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Adapting traditional paper-pencil instruments to computer-based environments has received considerable attention from the research community due to the possible administration mode effects on obtained measures. When differences due to mode of completion (i.e., paper-pencil, computer-based) are present, threats to measurement validity are posed. In this research, administration mode effects of web-based and conventional paper-pencil versions of a parent-completed developmental questionnaire, the Ages & Stages Questionnaires (ASQ), were estimated. Setting this study apart from similar studies reported in the literature, the ASQ requires parents to observe and rate their children's behavior. Most measures adapted for web-based administration are not observational. Using item response model invariance testing procedures, analyses tested whether ASQ items administered via the Internet function differently from corresponding traditional paper- pencil items. Analyzing the 4-, 12-, and 24-month ASQ intervals, statistically significant differences (i.e., DIF, or differential item functioning) were obtained on 10 of the 90 items examined in this study. Although DIF was observed for some items, the overall DIF model was rejected for all domains. On the basis of these results, the paper-pencil and web-based measures can be considered equivalent and the mode effect is not present; ASQ measures obtained from either mode are therefore interchangeable. Noteworthy remedies are considered for web-based administration of the 10 specific items lacking invariance. [ABSTRACT FROM AUTHOR]

Research studies generate data in various forms. Data can be quantitative or qualitative. Research involving human subjects requires protection of data to ensure privacy. Various regulations and local policies need to be followed to ensure data security. Data management plans are critical for effective data stewardship and include details plan on data collection, management, storage, and formatting. This paper will review data collection tools, data security strategies, file management, data storage, government regulations, prepping data for analysis, reference management, and file management. [ABSTRACT FROM AUTHOR]

Purpose: Asia Partnership Conference of Pharmaceutical Associations (APAC) promote regulatory agility of four important best practices i.e. reliance, digital platform, accepting electronic document and process integration. Dialogues and strong partnership witnessed reforms and efficiencies amidst the pandemic. In tracking the progress of regulatory agility, APAC identifies areas for improvement and recommends prioritizing these areas for change. Methods: As one voice, 13 main industry associations under the umbrella of APAC sent joint letters to our National Regulatory Authorities (NRAs) with a call to maintain regulatory agility and consider new ways of working. Consequently, APAC surveyed its member associations to measure regulatory agilities implemented by the NRAs during 2020 and 2021 in view of the pandemic. Results: This paper reports progress in implementing regulatory agility, e.g. the number of economies that can accept electronic Certificate of Pharmaceutical Products (eCPP) has reached 100% for the economies that require CPP and more than 90% can waive onsite inspection in the presence of Good Manufacturing Practice (GMP) certificate and/or inspection report. The paper also features the progress made in Malaysia, the Philippines, and the ASEAN (Association of South East Asian Nations) regional reliance initiative to reduce inefficiencies and duplications. Conclusions: We have demonstrated the power of working together to enable regulatory agilities and efficiencies. APAC will continue to track the progress of all economies including India within the areas for improvement prioritized and discussed in this paper. APAC is also committed to working with key stakeholders including our NRAs in Asia to sustain and enable a new era of innovation ushered in by COVID-19 to benefit patients. [ABSTRACT FROM AUTHOR]

• Remote learning should be a blend of asynchronous and synchronous material. • A wealth of free, on-demand videos and web-based resources are available. • Instructors should keep in mind the potential pitfalls to using online material. • Learning objectives should drive the search for on-demand content. • Accountability mechanisms are essential to ensuring student engagement. The move to online instruction forced many educators towards the task of producing recorded content, many for the first time. The result was that many instructors, usually those with limited or no remote-teaching experience, produced 60- to 90-minute audioonly lectures, recorded using Powerpoint or a conferencing platform, and posted them to a Learning Management System (LMS). Instructors more well-versed in online pedagogy or those who utilized instructional design best-practice training, produced video content more in-line with established research, which states that videos should be no longer than 6 to 15 min. Regardless of video length and quality, creating recorded content is time-consuming, resource-intensive, and arduous even for experienced educators. The alternative to self-created content is to mine the wealth of freely available forensic science videos, webinars, and web-based resources already posted to the internet. Prior to the move to remote instruction, some forensic educators integrated YouTube clips into lecture material. Post-pandemic, the quantity, and often quality, of online content has greatly increased. However, on-demand resources are spread widely across the internet. Whilst some repositories of material, such as webinars from individual organizations exist, other sources of content require more intentional searching. Identified content is meaningless unless integrated into a course or module's learning objectives. Instructors may need to shorten lengthy webinars or edit extraneous material in order to maintain educational best practice. Finally, once content is captured and posted to a course LMS, an instructor must set up an accountability structure to ensure students are viewing and digesting required material. The first half of this paper will outline the types and locations of free, on-demand online forensic materials and resources. As forensic science education looks to the future, the creation and use of web-based resources is likely to increase. The second half of this paper will address what to do once content is identified and how to effectively utilize it within the framework of remote instruction best-practice. Existing content can help the educator work smarter, not harder. The wealth of online forensic resources can help illustrate concepts and add variety to LMS-posted content, leading to a more enriching experience for the student. [ABSTRACT FROM AUTHOR]

