A Holographic Future: Comparing an AR headset to traditional construction methods

<p>New Zealand’s construction industry has seen a profound uptake in the use of Building Information Modelling (BIM) in recent years. BIM has proven to be beneficial to individuals during moments of the lifecycle of a building, but it has yet to play a significant role in the actual construction stage of a project. In recent years, researchers have become increasingly interested in the use of Augmented Reality (AR) to provide support for BIM implementation and productivity on-site. However, current research has yet to prove the effectiveness of integrating the information from the BIM model into an AR environment. With international AR applications emerging and the improvement on AR and BIM software, it has now become feasible to test the integration of these two technologies dynamically. This paper utilises recent developments in technology to provide a comparison of the effectiveness of information retrieval methods. A three-phase, mixed method experiment was conducted and evaluated over a one-year time frame in Wellington, New Zealand. By using a mixed method approach, the research gained multiple forms of data drawing on all possibilities. One phase involved a focus group with a variety of construction industry professionals exploring the use of BIM and how their teams work together to solve problems and tasks on-site. The second phase had twenty-four construction industry tradesmen randomly assigned to three control groups to complete clash detection tasks using different visualisation mediums. The control groups used either two-dimensional paper drawings, a BIM model on a laptop, or a BIM model in a Microsoft HoloLens to complete the information retrieval tasks. Following the task-based experiment, the control groups participated in a focus group to understand tradesmen’s perceptions of the different visualisation mediums and how current processes could be improved for their understanding. Each group was assessed on a usability framework model of effectiveness, efficiency and satisfaction. Based on the results of the experiments and focus groups, this research can produce evidence for determining the most effective methods for information retrieval and clash detection on-site. Can AR provide a more powerful system for construction productivity and information retrieval than paper or computer-based systems? The research does not provide a detailed solution but instead demonstrates the potential marriage between AR and BIM technologies to help evolve future building processes.</p>

A Holographic Future: Comparing an AR headset to traditional construction methods

<p>New Zealand’s construction industry has seen a profound uptake in the use of Building Information Modelling (BIM) in recent years. BIM has proven to be beneficial to individuals during moments of the lifecycle of a building, but it has yet to play a significant role in the actual construction stage of a project. In recent years, researchers have become increasingly interested in the use of Augmented Reality (AR) to provide support for BIM implementation and productivity on-site. However, current research has yet to prove the effectiveness of integrating the information from the BIM model into an AR environment. With international AR applications emerging and the improvement on AR and BIM software, it has now become feasible to test the integration of these two technologies dynamically. This paper utilises recent developments in technology to provide a comparison of the effectiveness of information retrieval methods. A three-phase, mixed method experiment was conducted and evaluated over a one-year time frame in Wellington, New Zealand. By using a mixed method approach, the research gained multiple forms of data drawing on all possibilities. One phase involved a focus group with a variety of construction industry professionals exploring the use of BIM and how their teams work together to solve problems and tasks on-site. The second phase had twenty-four construction industry tradesmen randomly assigned to three control groups to complete clash detection tasks using different visualisation mediums. The control groups used either two-dimensional paper drawings, a BIM model on a laptop, or a BIM model in a Microsoft HoloLens to complete the information retrieval tasks. Following the task-based experiment, the control groups participated in a focus group to understand tradesmen’s perceptions of the different visualisation mediums and how current processes could be improved for their understanding. Each group was assessed on a usability framework model of effectiveness, efficiency and satisfaction. Based on the results of the experiments and focus groups, this research can produce evidence for determining the most effective methods for information retrieval and clash detection on-site. Can AR provide a more powerful system for construction productivity and information retrieval than paper or computer-based systems? The research does not provide a detailed solution but instead demonstrates the potential marriage between AR and BIM technologies to help evolve future building processes.</p>

Human Evaluation Experiment of Legal Information Retrieval Methods

In this article, I present the results of the human evaluation experiment of three commonly used methods in legal information retrieval and a new “multilayered” approach. I use the doc2vec model, citation network analysis and two topic modelling algorithms for the Czech Supreme Court decisions retrieval and evaluate their performance. To improve the accuracy of the results of these methods, I combine the methods in a “multilayered” way and perform the subsequent evaluation. Both evaluation experiments are conducted with a group of legal experts to assess the applicability and usability of the methods for legal information retrieval. The combination of the doc2vec and citations is found satisfactory accurate for practical use for the Czech court decisions retrieval.

