The aviation industry needs led to an increase in the number of aircraft in the sky. When the number of flights within an airspace increases, the chance of a mid-air collision increases. Systems such as the Traffic Alert and Collision Avoidance System (TCAS) and Airborne Collision Avoidance System (ACAS) are currently used to alert pilots for potential mid-air collisions. The TCAS and the ACAS use algorithms to perform Aircraft Trajectory Predictions (ATPs) to detect potential conflicts between aircrafts. In this paper, three different aircraft trajectory prediction algorithms named Deep Neural Network (DNN), Random Forest (RF) and Extreme Gradient Boosting were implemented and evaluated in terms of their accuracy and robustness to predict the future aircraft heading. These algorithms were as well evaluated in the case of adversarial samples. Adversarial training is applied as defense method in order to increase the robustness of ATPs algorithms against the adversarial samples. Results showed that, comparing the three algorithm’s performance, the extreme gradient boosting algorithm was the most robust against adversarial samples and adversarial training may benefit the robustness of the algorithms against lower intense adversarial samples. The contributions of this paper concern the evaluation of different aircraft trajectory prediction algorithms, the exploration of the effects of adversarial attacks, and the effect of the defense against adversarial samples with low perturbation compared to no defense mechanism.
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We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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From the article: Abstract: An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.
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Our current smart society, where problems and frictions are smoothed out with smart, often invisible technology like AI and smart sensors, calls for designers who unravel and open the smart fabric. Societies are not malleable, and moreover, a smooth society without rough edges is neither desirable nor livable. In this paper we argue for designing friction to enhance a more nuanced debate of smart cities in which conflicting values are better expressed. Based on our experiences with the Moral Design Game, an adversarial design activity, we came to understand the value of creating tangible vessels to highlight conflict and dipartite feelings surrounding smart cities.
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The main hypothesis underlying this article is that although arbitrators are not formally part of national justice systems, they have dealt with questions of EU fundamental rights and the European rule of law standards for quite some time, at least formally since the landmark CJEU judgment in Eco Swiss in 1999. In fact, in all forms of arbitration, be it national or international, taking place in or across (Member) States daily and not necessarily concerning the application by arbitrators of EU law stricto sensu, arbitrators can be seen as guardians of many crucial procedural guarantees that increase parties’ access to justice and advance the European rule of law, or so we wish to argue. This article is an exploratory piece. That is, it combines the format of the state-of-the-art review with the format of conference proceedings through which we present the main activities of the DG Justice TRIIAL project concerning arbitration. Our main goal is three-fold: (1) to advance the discussion on the relationship between the European rule of law and arbitration, (2) to present the main findings stemming from research and training activities within the TRIIAL training workshops on arbitration, and (3) to formulate future research and practical questions on the topic at hand.
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In this post I give an overview of the theory, tools, frameworks and best practices I have found until now around the testing (and debugging) of machine learning applications. I will start by giving an overview of the specificities of testing machine learning applications.
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This study presents an automated method for detecting and measuring the apex head thickness of tomato plants, a critical phenotypic trait associated with plant health, fruit development, and yield forecasting. Due to the apex's sensitivity to physical contact, non-invasive monitoring is essential. This paper addresses the demand for automated, contactless systems among Dutch growers. Our approach integrates deep learning models (YOLO and Faster RCNN) with RGB-D camera imaging to enable accurate, scalable, and non-invasive measurement in greenhouse environments. A dataset of 600 RGB-D images captured in a controlled greenhouse, was fully preprocessed, annotated, and augmented for optimal training. Experimental results show that YOLOv8n achieved superior performance with a precision of 91.2 %, recall of 86.7 %, and an Intersection over Union (IoU) score of 89.4 %. Other models, such as YOLOv9t, YOLOv10n, YOLOv11n, and Faster RCNN, demonstrated lower precision scores of 83.6 %, 74.6 %, 75.4 %, and 78 %, respectively. Their IoU scores were also lower, indicating less reliable detection. This research establishes a robust, real-time method for precision agriculture through automated apex head thickness measurement.
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The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
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The huge number of images shared on the Web makes effective cataloguing methods for efficient storage and retrieval procedures specifically tailored on the end-user needs a very demanding and crucial issue. In this paper, we investigate the applicability of Automatic Image Annotation (AIA) for image tagging with a focus on the needs of database expansion for a news broadcasting company. First, we determine the feasibility of using AIA in such a context with the aim of minimizing an extensive retraining whenever a new tag needs to be incorporated in the tag set population. Then, an image annotation tool integrating a Convolutional Neural Network model (AlexNet) for feature extraction and a K-Nearest-Neighbours classifier for tag assignment to images is introduced and tested. The obtained performances are very promising addressing the proposed approach as valuable to tackle the problem of image tagging in the framework of a broadcasting company, whilst not yet optimal for integration in the business process.
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This research investigates the integration of stakeholders' values into the digital frameworks of Collective Management Organizations (CMOs) within the Dutch music copyright system. Utilizing Q methodology, the study captures diverse perspectives from composers, lyricists, publishers, and CMO representatives on values, value tensions, norms, and system requirements. A pilot study with four experts tested data collection methods and refined the study design for a larger, follow-up study involving 30 participants. Preliminary findings, based on factor analysis of participant rankings of 30 statements, reveal two distinct perspectives: one focused on "Fairness and Accountability," emphasizing trust-building and equitable treatment, and the other on "Technological Efficiency and Transparency," prioritizing clear information, verification mechanisms, and advanced IT systems. Qualitative insights from participant interviews provide nuanced understanding, highlighting the importance of transparency in royalty processes, balanced application of technology, and equitable royalty distribution in the digital age. This research contributes to the modernization of copyright management systems offering a conceptual model adaptable to other creative (Intellectual Property) industries
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