Breast cancer is a common cancer among women worldwide, and early diagnosis is crucial for improving the chances of successful treatment. Decision support systems (DSS) are computer-based systems that help and guide u...
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Many systems operating in a computer data center environment are extremely sensitive due to the importance of the services they provide. It is crucial to maintain them in constant operation without service disruption ...
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In the minimum degree vertex deletion problem,we are given a graph,a distinguished vertex in the graph,and an integer κ,and the question is whether we can delete at most κ vertices from the graph so that the disting...
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In the minimum degree vertex deletion problem,we are given a graph,a distinguished vertex in the graph,and an integer κ,and the question is whether we can delete at most κ vertices from the graph so that the distinguished vertex has the unique minimum *** maximum degree vertex deletion problem is defined analogously but here we want the distinguished vertex to have the unique maximum *** is known that both problems areΨ-hard and fixed-parameter intractable with respect to some natural *** this paper,we study the(parameterized)complexity of these two problems restricted to split graphs,p-degenerate graphs,and planar *** study provides a comprehensive complexity landscape of the two problems restricted to these special graphs.
In this article, we present an evolution language for graph databases and a method to realize evolution operations on graph databases and their schema. Graph database management systems like Neo4j can be used for diff...
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The swift expansion of solar photovoltaic (PV) technology has introduced significant challenges for those overseeing electricity distributions due to its reliance on weather conditions. In this study, predictive model...
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Ensembles are one of the most promising research directions for unsupervised anomaly detection. But combining many different models into such an ensemble requires good combination procedures that are able to combine t...
Ensembles are one of the most promising research directions for unsupervised anomaly detection. But combining many different models into such an ensemble requires good combination procedures that are able to combine the strengths of many different submodels. To find, evaluate and understand these procedures, we create the biggest experiment to date, including multiple orders of magnitude more ensembles than each of our competitors. Using this high number of comparisons, we also study the effect different normalization methods have on the combination procedure and extract conditional performances of individual models. We use this, to develop a simple set of best practices to create good and reliable anomaly detection ensembles.
This work introduces a novel approach to the currently available automatic volumetric measurement systems used in the industry. The proposed system uses a ceiling-mounted laser in tandem with an RGB camera to generate...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
This work introduces a novel approach to the currently available automatic volumetric measurement systems used in the industry. The proposed system uses a ceiling-mounted laser in tandem with an RGB camera to generate a point cloud of the object that is to be measured, utilizing an adaptation of a structured light scanning approach. The resulting point clouds are post-processed, removing detected structural anomalies to improve the yielded results. The resulting system is then tested and validated on a set of seven objects of various sizes commonly encountered in industrial environments. For these experiments, our approach yields outlier-corrected volumes which are accurate at up to 1% volume divergence. The results differ vastly when going beyond the system’s optimal object size range. The limits of the system are tested for smaller and larger objects, showing a notably higher inaccuracy when tested on relatively small or large objects. Further experiments, exploring optimal laser line distance, lighting conditions, and (in the case of containers) filling degrees are also conducted.
Software-Defined Networking (SDN) embodies an advanced network architecture, developed to transform traditional networks and adapt them to fulfill contemporary needs. SDN distinguishes itself by decoupling the control...
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Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,tr...
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Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.
This submission introduces an innovative method based on graph representation for re-identifying chipwood surface structures. In the specific application under consideration, it also addresses the re-identification of...
This submission introduces an innovative method based on graph representation for re-identifying chipwood surface structures. In the specific application under consideration, it also addresses the re-identification of Euro-pallets. To achieve this objective, we propose a departure from conventional re-identification methods by advocating the substitution of the standard image representation with a significantly compressed graph representation. This not only facilitates the development of an efficient algorithm but also imparts resilience to environmental variations, including rotation and shearing. The resultant approach, named “IRAG” (Image Representation through Anomaly Graphs), manifests as a siamese graph neural network. This network is employed on a pre-existing dataset, which comprises images sourced from 502 EPAL pallet blocks and has been previously published. The obtained results from this approach show a rank-1 accuracy of 27Additionally, the experiments conducted in this work demonstrate that the re-identification accuracy of the model is not affected by rotation or shearing, indicating the model’s invariance to these environmental *** paper represents an updated and improved version of the paper “Towards graph representation-based re-identification of chipwood pallet blocks” published at ICMLA 2022.
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