This study contributes to a deeper understanding of energy consumption in software applications, emphasizing the critical need for energy efficiency. We focus on integrating performance counter events and system call ...
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As a new communication technology for Industry 4.0, Time-Sensitive Networking (TSN) provides reliable support for real-time data amidst massive data transmissions. However, manually verifying the overall performance m...
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Online 3D model repositories such as Thingiverse ofer millions of open source designs that are shared for reuse and remix. Many of the designs are customizable to adapt to real-world objects upon personal needs of var...
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ISBN:
(纸本)9781450393584
Online 3D model repositories such as Thingiverse ofer millions of open source designs that are shared for reuse and remix. Many of the designs are customizable to adapt to real-world objects upon personal needs of varying tasks and physical dimensions. However, it is challenging for novices to discover such designs using text-based search queries, comprehend what each parameter means for customization, locate these parameters on the target objects for measurement, and conduct measurements correctly. These challenges may cause the designs to be incorrectly adjusted, thus failing to function as expected and requiring users to start over, which costs additional time and material. We present CustomizAR, a pipeline for facilitating the interactive exploration of adaptive designs and the measurement of real-world constraints to fabricate them correctly. CustomizAR supports the search and discovery of adaptive 3D designs using an object-centric graph-based data structure, and guides users through an interactive measurement process leveraging computer vision techniques. Our technical evaluations and user studies demonstrate that CustomizAR facilitates efective discovery, adjustment, and reuse of adaptive designs that are shared online.
The proceedings contain 31 papers. The topics discussed include: the efficient development of conflict structure datasets for evaluating sentiment recognition bias in large language models;comparative analysis of XGBo...
ISBN:
(纸本)9798350366822
The proceedings contain 31 papers. The topics discussed include: the efficient development of conflict structure datasets for evaluating sentiment recognition bias in large language models;comparative analysis of XGBoost and random forest for used car price prediction;a spatial approach to analyze the distribution and risk factors of stunting in north Sumatra with the k-means algorithm;optimizing broiler chicken supply chains under uncertain average growth rate acceleration;emotion classification for the 2024 general election using lexicon approach and deep learning algorithm;modeling bivariate departure delay distribution for estimating slack allocation;a taxonomy of banking financial model in study of computer science;and data analytics and visualization in bimanual rehabilitation monitoring systems: a user-centered design approach to healthcare professionals decision support.
The need to adaptively manage computersystems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE) with secure operation at relatively low levels of energy consumption is challeng...
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Due to a rising number of entities and more advanced systems, modern air combat engagements are increasing in complexity. Therefore, to ensure success in pilot training and perform accurate threat evaluation using sim...
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ISBN:
(纸本)9783031713965;9783031713972
Due to a rising number of entities and more advanced systems, modern air combat engagements are increasing in complexity. Therefore, to ensure success in pilot training and perform accurate threat evaluation using simulations, it is substantial to not only replicate and simulate the physical properties of the computer-Generated forces (CGFs) to an adequate degree but also to provide them with sufficiently realistic, coordinated and situation-adaptive behavior. Additionally, most air combat research assumes that all aircraft information is known, however, in real-world scenarios, multiple factors, such as sensor performance limitations, can lead to missing or incorrect information about the position, altitude, or velocity of adversary aircraft. In this paper, we propose a Tactical Planning Process as part of an overarching CGF Team Behavior Agent Function utilizing information such as threat risk and the enemy's intent from a Situation Analysis created with realistically available data. This process is partitioned into two stages, Team Planning and Maneuver Selection. Team Planning consists of deciding whether the mission itself should be commenced or aborted, selecting Tactics to counter the threats, as well as performing a Targeting in which threat aircraft are assigned to the individual CGFs. Further, in Maneuver Selection, the own current risks are assessed and used to continuously decide the current task for each CGF with respect to its target. Following, the maneuver command itself is being selected and sent to the simulated aircraft. This is done within an evaluation of the own chances and risks by, in a first step, identifying suitable tactical maneuver types, in a second step, narrow down their parameters, so that, in the final step, predicted risks from the Situation Analysis can be incorporated in the selection process as well. We employ Behavior Trees to guide the CGFs through these different tasks, while repeatedly assessing the developing risks to be ab
Background: Anomaly detection is essential for detecting unusual behaviors in dynamic networks that may represent emerging security threats. Traditional models focus on First-Order Network representations that overloo...
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The increased complexity and frequency of regulations provided by the European Union creates a highly dynamic landscape for the organizations based within it. Implementing these regulations is a challenging task, espe...
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Environmental sustainability and energy security are critical global issues that demand a balance between human progress and ecosystem preservation. This study leverages Explainable AI to perform causal analysis, empl...
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This paper shows the implementation of a physically unclonable function (PUF) of the arbiter type and its modifications, the generation of random numbers and unique identifiers, and a statistical analysis of the gener...
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