The integration of Building Information Modeling (BIM) and Geographic Information System (GIS) has been identified as a promising but challenging topic to transform information towards the generation ...of knowledge and intelligence. Achievement of integrating these two concepts and enabling technologies will have a significant impact on solving problems in the civil, building and infrastructure sectors. However, since GIS and BIM were originally developed for different purposes, numerous challenges are being encountered for the integration. To better understand these two different domains, this paper reviews the development and dissimilarities of GIS and BIM, the existing integration methods, and investigates their potential in various applications. This study shows that the integration methods are developed for various reasons and aim to solve different problems. The parameters influencing the choice can be summarized and named as “EEEF” criteria: effectiveness, extensibility, effort, and flexibility. Compared with other methods, semantic web technologies provide a promising and generalized integration solution. However, the biggest challenges of this method are the large efforts required at early stage and the isolated development of ontologies within one particular domain. The isolation problem also applies to other methods. Therefore, openness is the key of the success of BIM and GIS integration.
Attacks against computer systems can cause considerable economic or physical damage. High-quality development of security-critical systems is difficult, mainly because of the conflict between ...development costs and verifiable correctness. Jürjens presents the UML extension UMLsec for secure systems development. It uses the standard UML extension mechanisms, and can be employed to evaluate UML specifications for vulnerabilities using a formal semantics of a simplified fragment of UML. Established rules of security engineering can be encapsulated and hence made available even to developers who are not specialists in security. As one example, Jürjens uncovers a flaw in the Common Electronic Purse Specification, and proposes and verifies a correction. With a clear separation between the general description of his approach and its mathematical foundations, the book is ideally suited both for researchers and graduate students in UML or formal methods and security, and for advanced professionals writing critical applications. Written for:Researchers, advanced professionals, graduatesKeywords:System DesignSystem DevelopmentSystem SecurityUMLUMLsec
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Recommendation system concentrates on quickly matching products to consumer’s needs which plays a major role in improving user experiences and increase conversion rate. Travel recommendation has ...become a hot topic in both industry and academia with the development of the tourism industry. Nevertheless, the selection of travel products entails careful consideration of various geographical factors, such as departure and destination. Meanwhile, due to the limitation of finance and time, users browse and purchase travel products less frequently than they do for traditional products, which leads to data sparsity problem in representation learning. To solve these challenges, a novel model named GHGCL (short for
G
eography-aware
H
eterogeneous
G
raph
C
ontrastive
L
earning) is proposed for recommending travel products. Concretely, we model the travel recommender system as an heterogeneous information network with geographical information, and capture diverse user preferences from local and high-order structures. Especially, we design two kinds of contrastive learning tasks for better user and travel product representation learning. The multi-view contrastive learning aims to bridge the gap between network schema and meta-path view representations. The meta-path contrastive learning focuses on modeling the coarse-grained commonality between different meta-paths from the perspective of different geographical factors,
i.e.,
departure and destination. We assess the performance of GHGCL by performing a series of experiments on a real-world dataset and the results clearly verify its superiority as compared to the baseline methods.
Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating ...experience. Alternatively, systems can improve user satisfaction by proactively asking questions of the users to clarify their information needs. Asking clarifying questions is especially important in conversational systems since they can only return a limited number of (often only one) result(s).
In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems. To this end, we propose an offline evaluation methodology for the task and collect a dataset, called Qulac, through crowdsourcing. Our dataset is built on top of the TREC Web Track 2009-2012 data and consists of over 10K question-answer pairs for 198 TREC topics with 762 facets. Our experiments on an oracle model demonstrate that asking only one good question leads to over 170% retrieval performance improvement in terms of P@1, which clearly demonstrates the potential impact of the task. We further propose a retrieval framework consisting of three components: question retrieval, question selection, and document retrieval. In particular, our question selection model takes into account the original query and previous question-answer interactions while selecting the next question. Our model significantly outperforms competitive baselines. To foster research in this area, we have made Qulac publicly available.
Introducing Knowledge Graph (KG) to facilitate recommender system has become a tendency in recent years. Many existing methods leverage KG to obtain side information of items to promote item ...representation learning for enhancing recommendation performance. However, they ignore that KG also may contribute to better user representation learning. To solve the above issue, we propose a novel algorithm, KIGR ( K nowledge-aware I nteraction G raph for R ecommendation), to mine user-item interactions via Knowledge Graph for assisting user representation learning. Specifically, a user-item interaction is encoded by attentively summing up the relation embedding about the item in KG. Then an unsupervised learning method is used to group the user-item interactions into different latent types. Further, a user-item interaction graph is divided into several subgraphs, which is referred to as Knowledge-aware Interaction Graph, making each subgraph only contains one latent type of interactions. Finally, user representation is the fusion of user interest embedding, which is learned on knowledge-aware interaction graph; While item representation is learned on KG. Experimental results on MovieLens, LastFM and Amazon-Book validate that the proposed KIGR has a superior performance compared with the SOTA algorithms.
Background
A well‐functioning routine health information system (RHIS) can provide the information needed for health system management, for governance, accountability, planning, policy making, ...surveillance and quality improvement, but poor information support has been identified as a major obstacle for improving health system management.
Objectives
To assess the effects of interventions to improve routine health information systems in terms of RHIS performance, and also, in terms of improved health system management performance, and improved patient and population health outcomes.
Search methods
We searched the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library, MEDLINE Ovid and Embase Ovid in May 2019. We searched Global Health, Ovid and PsycInfo in April 2016. In January 2020 we searched for grey literature in the Grey Literature Report and in OpenGrey, and for ongoing trials using the International Clinical Trials Registry Platform (ICTRP) and ClinicalTrials.gov. In October 2019 we also did a cited reference search using Web of Science, and a ‘similar articles’ search in PubMed.
