61,019 research outputs found
Real or not? Identifying untrustworthy news websites using third-party partnerships
Untrustworthy content such as fake news and clickbait have become a pervasive problem on the Internet, causing significant socio-political problems around the world. Identifying untrustworthy content is a crucial step in countering them. The current best-practices for identification involve content analysis and arduous fact-checking of the content. To complement content analysis, we propose examining websites? third-parties to identify their trustworthiness. Websites utilize third-parties, also known as their digital supply chains, to create and present content and help the website function. Third-parties are an important indication of a website?s business model. Similar websites exhibit similarities in the third-parties they use. Using this perspective, we use machine learning and heuristic methods to discern similarities and dissimilarities in third-party usage, which we use to predict trustworthiness of websites. We demonstrate the effectiveness and robustness of our approach in predicting trustworthiness of websites from a database of News, Fake News, and Clickbait websites. Our approach can be easily and cost-effectively implemented to reinforce current identification methods
Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels
Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft
Systems and certification issues for civil transport aircraft flow control systems
This article is placed here with permission from the Royal Aeronautical Society - Copyright @ 2009 Royal Aeronautical SocietyThe use of flow control (FC) technology on civil transport aircraft is seen as a potential means of providing a step change in aerodynamic performance in the 2020 time frame. There has been extensive research into the flow physics associated with FC. This paper focuses on developing an understanding of the costs and design drivers associated with the systems needed and certification. The research method adopted is based on three research strands: 1. Study of the historical development of other disruptive technologies for civil transport aircraft, 2. Analysis of the impact of legal and commercial requirements, and 3. Technological foresight based on technology trends for aircraft currently under development. Fly by wire and composite materials are identified as two historical examples of successful implementation of disruptive new technology. Both took decades to develop, and were initially developed for military markets. The most widely studied technology similar to FC is identified as laminar flow control. Despite more than six decades of research and arguably successful operational demonstration in the 1990s this has not been successfully transitioned to commercial products. Significant future challenges are identified in cost effective provision of the additional systems required for environmental protection and in service monitoring of FC systems particularly where multiple distributed actuators are envisaged. FC generated noise is also seen as a significant challenge. Additional complexity introduced by FC systems must also be balanced by the commercial imperative of dispatch reliability, which may impose more stringent constraints than legal (certification) requirements. It is proposed that a key driver for future successful application of FC is the likely availability of significant electrical power generation on 787 aircraft forwards. This increases the competitiveness of electrically driven FC systems compared with those using engine bleed air. At the current rate of progress it is unlikely FC will make a contribution to the next generation of single-aisle aircraft due to enter service in 2015. In the longer term, there needs to be significant movement across a broad range of systems technologies before the aerodynamic benefits of FC can be exploited.This work is supported by the EU FP6 AVERT (AerodynamicValidation of Emissions Reducing Technologies) project
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach
We examine counterfactual explanations for explaining the decisions made by
model-based AI systems. The counterfactual approach we consider defines an
explanation as a set of the system's data inputs that causally drives the
decision (i.e., changing the inputs in the set changes the decision) and is
irreducible (i.e., changing any subset of the inputs does not change the
decision). We (1) demonstrate how this framework may be used to provide
explanations for decisions made by general, data-driven AI systems that may
incorporate features with arbitrary data types and multiple predictive models,
and (2) propose a heuristic procedure to find the most useful explanations
depending on the context. We then contrast counterfactual explanations with
methods that explain model predictions by weighting features according to their
importance (e.g., SHAP, LIME) and present two fundamental reasons why we should
carefully consider whether importance-weight explanations are well-suited to
explain system decisions. Specifically, we show that (i) features that have a
large importance weight for a model prediction may not affect the corresponding
decision, and (ii) importance weights are insufficient to communicate whether
and how features influence decisions. We demonstrate this with several concise
examples and three detailed case studies that compare the counterfactual
approach with SHAP to illustrate various conditions under which counterfactual
explanations explain data-driven decisions better than importance weights
Weak signal identification with semantic web mining
We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization's environment and that they appear in different contexts. We use internet information to represent organization's environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time
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Human resource management in India: strategy, performance and complementarity
This study seeks to explore which types of HR practice are associated with better organisational performance (OP). Whilst the core finding—that specific HR practices lead to better organisational outcomes may not be surprising—we also found an absence of complementarity. Normally, the absence of complementarities would suggest limitations in institutional supports; on the one hand, however, institutional shortfalls are not unique to India and may be encountered in many emerging market settings. In contrast, the great internal diversity of the Indian setting, with strong variations recognised amongst institutions, along with enforcement capabilities, might suggest that these tendencies are particularly pronounced. We also found a strong link between the intrinsic rewards and performance—an unexpected result in a low-income country, where wages are generally low. We suggest that this may reflect the nature of the labour market and the limited (and possibly proportionately shrinking) pool of good jobs, making exit a difficult option for all but the best qualified. Whilst this puts employees in a poor bargaining position in bidding-up pay (making pay rises seem unfeasible), the intrinsic attributes of the job become more important
Modelling the impacts of agricultural management practices on river water quality in Eastern England
Agricultural diffuse water pollution remains a notable global pressure on water quality, posing risks to aquatic ecosystems, human health and water resources and as a result legislation has been introduced in many parts of the world to protect water bodies. Due to their efficiency and cost-effectiveness, water quality models have been increasingly applied to catchments as Decision Support Tools (DSTs) to identify mitigation options that can be introduced to reduce agricultural diffuse water pollution and improve water quality. In this study, the Soil and Water Assessment Tool (SWAT) was applied to the River Wensum catchment in eastern England with the aim of quantifying the long-term impacts of potential changes to agricultural management practices on river water quality. Calibration and validation were successfully performed at a daily time-step against observations of discharge, nitrate and total phosphorus obtained from high-frequency water quality monitoring within the Blackwater sub-catchment, covering an area of 19.6 km2. A variety of mitigation options were identified and modelled, both singly and in combination, and their long-term effects on nitrate and total phosphorus losses were quantified together with the 95% uncertainty range of model predictions. Results showed that introducing a red clover cover crop to the crop rotation scheme applied within the catchment reduced nitrate losses by 19.6%. Buffer strips of 2 m and 6 m width represented the most effective options to reduce total phosphorus losses, achieving reductions of 12.2% and 16.9%, respectively. This is one of the first studies to quantify the impacts of agricultural mitigation options on long-term water quality for nitrate and total phosphorus at a daily resolution, in addition to providing an estimate of the uncertainties of those impacts. The results highlighted the need to consider multiple pollutants, the degree of uncertainty associated with model predictions and the risk of unintended pollutant impacts when evaluating the effectiveness of mitigation options, and showed that high-frequency water quality datasets can be applied to robustly calibrate water quality models, creating DSTs that are more effective and reliable
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