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3In the Netherlands an innovative programme for early detection of chronic obstructive pulmonary disease (COPD) in primary care among patients aged 40–70 years has been evaluated in both an effect study and a pilot implementation study. Health-care providers identified four obstacles for successful implementation of a COPD early detection programme. This Brief Communication describes the most important results of a qualitative study using in-depth interviews.
ARTIKEL
We present a novel anomaly-based detection approach capable of detecting botnet Command and Control traffic in an enterprise network by estimating the trustworthiness of the traffic destinations. A traffic flow is classified as anomalous if its destination identifier does not origin from: human input, prior traffic from a trusted destination, or a defined set of legitimate applications. This allows for real-time detection of diverse types of Command and Control traffic. The detection approach and its accuracy are evaluated by experiments in a controlled environment.
CONFERENTIEBIJDRAGE
Background and objectives: Physical examinations (PE) detect relatively few recurrences after breast cancer treatment, but are still recommended in surveillance guidelines. This study explores Health Care Professionals (HCPs) perspectives on the frequency and value of PE. Methods: Semi-structured interviews were conducted in 11 Dutch hospitals with 22 HCPs involved in breast cancer follow-up. Interviews were coded by the framework methodology using the software Atlas.ti 23. Results: Most HCPs occasionally deviated from the guideline, giving some patients more and others less PE than recommended. The majority attributed rather limited value to PE for detecting recurrences and all performed PE more often than they perceived valuable. More PE was performed to meet patients’ wishes, to evaluate treatment effects or to detect signals of recurrences which imaging cannot assess. Also the organization of consultations determined PE’s frequency. Performing less PE was mainly because of PE’s low detection rates of recurrences. Conclusions: HCPs seem aware of the low detection rates but still perform more PE than perceived as valuable to detect recurrences because of reasons other than detecting recurrences. Better information provision on PE’s limited value in detecting recurrences may prevent unrealistic expectations and unnecessary PE. Further evidence on the cost-effectiveness of PE is warranted to revise guidelines.
ARTIKEL
In recent years, drones have increasingly supported First Responders (FRs) in monitoring incidents and providing additional information. However, analysing drone footage is time-intensive and cognitively demanding. In this research, we investigate the use of AI models for the detection of humans in drone footage to aid FRs in tasks such as locating victims. Detecting small-scale objects, particularly humans from high altitudes, poses a challenge for AI systems. We present first steps of introducing and evaluating a series of YOLOv8 Convolutional Neural Networks (CNNs) for human detection from drone images. The models are fine-tuned on a created drone image dataset of the Dutch Fire Services and were able to achieve a 53.1% F1-Score, identifying 439 out of 825 humans in the test dataset. These preliminary findings, validated by an incident commander, highlight the promising utility of these models. Ongoing efforts aim to further refine the models and explore additional technologies.
CONFERENTIEBIJDRAGE
In this paper we propose a head detection method using range data from a stereo camera. The method is based on a technique that has been introduced in the domain of voxel data. For application in stereo cameras, the technique is extended (1) to be applicable to stereo data, and (2) to be robust with regard to noise and variation in environmental settings. The method consists of foreground selection, head detection, and blob separation, and, to improve results in case of misdetections, incorporates a means for people tracking. It is tested in experiments with actual stereo data, gathered from three distinct real-life scenarios. Experimental results show that the proposed method performs well in terms of both precision and recall. In addition, the method was shown to perform well in highly crowded situations. From our results, we may conclude that the proposed method provides a strong basis for head detection in applications that utilise stereo cameras.
CONFERENTIEBIJDRAGE
Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared. Link to the formail publication via its DOI https://doi.org/10.1016/j.autcon.2020.103344
ARTIKEL
With the proliferation of misinformation on the web, automatic methods for detecting misinformation are becoming an increasingly important subject of study. If automatic misinformation detection is applied in a real-world setting, it is necessary to validate the methods being used. Large language models (LLMs) have produced the best results among text-based methods. However, fine-tuning such a model requires a significant amount of training data, which has led to the automatic creation of large-scale misinformation detection datasets. In this paper, we explore the biases present in one such dataset for misinformation detection in English, NELA-GT-2019. We find that models are at least partly learning the stylistic and other features of different news sources rather than the features of unreliable news. Furthermore, we use SHAP to interpret the outputs of a fine-tuned LLM and validate the explanation method using our inherently interpretable baseline. We critically analyze the suitability of SHAP for text applications by comparing the outputs of SHAP to the most important features from our logistic regression models.
CONFERENTIEBIJDRAGE
In recent years, the fight against terrorism and political violence has focused more on anticipating the threats that they pose. Therefore, early detection of ideas by local professionals has become an important part of the preventive approach in countering radicalization. Frontline workers who operate in the arteries of society are encouraged to identify processes toward violent behavior at an early stage. To date, however, little is known about how these professionals take on this screening task at their own discretion. Research from the Netherlands suggests that subjective assessment appears to exist. In this article, we argue that the absence of a clear norm for preliminary judgments affects prejudice or administrative arbitrariness, which may cause side effects due to unjustified profiling.
ARTIKEL
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.
ARTIKEL