Selection criteria
Randomised and non‐randomised trials, controlled before‐after studies and time‐series studies comparing routine health information system interventions, with controls, in primary, hospital or community health care settings. Participants included clinical staff and management, district management and community health workers using routine information systems.
Data collection and analysis
Two authors independently reviewed records to identify studies for inclusion, extracted data from the included studies and assessed the risk of bias. Interventions and outcomes were too varied across studies to allow for pooled risk analysis. We present a 'Summary of findings' table for each intervention comparisons broadly categorised into Technical and Organisational (or a combination), and report outcomes on data quality and service quality. We used the GRADE approach to assess the certainty of the evidence.
Main results
We included six studies: four cluster randomised trials and two controlled before‐after studies, from Africa and South America. Three studies evaluated technical interventions, one study evaluated an organisational intervention, and two studies evaluated a combination of technical and organisational interventions. Four studies reported on data quality and six studies reported on service quality.
In terms of data quality, a web‐based electronic TB laboratory information system probably reduces the length of time to reporting of TB test results, and probably reduces the overall rate of recording errors of TB test results, compared to a paper‐based system (moderate certainty evidence). We are uncertain about the effect of the electronic laboratory information system on the recording rate of serious (misidentification) errors for TB test results compared to a paper‐based system (very low certainty evidence). Misidentification errors are inaccuracies in transferring test results between an electronic register and patients' clinical charts. We are also uncertain about the effect of the intervention on service quality (timeliness of starting or changing a patient's TB treatment) (very low certainty evidence).
A hand‐held electronic device probably improves the length of time to report TB test results, and probably reduces the total frequency of recording errors in TB test results between the laboratory notebook and the electronic information record system, compared to a paper‐based system (moderate‐certainty evidence). We are, however, uncertain about the effect of the intervention on the frequency of serious (misidentification) errors in recording between the laboratory notebook and the electronic information record, compared to a paper‐based system (very low certainty evidence).
We are uncertain about the effect of a hospital electronic health information system on service quality (length of time outpatients spend at hospital, length of hospital stay, and hospital revenue collection), compared to a paper‐based system (very low certainty evidence).
High‐intensity brief text messaging (SMS) may make little or no difference to data quality (in terms of completeness of documentation of pregnancy outcomes), compared to low‐intensity brief text messaging (low‐certainty evidence).
We are uncertain about the effect of electronic drug stock notification (with either data management support or product transfer support) on service quality (in terms of transporting stock and stock levels), compared to paper‐based stock notification (very low certainty evidence).
We are uncertain about the effect of health information strengthening (where it is part of comprehensive service quality improvement intervention) on service quality (health worker motivation, receipt of training by health workers, health information index scores, quality of clinical observation of children and adults) (very low certainty evidence).
Authors' conclusions
The review indicates mixed effects of mainly technical interventions to improve data quality, with gaps in evidence on interventions aimed at enhancing data‐informed health system management. There is a gap in interventions studying information support beyond clinical management, such as for human resources, finances, drug supply and governance. We need to have a better understanding of the causal mechanisms by which information support may affect change in management decision‐making, to inform robust intervention design and evaluation methods.
Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of ...time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.
Privacy is one of the few concepts that has been studied across many disciplines, but is still difficult to grasp. The current understanding of privacy is largely fragmented and discipline-dependent. ...This study develops and tests a framework of information privacy and its correlates, the latter often being confused with or built into definitions of information privacy per se. Our framework development was based on the privacy theories of Westin and Altman, the economic view of the privacy calculus, and the identity management framework of Zwick and Dholakia. The dependent variable of the model is perceived information privacy. The particularly relevant correlates to information privacy are anonymity, secrecy, confidentiality, and control. We posit that the first three are tactics for information control; perceived information control and perceived risk are salient determinants of perceived information privacy; and perceived risk is a function of perceived benefits of information disclosure, information sensitivity, importance of information transparency, and regulatory expectations. The research model was empirically tested and validated in the Web 2.0 context, using a survey of Web 2.0 users. Our study enhances the theoretical understanding of information privacy and is useful for privacy advocates, and legal, management information systems, marketing, and social science scholars.
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This book provides a comprehensive discussion on urban growth and sprawl, and how they can be analyzed using remote sensing imageries. It compiles the views of numerous researchers that help in ...understanding a host of topics in urban growth and sprawl.
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In this essay, we outline some important concerns in the hope of improving the effectiveness of security and privacy research. We discuss the need to re-examine our understanding of information ...technology and information system (IS) artefacts and to expand the range of the latter to include those artificial phenomena that are crucial to information security and privacy research. We then briefly discuss some prevalent limitations in theory, methodology, and contributions that generally weaken security/privacy studies and jeopardise their chances of publication in a top IS journal. More importantly, we suggest remedies for these weaknesses, identifying specific improvements that can be made and offering a couple of illustrations of such improvements. In particular, we address the notion of loose re-contextualisation, using deterrence theory research as an example. We also provide an illustration of how the focus on intentions may have resulted in an underuse of powerful theories in security and privacy research, because such theories explain more than just intentions. We then outline three promising opportunities for IS research that should be particularly compelling to security and privacy researchers: online platforms, the Internet of things, and big data. All of these carry innate information security and privacy risks and vulnerabilities that can be addressed only by researching each link of the systems chain, that is, technologies-policies-processes-people-society-economy-legislature. We conclude by suggesting several specific opportunities for new research in these areas.
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