Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Management: A continuing literature survey with indexes, March 1976

Management is a compilation of references to selected reports, journal articles, and other documents on the subject of management. This publication lists 368 documents originally announced in the 1975 issues of Scientific and Technical Aerospace Reports (STAR) or International Aerospace Abstracts (IAA). It includes references on the management of research and development, contracts, production, logistics, personnel, safety, reliability and quality control. It also includes references on: program, project and systems management; management policy, philosophy, tools, and techniques; decisionmaking processes for managers; technology assessment; management of urban problems; and information for managers on Federal resources, expenditures, financing, and budgeting

The design of accessible, usable and meaningful content. Volume 1: Explanatory Essay

This Explanatory Essay discusses the 31 papers which I have authored, or made a substantial contribution to, and submitted for a PhD by Prior Publication. The Essay presents these publications in the light of their original contribution to an emerging theme of concern, Content Design, which I will argue is the deliberate design of content so that it is accessible, usable and meaningful. Content is any type of information carrying material that is produced in any medium or mixture of media, for human, as opposed to machine, consumption. As such, content has always played an important role in our lives. In the Information Age, however, the importance of this role is becoming critical. This may be attributed to many factors, including: the inexorable proliferation of digitally produced content of all types; the increased possibilities, even expectations, to interact with content; and our growing reliance upon information. Thus, there should be a renewed attention to design of content, particularly its accessibility, usability and meaningfulness. There are many research areas that deal with aspects of content. I believe that deliberate attention to the composition and structuring of content can benefit from all of these. Content Design represents a multifaceted 'problem space' that draws on a wide variety of disciplines, from the humanities to the sciences. It also has lessons to learn from traditional ways of meaning-making, particularly literary studies and rhetoric. This problem space is a place to pull together knowledge and expertise that is needed in the digital age to help to design content so that it is consumable by humans. In this Essay, my publications are situated within three strands of research that offer such knowledge and expertise: Discourse Studies; the Uses of Metadata; and the Accessibility of Content. Broadly speaking, my work contributes, within these strands, to the design of content in terms of composing, packaging and making content apprehendable

The effect of institutional perspective on safety climate through a mediating role of governance practice

Bangladesh has encountered several ready-made garments manufacturing (RMG) industry disasters leading to the loss of valuable human lives due to the factory owner’s tendency to cut corners on safety. In 2013, the safety issue came into the limelight when Rana Plaza took less than 90 seconds to crumble with the workers inside, killing more than 1,100 and injuring more than 2,500 people. The sudden breakdown of Rana Plaza fetched home the much-needed attention, persuading different institutions to offer corrective steps which can rectify hazardous situations in thousands of factories. Nonetheless, irrespective of various institutions’ making effort to improve the safety situation, much remains to be done to ensure industrial safety behaviour and compliance. Recently, Accord Bangladesh Quarterly Report (2018: 3) acknowledged that while making an improvement, “major life-threatening safety concerns remain outstanding in too many factories and need to be fixed urgently”. Hence, crucial questions need to be explored: To what extent do institutional perspectives improve organisational safety behaviours? Whether governance mechanism can force organisations to commit and ensure workers safety? While a considerable attention has been paid to the institutional perspectives, existing literature is fragmented and disconnected with safety climate and performance measures. Therefore, this study examines institutional impacts on changing organisational safety climate and its performance, through the mediating role of governance practice. The survey results of 256 RMG workers from128 garments factories in Bangladesh with a usable response rate of 72.31% and satisfactory indices (e.g. Chi-square x2/df=1.620, RMR=.012, SRMR=.051, RMSEA=.049, CFI=.982, IFI=.983) demonstrate each component of safety climate is significantly associated with at least two institutional perspectives. This study suggests that regulations and laws only provide procedural instructions and guidance rather than definitive protocols. While norms and culturally established standards are decisive to the establishment of safety practices. Additionally, making organisations more accountable and/or obedient towards lawful practices can guarantee management’s commitment to safety and create a compulsion to pledge safety practices. Furthermore, accountable and ethical organisational behaviours motivate workers to actively participate in safety activities that ultimately result in fewer accidents and injuries. Interestingly, the study found that culturally established norm of safety is perceived as taken-for-granted by the workers, which refrain them from participating in voluntary safety activities. In general, establishing organisational safety climate can be considered as a socialised activity that is much contingent on the institutional pressures to comply with specific requirements and the organisational intention to uphold their legitimacy. The findings shed light on the way in which different types of institutional influence could be better exercised to facilitate safety improvement; reconditioning and reinforcing government policy can resolve sporadic safety climate level of the industry. While the study has gone some way towards enhancing our understanding, it also arises several questions that need further investigations. Finally, further research is needed to determine the impact of improvement mechanisms on workplace safety performance, such as how workplace design, safety training programmes, and institutional enforcement policies protect the well-being of workers

AICPA audit and accounting manual as of July 1, 2007 : nonauthoritative technical practice aid

https://egrove.olemiss.edu/aicpa_guides/1978/thumbnail.jp

Management: A continuing literature survey with indexes, March 1975

A special bibliography listing 1,064 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in 1974 is presented