The proceedings contain 95 papers. The topics discussed include: a trusted mechanism for participant screening and verification in federated learning;real-time rice disease spot segmentation using an improved YOLOv8 f...
ISBN:
(纸本)9798350375077
The proceedings contain 95 papers. The topics discussed include: a trusted mechanism for participant screening and verification in federated learning;real-time rice disease spot segmentation using an improved YOLOv8 framework;real-time rice disease spot segmentation using an improved YOLOv8 framework;real-time AR inspection method for main equipment of converter station based on two-channel threshold segmentation and visual feature value;Improved 3D object detection method based on PointPillars;design of track logistics control system based on digital twin;inverse kinematics solution algorithm of electric climbing robot based on improved beetle antennae search algorithm;and installation design based on the relationship between artificial intelligence and art development.
The proceedings contain 94 papers. The topics discussed include: classification method of surface defects of aluminum profile based on transfer learning;an improved anchor-free object detection method;lightweight real...
ISBN:
(纸本)9781665492461
The proceedings contain 94 papers. The topics discussed include: classification method of surface defects of aluminum profile based on transfer learning;an improved anchor-free object detection method;lightweight real-time object detection system based on embedded ai development kit;video content understanding based on feature selection and semantic alignment network;transferability of pretrained convolutional neural networks for breast cancer detection;analyzing the determinations of financial inclusion in Africa based on random forest model and logistic regression model;the impact of the distance sensors orientation on the obstacle avoidance ability of the robot;research on the performance of different convolutional neural network models on small datasets;construction method of network feasible path for power data platform;resource management scheduling-based on proximal policy optimization;and a study of fair prediction on credit assessment based on counterfactual fairness.
This article addresses the fundamental questions on machinelearning: what does it mean for machines to learn from experience, and what does it mean by machines in machinelearning? Despite recent popularity and growt...
详细信息
ISBN:
(纸本)9798350358513;9798350358520
This article addresses the fundamental questions on machinelearning: what does it mean for machines to learn from experience, and what does it mean by machines in machinelearning? Despite recent popularity and growth, significant challenges remain as the industry rapidly advances toward autonomous machines. In the context of autonomy, there is more to learning from experience than training machines to approximate big data. This brings the fundamental questions to the forefront of automation science and engineering as a critical area of exploration. This article examines the precise notion of autonomy in the context of machinelearning and provides a general framework for cyber-physical systems to become fully autonomous by learning from experience. The framework is derived from the principles of developmental autonomous behavior, which encapsulates broad classes of learning mechanisms. It offers a novel use case of machinelearning where sensorimotor systems build inference engines internally on their own by their own initiatives to develop new skills and behavior. The key contributions of this article are threefold. First on knowledge: it provides precise definitions of emergent and predefined knowledge and their roles in cognitive development of machines. Second on autonomy: it clarifies what a fully autonomous machine means by providing the precise definitions of autonomy and emergent behavior. Third on machinelearning: it unifies machinelearning as the ultimate-proximate causal drivers of emergent behavior. Ultimately, this article logically explains why and how a fully autonomous machine is possible by directly answering the fundamental questions.
This special issue contains nine extended and rigorously peer-reviewed papers selected from those originally presented at ECBS 2023, the 8th internationalconference on engineering of Computer-Based systems, held at M...
详细信息
This special issue contains nine extended and rigorously peer-reviewed papers selected from those originally presented at ECBS 2023, the 8th internationalconference on engineering of Computer-Based systems, held at M & auml;lardalen University, Sweden, October 16-18, 2023, under the theme "engineering for Responsible AI". The included papers represent innovative contributions addressing critical aspects of responsible artificial intelligence and integrated engineering practices. These contributions span from formal verification and security analyses of IoT protocols and federated learning frameworks to machinelearning-based simulations and predictions in hardware and software systems. The selection also includes work on automata learning techniques for protocol compliance, continuous integration approaches for neural network-based autonomous systems, assertion usage in software testing, language-driven engineering for code generation, and the integration of IoT backends in digital twin infrastructures. Together, these papers showcase recent advances, offering valuable insights into the rigorous integration of modern technologies within complex, computer-based systems.
In recent years, numerous machinelearning (ML) models, including Deep learning (DL) and classic ML models, have been developed to detect software vulnerabilities. However, there is a notable lack of comprehensive and...