Information retrieval against the human right to privacy

In the era of digital transformation, the main human right to be protected on the Internet appears to be the right to privacy. Human rights are breached not only by the governments and military forces, but also by the international private corporations. The rapid development of the Information Retrieval methods with the Machine Learning techniques and unrestrained access to personal data gives global potentates access to automatic processing of personal Big Data. In the article there are discussed the vital problems of the privacy of the humanity, the need for international regulations for this human right enforcement and the reflections over uninhibited, technical expansion without ethical boundaries.

An Information Retrieval Pipeline for Legislative Documents from the Brazilian Chamber of Deputies

This work investigates information retrieval methods to address the existing difficulties on the Preliminary Search, part of the law making process from the Brazilian Chamber of Deputies. For such, different preprocessing approaches, stemmers, language models, and BM25 variants were compared. Two legislative corpora from Chamber were used to build and validate the pipeline. All texts were converted to lowercase and had stopwords, accentuation, and punctuation removed. Words were represented by their stem combined with word unigram and bigram language models. Retrieving the bill that was originated from a specific job request, the BM25L with Savoy stemmer reached a R@20 of 0.7356. After removing queries with inconsistencies or which made reference exclusively to attachments, to other job requests, or to bills, the R@20 increased to 0.94.

Classical and Probabilistic Information Retrieval Techniques: An Audit

Information retrieval is acquiring particular information from large resources and presenting it according to the user’s need. The incredible increase in information resources on the Internet formulates the information retrieval procedure, a monotonous and complicated task for users. Due to over access of information, better methodology is required to retrieve the most appropriate information from different sources. The most important information retrieval methods include the probabilistic, fuzzy set, vector space, and boolean models. Each of these models usually are used for evaluating the connection between the question and the retrievable documents. These methods are based on the keyword and use lists of keywords to evaluate the information material. In this paper, we present a survey of these models so that their working methodology and limitations are discussed. This is an important understanding because it makes possible to select an information retrieval technique based on the basic requirements. The survey results showed that the existing model for knowledge recovery is somewhere short of what was planned. We have also discussed different areas of IR application where these models could be used.

A dependency-based machine learning approach to the identification of research topics: a case in COVID-19 studies

PurposePrevious research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics.Design/methodology/approachFirst, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity.FindingsThe new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f-measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results.Originality/valueThis study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.

Classification of micropollutants sources as components of road surface runoff pollution

Introduction. The current state and the development of road construction and infrastructure is the cause of increasing the environmental pollution, especially in close proximity to the roads.Problem Statement. Surface runoff from the roads is a significant volume of polluted water which most often enters water bodies and the surrounding area without treatment, which is contrary to environmental requirements. Not only accidental leaks of oil and chloride residues present in runoff, but also a significant volume of suspended solids, which are often settled and accumulated and are dangerous for the environment and public health. In order to predict measures or prevent such pollution and accumulation, it is necessary to determine the sources of suspended solids, qualitative characteristics, physical and chemical properties of pollutants.Purpose. The purpose of the work is to determine of suspended solids pollutants (micropollutants), their components, to develop a classification of sources and their formation.Materials and methods. Analytical, information retrieval methods were used for this study.Results. As a result of the work, classification, which presents potentially possible types of micropollutants which are the components of suspended solids found in road surface runoff, was developed.Conclusions. The developed classification of micropollutants sources is the initial stage to study such kind of pollution, migration in the environment, the impacts of accumulation and decomposition products, as well as to develop measures for reducing the formation and dissemination of micropollutants in the environment.Keywords: road pavement, micropollutants, composition, particles.

Peer review of searches for studies for health technology assessments, systematic reviews, and other evidence syntheses

Introduction Peer review of searches is a process whereby both the search strategies and the search process description are reviewed, ideally using an evidence-based checklist. Rationale As the search strategy underpins any well-conducted evidence synthesis, its quality could affect the final result. Evidence shows, however, that search strategies are prone to error. Findings There is increasing awareness and use of the PRESS Evidence-Based Checklist and peer review of search strategies, at the outset of evidence syntheses, prior to the searches being run, and this is now recommended by a number of evidence synthesis organizations. Recommendations and conclusions Searches for evidence syntheses should be peer reviewed by a suitably qualified and experienced librarian or information specialist after being designed, ideally, by another suitably qualified and experienced librarian or information specialist. Peer review of searches should take place at two important stages in the evidence synthesis process; at the outset of the project prior to the searches being run and at the prepublication stage. There is little empirical evidence, however, to support the effectiveness of peer review of searches. Further research is required to assess this. Those wishing to stay up to date with the latest developments in information retrieval, including peer review of searches, should consult the SuRe Info resource (http://www.sure-info.org), which seeks to help information specialists and others by providing easy access to the findings from current information retrieval methods research and thus support more research-based information retrieval practice.