Based on scientific literature analysis, this paper studies the development process of technologies of specific technology domain, and tries to identify evolution paths of technology development, evaluate the maturity of technologies, and forecast the development trend, which can support scientific research management and decision-making. Based on the technology life cycle theory and literature citation network, this paper analyzes the citation main path, proposes a method to judge and identify the evolution path of technology development at different stages, and takes the research field of image recognition as an example to carry out empirical research. The experimental results show that this method can identify the evolution path of technology development effectively by stages and grasp the key points of technology development. The technology development path analysis method based on technology life cycle needs to be combined with professional field knowledge mining text content for deeper interpretation. [ABSTRACT FROM AUTHOR]

Copyright of Tourism & Management Studies is the property of Escola Superior de Gestao, Hotelaria e Turismo, Universidade do Algarve and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims to clarify the current status, hotspots, historical evolution and development trend of the research on funding and management of basic research in China. Firstly, 736 relevant papers indexed during 1992-2019 in the core journal database of China National Knowledge Infrastructure (CNKI) were analyzed in details. Using the information visualization software CiteSpaceV5.6.R3, the institutional cooperation co-occurrence map, author cooperation co-occurrence map, keyword co-occurrence map, and keyword timeline view maps were plotted through content mining. The results show that: the research institutions in the research field of basic research funding and management have formed three core cooperation networks, and the institutions in Beijing attract the most attention; most of the prolific and active authors choose teamwork over independent research; the research topics mainly fall into four aspects of basic research: input, subjects, results, and talents; the research hotspots are in line with policies and demands, and evolve through three stages: exploration and gradual progress, adjustment and development, and expansion and acceleration. The research results open up a new direction for relevant studies, and provide a reference for innovative parties to implement basic research. [ABSTRACT FROM AUTHOR]

Emerging infectious diseases have affected every aspect of medical practice and the advent of COVID-19 in 2019 created many challenges for basic science research methods. Face-to-face data collection was extremely problematic to undertake in any healthcare environment. The emergency department (ED) in low-resource environments, specifically, was severely impacted by the research restrictions, the psychological state of the ED staff, and the lack of perceived importance of research in the emergency departments. This paper aims to reflect on the challenges in collecting research data in the ED during the COVID-19 pandemic and provides some suggestions and lessons learnt for other researchers embarking on such a path. Notwithstanding challenges inherent to virtual platforms, video-calling may be an option rather than in-person interviewing and can allow for data collection over a large geographical area, even when social distancing measures are not in place. New opportunities were acquired through which the self- reflexive practice characteristics of action research were strengthened. [ABSTRACT FROM AUTHOR]