详细信息
In recent years, numerous machinelearning (ML) models, including Deep learning (DL) and classic ML models, have been developed to detect software vulnerabilities. However, there is a notable lack of comprehensive and systematic surveys that summarize, classify, and analyze the applications of these ML models in software vulnerability detection. This absence may lead to critical research areas being overlooked or underrepresented, resulting in a skewed understanding of the current state of the art in software vulnerability detection. To close this gap, we propose a comprehensive and systematic literature review that characterizes the different properties of ML-based software vulnerability detection systems using six major Research Questions (RQs). Using a custom web scraper, our systematic approach involves extracting a set of studies from four widely used online digital libraries: ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. We manually analyzed the extracted studies to filter out irrelevant work unrelated to software vulnerability detection, followed by creating taxonomies and addressing RQs. Our analysis indicates a significant upward trend in applying ML techniques for software vulnerability detection over the past few years, with many studies published in recent years. Prominent conference venues include the internationalconference on Software engineering (ICSE), the international Symposium on Software Reliability engineering (ISSRE), the Mining Software Repositories (MSR) conference, and the ACM internationalconference on the Foundations of Software engineering (FSE), whereas Information and Software Technology (IST), Computers & Security (C&S), and Journal of systems and Software (JSS) are the leading journal venues. Our results reveal that 39.1% of the subject studies use hybrid sources, whereas 37.6% of the subject studies utilize benchmark data for software vulnerability detection. Code-based data are the most commonly used data t
Currently,simultaneous buffer and service rate allocation is a topic of interest in the optimization of manufacturing *** allocation problems have been solved previously to satisfy economic requirements;however,owing ...
详细信息
Currently,simultaneous buffer and service rate allocation is a topic of interest in the optimization of manufacturing *** allocation problems have been solved previously to satisfy economic requirements;however,owing to the progress of green manufacturing,energy conservation and environmental protection have become increasingly ***,an energy-efficient approach is developed to maximize the throughput and minimize the energy consumption of manufacturing systems,subject to the total buffer capacity,total service rate,and predefined energy *** energy-efficient approach integrates the simulated annealing-non-dominated sorting genetic algorithm-II with the honey badger algorithm-histogram-based gradient boosting regression *** former algorithm searches for Pareto-optimal solutions of sufficient *** latter algorithm builds prediction models to rapidly calculate the throughput,energy consumption,and energy *** examples show that the proposed hybrid approach can achieve a better solution quality compared with previously reported ***,the prediction models can rapidly evaluate manufacturing systems with sufficient *** study benefits the multi-objective optimization of green manufacturing systems.
machinelearning has developed into a crucial tool for the financial industry, allowing financial organizations to enhance their processes and offerings. This paper gives a general overview of machinelearning's u...
详细信息
As the number of online purchases increases at an alarming rate, fraudulent activities have surfaced as a major concern for retailers and consumers alike. Fraudsters frequently face setbacks when attempting to bypass ...
详细信息
As the number of online purchases increases at an alarming rate, fraudulent activities have surfaced as a major concern for retailers and consumers alike. Fraudsters frequently face setbacks when attempting to bypass automated rule-based systems that are specifically engineered to identify fraudulent activities. The objective of this research is to investigate methods by which fraudulent online transactions can be detected using machinelearning algorithms. To detect fraudulent activities, an analysis is performed on various machinelearning methodologies, including supervised, unsupervised, and semi-supervised learning. This research primarily examines the evaluation criteria, feature engineering, and model selection that are distinct to the domain of electronic commerce. Furthermore, obstacles that manifest during the detection of fraudulent activities in electronic commerce must be confronted, including the dissemination of deceptive ideas and manipulated data. By subjecting machinelearning models to rigorous testing on real-world datasets, we demonstrate their efficacy in detecting fraudulent activities and underscore their capacity to dynamically adjust to evolving fraud patterns. In conclusion, we discuss the practical obstacles and possible avenues for future research concerning machinelearning-based e-commerce fraud detection systems.
The emergence of sixth generation (6G) mobile networks is poised to transform global communication by providing unmatched speed, dependability, and sophisticated network management. This study examines the essential f...
详细信息
暂无评论