The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting ...
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Automatic weeding technologies have attained a lot of attention lately, because of the harms and challenges weeds cause for livestock farming, in addition to that weeds reduce yields. We are targeting automatic and me...
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ISBN:
(纸本)9798331516246;9798331516239
Automatic weeding technologies have attained a lot of attention lately, because of the harms and challenges weeds cause for livestock farming, in addition to that weeds reduce yields. We are targeting automatic and mechanical Rumex weeding in open pasture fields using light weight mobile field robot technologies. Since the Rumex plants can cause oxalate poisoning in livestock, it is important to remove the weeds from the pastures. We describe a mobile weeding robot with GNSS navigation, 3D computer vision for weed detection, and a robot arm with a mechanical weeding tool. Our main contribution is showing the feasibility of light weight robot, sensor, and tool technologies in mechanical removal of weed seedlings.
Software security exploits, such as Return-Oriented Programming (ROP) attacks, have persisted for more than a decade. ROP attacks inject malicious behaviors into programs, posing serious risks to computing devices, an...
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ISBN:
(纸本)9798350376975;9798350376968
Software security exploits, such as Return-Oriented Programming (ROP) attacks, have persisted for more than a decade. ROP attacks inject malicious behaviors into programs, posing serious risks to computing devices, and they can be particularly challenging to detect in systems with limited resources. In this paper, we introduce an approach that exploits Low-Level Virtual Machine (LLVM) passes, programmatic transformations applied during compilation, to detect ROP attacks in ARM-based embedded systems. By customizing LLVM passes, developers can integrate tailored security checks and optimizations into embedded systems requirements. Our approach is motivated by the use of Hardware Performance Counters (HPCs) for certain mitigations, which are not commonly available on all embedded systems. The experimental evaluation of our approach for detecting ROP attacks in real-world applications shows that it is feasible and can be extended to detect new attacks independently of an Operating System (OS). The storage overhead induced by our approach is approximately 55%.
Cyber-physical systems have become an essential part of the modern healthcare industry. The healthcare cyber-physical systems (HCPS) combine physical and cyber components to improve the healthcare industry. While HCPS...
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ISBN:
(纸本)9798350376975;9798350376968
Cyber-physical systems have become an essential part of the modern healthcare industry. The healthcare cyber-physical systems (HCPS) combine physical and cyber components to improve the healthcare industry. While HCPS has many advantages, it also has some drawbacks, such as a lengthy data entry process, a lack of real-time processing, and limited real-time patient visualization. To overcome these issues, this paper represents an innovative approach to integrating large language model (LLM) to enhance the efficiency of the healthcare system. By incorporating LLM at various layers, HCPS can leverage advanced AI capabilities to improve patient outcomes, advance data processing, and enhance decision-making.
The eight papers included in this special issue represent a selection of extended contributions presented at the 17th internationalconference on Soft Computing Models in Industrial and Environmental applications, SOC...
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The eight papers included in this special issue represent a selection of extended contributions presented at the 17th internationalconference on Soft Computing Models in Industrial and Environmental applications, SOCO 2022 held in Salamanca, Spain, September 6th-8th, 2022, and organized by the BISITE group at University of Salamanca. SOCO 2022 internationalconference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines which investigate, simulate, and analyse very complex issues and phenomena. This special issue is aimed at practitioners, researchers, and postgraduate students who are engaged in developing and applying advanced intelligent systems principles to solve real-world problems in the mentioned fields.
Fostering the widespread adoption of digital credentials requires trust and confidence within the civilian population, which needs to be facilitated through strong privacy guarantees. In this context, the concept of t...
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ISBN:
(纸本)9798350376975;9798350376968
Fostering the widespread adoption of digital credentials requires trust and confidence within the civilian population, which needs to be facilitated through strong privacy guarantees. In this context, the concept of third-party deniability, i.e., a credential verifier's inability to prove the authenticity of received personal data to third parties, is particularly important. This work gives a structured overview of signature-based authentication mechanisms which offer third-party deniability and can be applied to the setting of a three-party credential system for digital identity documents. The resulting primitives are then compared with respect to practical challenges and requirements arising from credential systems.
In engineering and scientific practice, models of uncertain discrete control systems can be described by interval systems and interval optimization problems over extremal algebra. A manufacture control example is give...
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ISBN:
(纸本)9798350386783;9798350386776
In engineering and scientific practice, models of uncertain discrete control systems can be described by interval systems and interval optimization problems over extremal algebra. A manufacture control example is given to show the motivation and the application of AE solutions. Full characterizations are established. Moreover, the concepts and characterizations of traditional solutions are also extended to max-min algebra, as special cases of AE solutions.
Understanding user behavior in the context of recommender systems remains challenging for researchers and practitioners. Inconsistent and misleading user information, which is often concealed in datasets, can inevitab...
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ISBN:
(纸本)9798350310085
Understanding user behavior in the context of recommender systems remains challenging for researchers and practitioners. Inconsistent and misleading user information, which is often concealed in datasets, can inevitably shape the recommendation results in certain distorted ways despite utilizing recommender models with enhanced personalizing capabilities. Naturally, the quality of data that fuels those recommenders should be extremely reliable and free of any biases that might be invisible to a model, irrespective of its type. In this article, we introduce two modern forms of noise that are intrinsically hard to detect and eliminate;one is malicious in nature and will be termed Burst while the other is unique in that it forms its own category and will be referred to as Opt-out. Additionally, with the aim of segregating the nature of noise behind such threats, we present a distinct case study on Burst and Opt-out to illustrate how the detection of those threats can be challenging compared to that of traditional noise and with the current detection methods. Finally, we expound on the ability of such threats to bias the output of recommenders in their own unique way while primarily retaining data that is not fundamentally erroneous.
This paper presents a new category that has been added to the classification of Kim and Ko (2017) for programming learning systems, namely the Online Coding Tutorial System (OCTS) category. In this current study, firs...
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ISBN:
(纸本)9798350376975;9798350376968
This paper presents a new category that has been added to the classification of Kim and Ko (2017) for programming learning systems, namely the Online Coding Tutorial System (OCTS) category. In this current study, firstly, seven popular online coding tutorial systems have been selected to investigate how these systems taught learners and what their characteristics and features were. Secondly, from Kim and Ko's classification, one system has been selected from each category and analyzed across the identified characteristics of online coding tutorial systems to investigate whether any existing category in Kim and Ko's classification shares the same characteristics. As a result, it was found that online coding tutorial systems have adopted many of the features that have been identified in Kim and Ko's first category of interactive platforms, along with some aspects of their creative platforms and MOOCs. Therefore, online coding tutorial systems have been considered a new category of programming learning systems that includes several characteristics from other existing categories.
This research paper focuses on developing vision-based autonomous navigation and motion control algorithms for quadcopters to track high-rise industrial vertical structures. A camera, fixed on the drone will capture t...
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ISBN:
(纸本)9798331516246;9798331516239
This research paper focuses on developing vision-based autonomous navigation and motion control algorithms for quadcopters to track high-rise industrial vertical structures. A camera, fixed on the drone will capture the Red-Green-Blue (RGB) channel image and will feed to the image processing node, which is a convolution neural network (CNN) based encoder-decoder model for image segmentation. The segmented image will go to the classical computer vision algorithm to get the desired features for navigation and the motion control algorithm. In this paper, we have implemented a nonlinear sliding mode control algorithm for motion control and discuss the results of positioning and motion control of the drone for the motion in circular and spiral paths. The entire experimentation is performed on the DJI M100 drone with the Nvidia Jetson Xavier NX as an onboard computer.
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