There remains some debate about whether beta power effects observed during sentence comprehension reflect ongoing syntactic unification operations (beta-syntax hypothesis), or instead reflect maintenance or updating of the sentence-level representation (beta-maintenance hypothesis). In this study, we used magnetoencephalography to investigate beta power neural dynamics while participants read relative clause sentences that were initially ambiguous between a subject- or an object-relative reading. An additional condition included a grammatical violation at the disambiguation point in the relative clause sentences. The beta-maintenance hypothesis predicts a decrease in beta power at the disambiguation point for unexpected (and less preferred) object-relative clause sentences and grammatical violations, as both signal a need to update the sentence-level representation. While the beta-syntax hypothesis also predicts a beta power decrease for grammatical violations due to a disruption of syntactic unification operations, it instead predicts an increase in beta power for the object-relative clause condition because syntactic unification at the point of disambiguation becomes more demanding. We observed decreased beta power for both the agreement violation and object-relative clause conditions in typical left hemisphere language regions, which provides compelling support for the beta-maintenance hypothesis. Mid-frontal theta power effects were also present for grammatical violations and object-relative clause sentences, suggesting that violations and unexpected sentence interpretations are registered as conflicts by the brain's domain-general error detection system.
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In a recent official statement, Google highlighted the negative effects of fake reviews on review websites and specifically requested companies not to buy and users not to accept payments to provide fake reviews (Google, 2019). Also, governmental authorities started acting against organisations that show to have a high number of fake reviews on their apps (DigitalTrends, 2018; Gov UK, 2020; ACM, 2017). However, while the phenomenon of fake reviews is well-known in industries as online journalism and business and travel portals, it remains a difficult challenge in software engineering (Martens & Maalej, 2019). Fake reviews threaten the reputation of an organisation and lead to a disvalued source to determine the public opinion about brands. Negative fake reviews can lead to confusion for customers and a loss of sales. Positive fake reviews might also lead to wrong insights about real users’ needs and requirements. Although fake reviews have been studied for a while now, there are only a limited number of spam detection models available for companies to protect their corporate reputation. Especially in times with the coronavirus, organisations need to put extra focus on online presence and limit the amount of negative input that affects their competitive position which can even lead to business loss. Given state-of-the-art derived features that can be engineered from review texts, a spam detector based on supervised machine learning is derived in an experiment that performs quite well on the well-known Amazon Mechanical Turk dataset.
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Frontline professionals such as social workers and civil servants play a crucial role in countering violent extremism.Because of their direct contac twith society,first liners are tasked with detecting individuals that may threaten national security and the democratic rule of law. Preliminary screening takes place during the pre-crime phase. However, without clear evidence or concrete indicators of unlawful action or physical violence, it is challenging to determine when someone poses a threat. There are no set patterns that can be used to identify cognitive radicalization processes that will result in violent extremism. Furthermore, prevention targets ideas and ideologies with no clear framework for assessing terrorism-risk. This article examines how civil servants responsible for public order, security and safety deal with their mandate to engage in early detection, and discusses the side effects that accompany this practice. Based on openinterviews with fifteen local security professionals in the Netherlands, we focus here on the risk assessments made by these professionals. To understand their performance, we used the following two research questions: First, what criteria do local security professionals use to determine whether or not someone forms a potential risk? Second, how do local security professionals substantiate their assessments of the radicalization processes that will develop into violent extremism? We conclude that such initial risk weightings rely strongly on ‘gut feelings’ or intuition. We conclude that this subjectivitymayleadto prejudiceand/oradministrativearbitrariness in relationtopreliminary risk assessment of particular youth.
