Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional indep.ndence testing ...
Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional indep.ndence testing commonly employed in constraint-based causal discovery for identifying causal relations. To address this issue, existing methods introduced proxy variables to adjust for the bias caused by unobserveness. However, these methods were either limited to categorical variables or relied on strong parametric assumptions for identification. In this paper, we propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables, without any parametric constraint. Our procedure is based on discretization, which under completeness conditions, is able to asymptotically establish a linear equation whose coefficient vector is identifiable under the causal null hypothesis. Based on this, we introduce our test statistic and demonstrate its asymptotic level and power. We validate the effectiveness of our procedure using both synthetic and real-world data. Code is publicly available at https://***/lmz123321/proxy_causal_discovery. Copyright 2024 by the author(s)
Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management....
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ISBN:
(数字)9798331540012
ISBN:
(纸本)9798331540029
Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan.
Wolves are spreading in the Alpine region at an increasing rate, which leads to human–wolf conflicts. In order to reduce those and to perform an active wolf management, solid information about the presence of wolves ...
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Dual-arm manipulation is a key enabler for significantly enhancing the interaction between humans and robots, and their capabilities to purposefully shape the surrounding environment. However, the spatiotemporal coord...
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ISBN:
(数字)9798331509231
ISBN:
(纸本)9798331509248
Dual-arm manipulation is a key enabler for significantly enhancing the interaction between humans and robots, and their capabilities to purposefully shape the surrounding environment. However, the spatiotemporal coordination between the motion of the hands required for this type of actions makes their planning not trivial. A proper definition of these coordination patterns moving from the human example could simplify their translation on the robot side, fostering the generation of effective bimanual tasks. In this work, we propose Multivariate functional Principal Component Analysis (MfPCA) as a mathematical tool to encode inter-hands temporal kinematic covariations in terms of principal spatiotemporal coordination patterns in the Cartesian domain. We compared these patterns extracted from a dataset of human bimanual tasks with those resulting from the usage of classical fPCA, applied indep.ndently to each hand (univariate fPCA). We found that MfPCA allows for a better classification of the tasks, with respect to a state of the art taxonomy. For what concerns motion planning, MfPCA and fPCA yield similar accuracy in the reconstruction of the motion, but with a smaller number of principal components needed in the MfPCA case. These results, although preliminary, can open interesting perspectives for the usage of MfPCA for human-like bimanual motion planning and control of robotic manipulators, as well as for action recognition, to enable a more effective human-robot interaction.
Understanding human behavior and monitoring mental health are essential to maintaining the community and society's safety. As there has been an increase in mental health problems during the COVID-19 pandemic due t...
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The current industry has to adapt to rapidly changing customers' needs. Reconfigurable manufacturing, therefore, provides capacity and functionality on demand which is essential for competitiveness in fast-changin...
The current industry has to adapt to rapidly changing customers' needs. Reconfigurable manufacturing, therefore, provides capacity and functionality on demand which is essential for competitiveness in fast-changing markets. Furthermore, Industry 4.0 or even more so, Industry 5.0 emphasizes human-centred production with collaborative robots, Cobots, to create human-robot interactions. In such scenarios, safety and security are difficult to address due to the intrinsic features of reconfigurable manufacturing, like exposure to numerous requirements changes in a short period. As safety and security can conflict in different phases of the system life-cycle, one of the earliest activities to avoid conflicts is requirements engineering which can significantly diminish the cost and time of fixing issues compared to later phases like operation. This paper proposes a methodology for safety and security requirements interaction management, including conflict detection and resolution, and shows its applicability through a reconfigurable collaborative human-robot use case. Based on the proposed methodology, we detected and resolved two safety and security requirement conflicts.
This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases...
This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.
The sharing of information through unsecured networks like the internet has increased fast recently. This information could be text, audio, image, or video. The security of data transmission is an essential issue. Enc...
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Due to the increasing amount of industrial data worldwide, deep learning solutions have become extensively popular for preventive maintenance programs. Preventive maintenance reduces the costs of equipment failures, t...
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ISBN:
(数字)9798350333961
ISBN:
(纸本)9798350333978
Due to the increasing amount of industrial data worldwide, deep learning solutions have become extensively popular for preventive maintenance programs. Preventive maintenance reduces the costs of equipment failures, thus crafting detailed preventive maintenance programs are very effective. Neural networks are intelligent computing techniques inspired by biological neurons. Neural networks are one of the most common and practical machine learning algorithms that are utilized in many industrial applications. In this research, a fully connected neural network is developed to predict the systems reliability indices, i.e. the number of failures occurring in feeders and the amount of energy not served (ENS) in electricity distribution feeders. As a result, a data-driven improvement is provided for the preventive maintenance program.
OFDM (Orthogonal Frequency Division Multiplexing) Next-generation wireless communication networks rely heavily on the OFDM system to transmit digital information in a more effective method as well as other traditional...
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ISBN:
(数字)9781665474832
ISBN:
(纸本)9781665474849
OFDM (Orthogonal Frequency Division Multiplexing) Next-generation wireless communication networks rely heavily on the OFDM system to transmit digital information in a more effective method as well as other traditional methods. Using mobile devices, digital document data such as videos, images, and texts can be easily shared and copied without the owner's permission. In this case, information security is a primary consideration. There are several advantages to using chaotic systems, such as their randomness, which makes them far more secure than traditional encryption methods, such as RSA (AES, DES). An efficient method for transmitting secure images across an AWGN channel has been developed because of this research. The dual-sided encryption and scrambling method for an OFDM system proposed in this paper is described in detail. Here, MATLAB is used to design an OFDM system that employs high-efficiency scrambling and a chaotic map for encryption to achieve high-security image transfer. Signal to noise ratio as well as bit error rate measurements are utilized to gauge the clear of image transmission. Using entropy, histogram analysis, NPCR, and UACI for testing encryption quality.
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