Copyright of Japanese Sociological Review / Shakaigaku Hyoron is the property of Japan Sociological Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The overhead contact system (OCS), as the power source of electrified railway, has a complex composition and various types of faults, so it places high requirements on its fault prevention. In recent years, with the establishment of railway OCS fault database, association analysis has been used to implement fault prevention from system-wise perspective and provide guidance for operation and maintenance. However, due to the hierarchical structure of fault database, the existing frequent itemset mining has a lot of redundancy in the results, and cannot locate the most precise faults, which affects the decision-making and makes troubleshooting lack of pertinence. To address this issue, this paper proposed a new concept, called marginal frequent itemset, which is an itemset composed of as precise items as possible in hierarchical database that meets the threshold, and an alternative mining task: mining marginal frequent itemsets instead of all the frequent itemsets. Two methods, path transform and descending depth of itemset, are proposed for achieving mining a set of marginal frequent itemsets. Two novel measures, margin degree and marginal information quantity, are proposed to evaluate the content of the mining results. An efficient algorithm, named MFIM CL , is developed for mining cross-level marginal frequent itemsets from railway OCS fault database. Our performance study shows that MFIM CL has high performance and can obtain more key information and reduce the number of results. Furthermore, marginal frequent itemset mining can simplify the fault relation network constructed by association rules and optimize the decision-making process for fault prevention of railway OCS. • Marginal frequent itemset can locate the most precise items in the database with hierarchical structure in association analysis. • Margin degree and marginal information quantity are designed to evaluate the content of mining results. • The algorithm MFIM CL outperforms existent algorithms for railway fault and can reduce the redundancy of results. • The decision-making for maintenance based on fault relation network can be optimized by marginal frequent itemset mining. [ABSTRACT FROM AUTHOR]

Copyright of Estudios Atacameños is the property of Estudios Atacamenos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For a number of compelling scientific, operational, and regulatory reasons, the use of electronic data capture is becoming the preferred means of collecting clinical outcome assessment (eg, patient-reported outcome [PRO]) data in clinical trials. Electronic PRO (ePRO) data collection leverages screen-based technologies (eg, handheld devices, tablet computers, and web-based systems) and telephone-based (eg, interactive voice response) systems. Data collection is routinely either site based (ie, clinical study site) or field based (eg, subject’s home, school, or workplace). While tablet computers are often used for site-based PRO data collection, handheld devices have become the mainstay for ePRO data capture in field-based settings. The data collection devices are usually provisioned to the sites or subjects by an ePRO system provider contracted by the clinical trial sponsor. With site-based data collection, study staff are responsible for ensuring subject compliance with the protocol-driven data collection procedures, whereas with field-based data collection, the subject is responsible for compliance with the data entry requirements and sites are accountable for remotely monitoring the data for compliance. In addition to site and subject compliance issues, technology-related factors must be anticipated in order to adhere to the electronic PRO data collection plan. The objective of this paper is to describe study site-, subject-, and technology-related factors that may lead to deviations from the planned electronic collection of PRO data (eg, defaulting to paper-based data collection) and to provide recommendations aimed at preventing potential problems or quickly resolving problems once they occur. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe the 4-step process (consent, selection, protection, and abstraction) of acquiring a large sample of chiropractic patient records from multiple practices and subsequent data abstraction. From April 2017 to December 2017, RAND acquired patient records from 99 chiropractic practices across the United States. The records included patients enrolled in a survey e-study (prospective sample) and a random sample of all clinic patients (retrospective sample) with chronic back or neck pain. Clinic staff were trained to collect the sample, scan, and transfer the records. We designed an online data collection tool for abstraction. Protocols were instituted to protect patient confidentiality. Doctors of chiropractic were selected and trained as abstractors, and a system was established to monitor data collection. In compliance with data protection protocols, 3603 patient records were scanned, including 1475 in the prospective sample and 2128 in the random sample. A total of 1716 patients (prospective sample) consented to having their records scanned, but only 1475 could be retrieved. Of records scanned, 19% were unusable owing to illegibility, no care during the period of interest, or poor scanning. The abstractor interrater reliability for appropriateness of care decisions was fair to moderate (κ.38-.48). The acquisition, handling, and abstraction of a large sample of chiropractic records was a complex task with challenges that necessitated adapting planned approaches. Of the records abstracted, many revealed incomplete provider documentation regarding the details of and rationale for care. Better documentation and more standardized record keeping would facilitate future research using patient records. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Ciencias Economicas: Investigacion y Reflexion is the property of Revista Facultad de Ciencias Economicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of African Journal of Reproductive Health is the property of Women's Health & Action Research Centre and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Finding information on Web is a difficult and challenging task because of the extremely large volume of data. Search engine can be used to facilitate this task, but it is still difficult to cover all the webpages present on Web. This paper proposes a query based crawler where a set of keywords relevant to the topic of interest of the user is used to shoot queries on search interface. These search interfaces are found on webpage of the website corresponding to seed URL. This helps crawler to get most relevant links from the domain without actually going in depth of that domain. No existing focused crawling approach uses query based approach to find webpages of interest. In the proposed crawler, list of keywords is passed to the search query interfaces found on the websites. The proposed work will give the most relevant information based on the keywords in a particular domain without actually crawling through many irrelevant links in between them. [ABSTRACT FROM AUTHOR]