‘Dieren in de dijk’ aims to address the issue of animal burrows in earthen levees, which compromise the integrity of flood protection systems in low-lying areas. Earthen levees attract animals that dig tunnels and cause damages, yet there is limited scientific knowledge on the extent of the problem and effective approaches to mitigate the risk. Recent experimental research has demonstrated the severe impact of animal burrows on levee safety, raising concerns among levee management authorities. The consortium's ambition is to provide levee managers with validated action perspectives for managing animal burrows, transitioning from a reactive to a proactive risk-based management approach. The objectives of the project include improving failure probability estimation in levee sections with animal burrows and enhancing risk mitigation capacity. This involves understanding animal behavior and failure processes, reviewing existing and testing new deterrence, detection, and monitoring approaches, and offering action perspectives for levee managers. Results will be integrated into an open-access wiki-platform for guidance of professionals and in education of the next generation. The project's methodology involves focus groups to review the state-of-the-art and set the scene for subsequent steps, fact-finding fieldwork to develop and evaluate risk reduction measures, modeling failure processes, and processing diverse quantitative and qualitative data. Progress workshops and collaboration with stakeholders will ensure relevant and supported solutions. By addressing the knowledge gaps and providing practical guidance, the project aims to enable levee managers to effectively manage animal burrows in levees, both during routine maintenance and high-water emergencies. With the increasing frequency of high river discharges and storm surges due to climate change, early detection and repair of animal burrows become even more crucial. The project's outcomes will contribute to a long-term vision of proactive risk-based management for levees, safeguarding the Netherlands and Belgium against flood risks.
Rotating machinery, such as centrifugal pumps, turbines, bearings, and other critical systems, is the backbone of various industrial processes. Their failures can lead to significant maintenance costs and downtime. To ensure their continuous operation, we propose a fault diagnosis and monitoring framework that leverages the innovative use of acoustic sensors for early fault detection, especially in components less accessible for traditional vibration-based monitoring strategies. The main objective of the proposed project is to develop a fault diagnosis and monitoring framework for rotating machinery, including the fusion of acoustic sensors and physics-based models. By combining real-time monitoring data from acoustic sensors with an understanding of first principles, the framework will enable maintenance practitioners to identify and categorize different failure modes such as wear, fatigue, cavitation, reduced flow, bearing damage, impeller damage, misalignment, etc. In the initial phase, the focus will be on centrifugal pumps using the existing test set-up at the University of Twente. Sorama specializes in acoustic sensors to locate noise sources and will provide acoustic cameras to capture sound patterns related to pump deterioration during various operating conditions. These acoustic signals will then be correlated with the different failure modes and mechanisms that will be described by physics-based models, such as wear, fatigue, cavitation, corrosion, etc. Furthermore, a recently published data set by the Dynamics Based Maintenance research group that includes vibration analysis data and motor current analysis data of various fault scenarios, such as mentioned above, will be used as validation. The anticipated outcome of this project is a versatile framework for a physics-informed acoustic monitoring system. This system is designed to enhance early fault detection significantly, reducing maintenance costs and downtime across a broad spectrum of industrial applications, from centrifugal pumps to turbines, bearings, and beyond.
Wildlife crime is an important driver of biodiversity loss and disrupts the social and economic activities of local communities. During the last decade, poaching of charismatic megafauna, such as elephant and rhino, has increased strongly, driving these species to the brink of extinction. Early detection of poachers will strengthen the necessary law enforcement of park rangers in their battle against poaching. Internationally, innovative, high tech solutions are sought after to prevent poaching, such as wireless sensor networks where animals function as sensors. Movement of individuals of widely abundant, non-threatened wildlife species, for example, can be remotely monitored ‘real time’ using GPS-sensors. Deviations in movement of these species can be used to indicate the presence of poachers and prevent poaching. However, the discriminative power of the present movement sensor networks is limited. Recent advancements in biosensors led to the development of instruments that can remotely measure animal behaviour and physiology. These biosensors contribute to the sensitivity and specificity of such early warning system. Moreover, miniaturization and low cost production of sensors have increased the possibilities to measure multiple animals in a herd at the same time. Incorporating data about within-herd spatial position, group size and group composition will improve the successful detection of poachers. Our objective is to develop a wireless network of multiple sensors for sensing alarm responses of ungulate herds to prevent poaching of rhinos and elephants.
