In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state i...
详细信息
Deep supervised learning has demonstrated strong capabilities; however, such progress relies on massive and expensive data annotation. Active Learning (AL) has been introduced to selectively annotate samples, thus red...
Deep supervised learning has demonstrated strong capabilities; however, such progress relies on massive and expensive data annotation. Active Learning (AL) has been introduced to selectively annotate samples, thus reducing the human labeling effort. Previous AL research has focused on employing recently trained models to design sampling strategies, based on uncertainty or representativeness. Drawing inspiration from the issue of model forgetting, we propose a novel AL framework called Temporal Inconsistency-Based Active Learning (TIR-AL). In this framework, multiple snapshots of the models across consecutive cycles are jointly utilized to select samples with higher temporal inconsistency, by computing the proposed self-weighted nuclear norm metric. Furthermore, we introduce a consistency regularization term to mitigate the issue of forgetting. Together, these components make full use of the potential of data and facilitate effective interaction within the AL loop. To demonstrate the efficacy of TIR-AL, we conducted a set of experiments illustrating how our approach outperforms state-of-the-art methods without incurring any additional training costs.
This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnera...
详细信息
This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.
Microservice architecture is increasingly replacing monolithic architectures due to its numerous advantages, including ease of deployment, flexibility, and availability. The advent of containerization has further ampl...
Microservice architecture is increasingly replacing monolithic architectures due to its numerous advantages, including ease of deployment, flexibility, and availability. The advent of containerization has further amplified the importance of microservices, making them scalable and portable… Orchestration tools rely on schedulers to control and manage containers running microservices, driving extensive research in search of suitable and optimal scheduling algorithms that minimize costs, reduce overhead, and ensure cluster performance. This paper provides an overview of several popular scheduling algorithms applied by researchers, offering insights into their methodologies through a comparative analysis. We explore the different parameters used and their impact on algorithm performance. Additionally, we delve into the advantages and drawbacks associated with each algorithm, illuminating their strengths and weaknesses. In conclusion, we discuss potential future directions, aiming to identify areas for improvement and innovation in the selection and optimization of scheduling algorithms.
Many Internet platforms collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer reward to incentivize users and compensate their costs ...
详细信息
We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approa...
详细信息
Urban traffic management faces significant challenges due to non-compliance with traffic rules, particularly among motorcycle riders. This study introduces an innovative approach employing the YOLOv9 object detection ...
详细信息
ISBN:
(数字)9798350351200
ISBN:
(纸本)9798350351217
Urban traffic management faces significant challenges due to non-compliance with traffic rules, particularly among motorcycle riders. This study introduces an innovative approach employing the YOLOv9 object detection framework to monitor and analyze motorcycle violations at busy intersections in Marrakech. By leveraging advanced machine learning techniques, the study aimed to detect helmet usage, traffic light violations, and assess gender-based differences in violation rates. Data were collected using smartphone cameras at strategic locations during peak traffic times across selected days. The captured video footage was analyzed using the YOLOv9 model, which was pretrained on diverse traffic scenes to enhance its accuracy and reliability in real-time object detection. The findings reveal that the automated system not only aligns closely with manual counting but also offers greater consistency and efficiency. Key results indicated a pronounced gender disparity in violation frequencies and provided insights into the temporal patterns of traffic rule infractions. Moreover, the study highlighted minor discrepancies due to environmental factors, which were systematically addressed to refine the detection process. Contextual factors in interpreting detection results.
Joint safety and security analysis of cyber-physical systems is a necessary step to correctly capture inter-dependencies between these properties. Attack-Fault Trees represent a combination of dynamic Fault Trees and ...
详细信息
Through the recent progress in the field of microelectronics and the emergence of wireless communication technologies, wireless sensor networks (WSNs) have seen the light of day. However, one of the major problems of ...
详细信息
Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate; they might have high levels of androgens and have many small cysts on the ovaries. It ...
Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate; they might have high levels of androgens and have many small cysts on the ovaries. It can cause missed or irregular menstrual periods, excess hair growth, acne, infertility, and weight gain. Machine Learning (ML) can effectively diagnose this disease at an earlier stage as tons of medical data are available now. Traditional approaches to detect PCOS encompass a combination of clinical evaluation, medical history assessment, physical examination, and laboratory tests. These approaches aim to identify the characteristic symptoms and hormonal imbalances associated with PCOS. Physical examination requires good resources and costs time and money. In recent times, data-driven techniques have substantially advanced disease prediction within the medical field. We aim to utilize ML approaches, incorporating unique feature selection algorithms, to predict PCOS. This paper introduces a data-driven approach to PCOS diagnosis, combining Feature engineering and ML. Several feature selection approaches have been considered to select sets of features for training the ML model, including CatBoost, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), AdaBoost, Random Forest (RF). Results demonstrate that AdaBoost, with ten features selected by RF Feature Importance and Highest Correlation (HC), provides the highest test accuracy.
暂无评论