Copyright of Revista Cubana de Ciencias Informáticas is the property of Universidad de las Ciencias Informaticas (UCI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research questions require specific data collection techniques to appropriately explore and understand the phenomena of interest. Observation as a term features commonly in the literature as a way to describe both the design of a study and methods deployed within procedures. Observation as a data collection method is a mode of inquiry to systematically collect information about different settings and groups. However, the objective of observation in data collection is to better understand the phenomena of interest situated in context. Specifically, observation data collection can improve understanding of practice, processes, knowledge, beliefs, and attitudes embedded in clinical work and social interactions. This pragmatic paper will assist emergency nurses and other clinicians to understand how observation can be used as a data collection method within clinical practice. [ABSTRACT FROM AUTHOR]

• This paper aims to assess books' comprehensive impacts via integrating multiple resources. • The evaluation results based on evaluation system are more accurate and comprehensive than those based on single resource. • The existing evaluation metrics extracted from various resources are usually complementary to each other. • Finer-grained resource mining on is conducted to detect multi-level evaluation metrics. The surge in the number of books published makes the manual evaluation methods (e.g. peer review) difficult to efficiently evaluate books. The use of books' citations and alternative evaluation metrics (e.g. library holdings, social media mentions, book reviews) can assist manual evaluation and reduce the cost of evaluation. However, most existing evaluation research was based on a single evaluation source with coarse-grained analysis, which may obtain incomprehensive or one-sided evaluation results of book impact. Meanwhile, relying on a single resource for book assessment may lead to the risk that the evaluation results cannot be obtained due to the lack of the evaluation data, especially for newly published books. Hence, this paper measured book impact based on an evaluation system constructed by integrating multiple evaluation sources. Specifically, we conducted finer-grained mining on the multiple evaluation sources, including books' internal evaluation resources (e.g. books' contents) and external evaluation resources (e.g. books' reviews, books' citations and books' usages). Various technologies (e.g. topic extraction, sentiment analysis, text classification) were used to extract corresponding evaluation metrics from the internal and external evaluation resources. Then, Expert evaluation combined with analytic hierarchy process was used to integrate the evaluation metrics and construct a book impact evaluation system. Finally, the reliability of the evaluation system was verified by comparing with the results of expert evaluation, detailed and diversified evaluation results were then obtained. The experimental results reveal that differential evaluation resources can measure the books' impacts from different dimensions, and the integration of multiple evaluation data can assess books more comprehensively. Meanwhile, the book impact evaluation system can provide personalized evaluation results according to the users' evaluation purposes. In addition, the disciplinary differences should be considered for assessing books' impacts. [ABSTRACT FROM AUTHOR]

The increase in the use of electronic patient-reported outcome (ePRO) instruments has presented study teams with considerations not previously encountered with paper. Specifically, in an effort to minimize missing data, there is now the opportunity of requiring subjects to provide a response to an item before allowing the subject to proceed to the next item. While the ability to require subjects to respond to ePRO items would seem to guarantee a complete data set, it raises questions about the conditions under which it is appropriate to require subjects to respond to the items in an instrument. This article provides guidance on the circumstances under which allowing a subject to opt out of responding to ePRO items may be appropriate. Three main scenarios are discussed: (1) requiring subjects to complete all items in all the instruments in the study, (2) allowing subjects to opt out of at least some selective items that do not support key primary or secondary endpoints, and (3) allowing subjects to opt out of responding to any or all items in the study. For either of the 2 scenarios allowing the subject to opt out of responding to an item, the use of programmed edit checks is highly recommended to confirm that the subject intended to “skip” or “opt out of” the item. This ensures that, at the end of the study, the database contains an explicit data point indicating when a subject has actively decided to skip an item. While this article is focused on patient-reported outcomes, the issues raised could also apply to other clinical outcome assessments, such as clinician- and observer-reported outcomes. [ABSTRACT FROM AUTHOR]

