively.

Using conceptual tool for intuitive interaction to design intuitive website for SME in India : A case study

Statistical reports of SMEs Internet usage from various countries indicate a steady growth. However, deeper investigation of SME’s e-commerce adoption and usage reveals that a number of SMEs fail to realize the full potential of e-commerce. Factors such as lack of tools and models in Information Systems and Information Technology for SMEs, and lack of technical expertise and specialized knowledge within and outside the SME have the most effect. This study aims to address the two important factors in two steps. First, introduce the conceptual tool for intuitive interaction. Second, explain the implementation process of the conceptual tool with the help of a case study. The subject chosen for the case study is a real estate SME from India. The design and development process of the website for the real estate SME was captured in this case study and the duration of the study was four months. Results indicated specific benefits for web designers and SME business owners. Results also indicated that the conceptual tool is easy to use without the need for technical expertise and specialized knowledge.

Scalable document hashing and retrieval

This thesis studies document signatures, which are small representations of documents and other objects that can be stored compactly and compared for similarity. This research finds that document signatures can be effectively and efficiently used to both search and understand relationships between documents in large collections, scalable enough to search a billion documents in a fraction of a second. Deliverables arising from the research include an investigation of the representational capacity of document signatures, the publication of an open-source signature search platform and an approach for scaling signature retrieval to operate efficiently on collections containing hundreds of millions of documents.

Using Mathematical Morphology for Geometric Music Retrieval

The usual task in music information retrieval (MIR) is to find occurrences of a monophonic query pattern within a music database, which can contain both monophonic and polyphonic content. The so-called query-by-humming systems are a famous instance of content-based MIR. In such a system, the user's hummed query is converted into symbolic form to perform search operations in a similarly encoded database. The symbolic representation (e.g., textual, MIDI or vector data) is typically a quantized and simplified version of the sampled audio data, yielding to faster search algorithms and space requirements that can be met in real-life situations. In this thesis, we investigate geometric approaches to MIR. We first study some musicological properties often needed in MIR algorithms, and then give a literature review on traditional (e.g., string-matching-based) MIR algorithms and novel techniques based on geometry. We also introduce some concepts from digital image processing, namely the mathematical morphology, which we will use to develop and implement four algorithms for geometric music retrieval. The symbolic representation in the case of our algorithms is a binary 2-D image. We use various morphological pre- and post-processing operations on the query and the database images to perform template matching / pattern recognition for the images. The algorithms are basically extensions to classic image correlation and hit-or-miss transformation techniques used widely in template matching applications. They aim to be a future extension to the retrieval engine of C-BRAHMS, which is a research project of the Department of Computer Science at University of Helsinki.

Clinical teaching: Maintaining an educational role for doctors in the new health care environment

Context and objectives: Good clinical teaching is central to medical education but there is concern about maintaining this in contemporary, pressured health care environments. This paper aims to demonstrate that good clinical practice is at the heart of good clinical teaching. Methods: Seven roles are used as a framework for analysing good clinical teaching. The roles are medical expert, communicator, collaborator, manager, advocate, scholar and professional. Results: The analysis of clinical teaching and clinical practice demonstrates that they are closely linked. As experts, clinical teachers are involved in research, information retrieval and sharing of knowledge or teaching. Good communication with trainees, patients and colleagues defines teaching excellence. Clinicians can 'teach' collaboration by acting as role models and by encouraging learners to understand the responsibilities of other health professionals. As managers, clinicians can apply their skills to the effective management of learning resources. Similarly skills as advocates at the individual, community and population level can be passed on in educational encounters. The clinicians' responsibilities as scholars are most readily applied to teaching activities. Clinicians have clear roles in taking scholarly approaches to their practice and demonstrating them to others. Conclusion: Good clinical teaching is concerned with providing role models for good practice, making good practice visible and explaining it to trainees. This is the very basis of clinicians as professionals, the seventh role, and should be the foundation for the further development of clinicians as excellent clinical teachers.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.

A semantic middleware to enhance current multimedia retrieval systems with content-based functionalities

210 p. : graf.