Automatic activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors, and permit continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in healthcare applications, for automatic and intelligent daily activity monitoring for elderly people. In this paper, we present novel data analytic scheme for intelligent Human Activity Recognition (AR) using smartphone inertial sensors based on information theory based feature ranking algorithm and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments with a publicly available database 1 of human activity with smart phone inertial sensors show that the proposed approach can indeed lead to development of intelligent and automatic real time human activity monitoring for eHealth application scenarios for elderly, disabled and people with special needs. [ABSTRACT FROM AUTHOR]

During the development of object-oriented software system; huge amount of software-development data especially change-history is stored in software-repositories. This change-history can be mined to measure the quality of the software systems. In this paper, we present change-history based package-changeability measures namely; a) Package Change-Proximity, b) Change-Coupling Index and c) Package-Changeability. These measures are defined and computed on the basis of change-coupling among packages. A case study has been performed and findings derived from the results are discussed. Our results show that these package-changeability measures can be very helpful for development team to predict and quantify the change-coupling among packages. [ABSTRACT FROM AUTHOR]

In this paper, we propose energy efficient big data mining scheme for forest cover type and gas drift classification. Efficient machine learning and data mining techniques provide unprecedented opportunity to monitor and characterize physical environments, such as forest cover type, using low cost wireless sensor networks. The experimental validation on two different sensor network datasets, forest cover type and gas sensor array drift dataset from publicly available UCI machine learning repository. Coupled with an appropriate feature selection, the complete scheme leads towards an energy efficient protocol for intelligent monitoring of large physical environments instrumented with wireless sensor networks. [ABSTRACT FROM AUTHOR]

Implementation of data mining algorithms with low cost is one of the challenging tasks in the present world of massively increasing data. The key idea of this paper is to utilize the functionalities of Mathematica which is freely accessible on Raspberry Pi for the purpose of implementing Multi-label classification algorithm with low cost. With the facilities available in Mathematica software for Raspberry Pi, the line of code required for implementing data mining algorithms can be reduced sufficiently. Use of Random Kitchen Sink algorithm improves the accuracy of Multi-label classification and brings improvement in terms of memory usage for large dataset. [ABSTRACT FROM AUTHOR]

• This paper aims to assess books' depth and breadth via mining tables' of contents. • Books' depth and breadth values present an approximate linear relationship. • Books' tables of contents could be a helpful reference for evaluating books' wider impacts. • Multi-level content mining is conducted in this paper. Compared with journal articles, books can provide broader, deeper and more comprehensive information, and often have higher expertise and academic depth. However, most researches on book assessment focus on measuring academic value of books (e.g. citations analysis) or identifying attitudes of readers (e.g. book review mining), depth and breadth reflected by book contents is neglected. Therefore, in this paper, we measure books' depth and breadth by mining books' tables of contents, so as to enrich resources and methods for book assessment research, help users understand book contents quickly and improve efficiency of book selection. Specifically, we measured books' depth and breadth based on books' tables of contents via two levels: topic level and feature level. Firstly, we obtained topic-level metrics by identifying topics expressed in tables of contents and calculating topic distributions. Then, we got feature-level results via feature extraction and feature distribution calculation. Finally, we compared depth and breadth metrics and other book assessment metrics. Experimental results reveal that, books' depth and breadth at two levels are different, and substantial differences between disciplines and book types are obvious. In addition, books' depth and breadth can provide alternative and supplementary information for assessing multi-dimensional values of books. [ABSTRACT FROM AUTHOR]

Qualitative research in health provides insight into the experiences, perceptions and interactions of clients, caregivers, health professionals and the broader community. In this paper, the use of ethnography is discussed as a qualitative research technique to facilitate the understanding of the practice of speech-language pathology in different cultural and linguistic contexts around the world. A description is provided of the different types of data collection methods that can be employed in ethnographic research (such as observation, interviews, photography, video-recording, and personal reflection) and their usefulness in facilitating understanding of complex practice situations. Important considerations for designing and undertaking ethical and culturally appropriate qualitative research are explored and the benefits of qualitative research to the speech-language pathology profession are discussed. [ABSTRACT FROM AUTHOR]

Abstract: The number of semi-structured documents that is produced is steadily increasing. Thus, it will be essential for discovering new knowledge from them. In this survey paper, we review popular semi-structured documents mining approaches (structure alone and both structure and content). We provide a brief description of each technique as well as efficient algorithms for implementing the technique and comparing them using different comparison criteria. [Copyright &y& Elsevier]

With the increasing cultural and linguistic diversity of speech-language pathologists' caseloads, there is a pressing need for assessments that enable accurate and authentic evaluations of the communication skills of children from diverse backgrounds. Language sample analysis (LSA) has many properties that make it an effective tool for the comprehensive evaluation of culturally and linguistically diverse (CALD) children's expressive language skills. Using LSA allows the clinician to assess language skills within naturalistic discourse, and as such, is more suitable for CALD children than most decontextualised norm-roferenced assessments. Furthermore, LSA provides rich descriptive data and can be used within a dynamic assessment protocol to assist with the accurate identification of CALD children with language impairments. The goal of this paper is to summarise the complex issues that arise when completing LSA with paediatric CALD clients and describe how technological advances in computerised LSA have improved the accuracy and efficiency of the process. [ABSTRACT FROM AUTHOR]

Introduction: The social inclusion of individuals with mental health problems is an issue for mental health services, for the individuals who experience stigma, discrimination and exclusion, and for society at large. To develop community-orientated services that are capable of promoting inclusion it will, therefore, be advantageous to all parties to understand what service users find most helpful. Method: A 2-year action research project explored the recovery journeys of a group of assertive outreach service users who had progressed from being socially excluded and occupationally deprived to being participants in their local communities. The research aimed to understand how these outcomes were produced and to use this knowledge to inform local service development. Findings: This paper focuses on eight qualitative interviews, where service users recounted their stories of community participation and inclusion. The findings show how assertive outreach practitioners harnessed occupation as a basis for building relationships between practitioners and service users, and how this became a conduit towards participation in the mainstream community. Conclusion: Facilitating engagement in community-based occupations through creative collaboration helped participants reconnect with cherished roles, achieve long-standing goals and develop feelings of self-efficacy, belonging and wellbeing. [ABSTRACT FROM AUTHOR]

Preserving the confidentiality of sensitive data, while permitting knowledge discovery, is an important goal in privacy-preserving data mining. This paper investigates the effectiveness of data shuffling for classification tree and regression analysis. We compare the effectiveness of data shuffling to the tree based data perturbation method which was developed specifically for the purpose of data mining. Results suggest that data shuffling provides the higher levels of data security and more effectively preserves data mining knowledge than tree based data perturbation method. [ABSTRACT FROM AUTHOR]

Abstract: Existing meta-search engines return web search results based on the page relevancy to the query, their popularity and content. It is necessary to provide a meta-search engine capable of ranking results considering the user’s field of interest. Social networks can be useful to find the users’ tendencies, favorites, skills, and interests. In this paper we propose MSE, a meta-search engine for document retrieval utilizing social information of the user. In this approach, each user is assumed to have a profile containing his fields of interest. MSE extracts main phrases from the title and short description of receiving results from underlying search engines. Then it clusters the main phrases by a Self-Organizing Map neural network. Generated clusters are then ranked on the basis of the user’s field of interest. We have compared the proposed MSE against two other meta-search engines. The experimental results show the efficiency and effectiveness of the proposed method. [Copyright &y& Elsevier]

Occupational therapists working in higher education have acknowledged the benefits of peer observation to their professional development and there has been discussion of its potential benefits in clinical settings. However, there is limited literature and no practical guidance on the implementation of observed practice within clinical practice. This action research study sought to develop a work-based observed practice programme for occupational therapists and physiotherapists in a United Kingdom mental health trust. Action research was chosen to enable the positive engagement of practitioners in order to co-create knowledge and to improve practice. Occupational therapists and physiotherapists with expertise of local practice and personal experience of staff development and clinical supervision formed a working group to develop and implement the programme. The group considered examples of similar programmes from higher education and addressed issues relating to the purpose, status and process of the programme. It also explored its relationship to existing preceptorship programmes and clinical supervision. This paper summarises the key steps of the observed practice process and the development of supporting documentation created by the working group. The observed practice programme was implemented at workshops in the autumn of 2009 to be followed by a formal evaluation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue–saturation–value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall. [Copyright &y& Elsevier]

Web content mining is one of the categories of web mining which still has the bottleneck of word sense ambiguity. Sense ambiguity drastically affects the performance of web content mining. Web is a heterogeneous source of information which is unstructured in nature. Relevant information retrieval from the web is a big problem. This paper performs a comprehensive review of the major approaches which are used to remove sense ambiguity and improve the performance of web content mining. It mainly focuses on the methodology and success rate of the various approaches. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a model for separator detection using the Delaunay triangulation and a common sampling algorithm based on a triangular filter. By using a sampling algorithm, the blur effect is created and together with the output triangles, obtained from the modified Delaunay algorithm, the separators can be determined after processing. [ABSTRACT FROM AUTHOR]

Innovative approaches to searching for hidden meaning in data have recently emerged. In some cases, however, the support of privacy is compromised and consistent privacy protection remains elusive. Privacy perceptions and requirements differ from person to person and over time, leading to diverse privacy preferences within a community. This paper reports on a privacy-enhancing database architecture that is premised upon this view of privacy and on Australia's Use and Disclosure National Privacy Principle. The approach extends to supporting privacy under Knowledge Discovery and Data Mining. [ABSTRACT FROM AUTHOR]

• An integrated attribute-satisfaction model is introduced to optimize service offerings based on opinion mining user-generated content. • The attribute impact-asymmetry, impact-range, and sentiment-performances of service attributes are jointly visualized in a 3D grid. • Novel understanding of customers' service assessments is gained, leading to attribute prioritization and specific recommendations for optimizing future service offerings. Researchers refer to various theories to investigate the distinct relationships between importance, performance, and the (a)symmetric impact of service attributes on customer satisfaction (CS). However, a fully integrated model that would allow practitioners to automatically execute analyses to optimize their service offerings in a competitive landscape is missing. Previous studies widely rely on importance/performance ratings of predefined service attributes retrieved from closed-ended questionnaires, which can hardly capture the competitive landscape from the customers' perspective. This paper introduces a novel asymmetric impact-sentiment-performance analysis (AISPA) to address these gaps by performing automated opinion mining on online reviews. Customers' evaluations of three hotel chains serve as an example application. The impact-asymmetry of the hotel service attributes on CS, the attribute impact and performance are jointly visualized in a 3D grid. An elaborate understanding of service assessments is gained, leading to attribute prioritization and specific recommendations for optimizing future offerings. [ABSTRACT FROM AUTHOR]

Background: The Manitoba Health Registry does not fully identify First Nations Manitobans, impacting the ability to adequately describe their health status and use of health services using this data source alone. This paper describes the processes in producing a valid database for use in a population-based report by the Manitoba Centre for Health Policy (MCHP).Methods: The Indian Registry's Status Verification System (SVS) file is a national database containing a complete list of Registered First Nations eligible for benefits through the Indian Act. Through negotiations with the Assembly of Manitoba Chiefs' Health Information Research Committee, Indian and Northern Affairs Canada, FNIHB, Manitoba Health, and MCHP, a linkage of the SVS files and Manitoba Health's Registry was accomplished. Of the 116,177 SVS records and 5,803 deceased records, 97,635 individuals linked to the Manitoba Health Registry.Results: There was a 99% match on gender, 70% match on surname, 94% match on given name, and 96% match on birth year. The total represents a 20% decrease in records from the Indian Registry. The decrease was greater for females, older people and those from southern areas.Conclusion: The linkage resulted in a 20% increase over Manitoba Health data alone. Our inability to link all of the records may be due to several factors. Individuals with a Manitoba Band affiliation living outside of the province could not be linked to the Manitoba Health Registry. First Nations living in Manitoba but affiliated with a non-Manitoba Band would not have been in the file obtained. Finally, births, deaths and surname change after marriage may be under-reported to the Indian Registry. This linkage enabled MCHP to provide a more accurate picture of First Nations health status and use of health care services than otherwise would have been available. Ongoing linkages with Manitoba Health data, as well as similar linkages elsewhere in Canada, are encouraged. [ABSTRACT FROM AUTHOR]