218 research outputs found
El deseo de los profesores de inglés como lengua extranjera (ILE) chinos de asistir a programas de desarrollo profesional: Explorando el papel de la satisfacción laboral y el compromiso organizacional
This work was supported by “Jiangsu Provincial Social Science Fund of China” (Grant No.: 22ZWD001).The influence of teachers’ inner forces and factors in professional development (PD) has been highlighted in the past decades. However, little is written about the interplay of English as a foreign language (EFL) teachers’ desires to attend PD programs and their perceived job satisfaction and organizational commitment. To fill this gap, this study used three questionnaires to inspect the relationships among these three constructs. It also aimed to showcase whether Chinese EFL teachers’ desire to attend PD programs is predicted by their job satisfaction and organizational commitment. Adopting a random sampling technique, a sample of 357 EFL teachers was recruited from different colleges and universities in China. The results of Structural Equation Modeling (SEM) and correlation analysis revealed a positive and strong correlation between teachers’ job satisfaction, organizational commitment, and their desire to attend PD programs. Moreover, it was found that both job satisfaction and organizational commitment could collectively predict around 73% of changes in teachers’ desire to attend PD programs. The results are discussed and implications for the theory and practice of second/foreign language (L2) education in light of psycho-affective factors are enlisted.En las últimas décadas se ha destacado la influencia de las fuerzas y factores internos de los profesores en el desarrollo profesional (DP). Sin embargo, se ha escrito poco sobre la interacción entre el deseo de los profesores de inglés como lengua extranjera (ILE) chinos de asistir a programas de DP y su percepción de satisfacción laboral y compromiso organizacional. Para llenar esta brecha, este estudio utilizó tres cuestionarios para inspeccionar la relación entre estos tres constructos. También tuvo como objetivo mostrar si el deseo de los profesores de inglés como lengua extranjera (ILE) chinos de asistir a programas de desarrollo profesional está predicho por su satisfacción laboral y compromiso organizacional. Una muestra de 357 profesores de ILE participaron en la encuesta. Los resultados del Modelo de Ecuaciones Estructurales (SEM) y el análisis de correlación revelaron una correlación positiva y fuerte entre la satisfacción laboral de los profesores y su compromiso organizacional, y su deseo de asistir a programas de desarrollo profesional. Además, se encontró que tanto la satisfacción laboral como el compromiso organizacional podrían predecir conjuntamente alrededor del 73% de los cambios en el deseo de los profesores de asistir a programas de desarrollo profesional (β = .73, p < .002). Se discuten los resultados y las implicaciones para la teoría y la práctica de la educación en segundo/idioma extranjero (L2) a la luz de los factores psicoafectivos.Jiangsu Provincial Social Science Fund of China (22ZWD001
El deseo de los profesores de inglés como lengua extranjera (ILE) chinos de asistir a programas de desarrollo profesional: Explorando el papel de la satisfacción laboral y el compromiso organizacional
The influence of teachers’ inner forces and factors in professional development (PD) has been highlighted in the past decades. However, little is written about the interplay of English as a foreign language (EFL) teachers’ desires to attend PD programs and their perceived job satisfaction and organizational commitment. To fill this gap, this study used three questionnaires to inspect the relationships among these three constructs. It also aimed to showcase whether Chinese EFL teachers’ desire to attend PD programs is predicted by their job satisfaction and organizational commitment. Adopting a random sampling technique, a sample of 357 EFL teachers was recruited from different colleges and universities in China. The results of Structural Equation Modeling (SEM) and correlation analysis revealed a positive and strong correlation between teachers’ job satisfaction, organizational commitment, and their desire to attend PD programs. Moreover, it was found that both job satisfaction and organizational commitment could collectively predict around 73% of changes in teachers’ desire to attend PD programs. The results are discussed and implications for the theory and practice of second/foreign language (L2) education in light of psycho-affective factors are enlisted.
FUNDING INFORMATION. This work was supported by “Jiangsu Provincial Social Science Fund of China” (Grant No.: 22ZWD001).En las últimas décadas se ha destacado la influencia de las fuerzas y factores internos de los profesores en el desarrollo profesional (DP). Sin embargo, se ha escrito poco sobre la interacción entre el deseo de los profesores de inglés como lengua extranjera (ILE) chinos de asistir a programas de DP y su percepción de satisfacción laboral y compromiso organizacional. Para llenar esta brecha, este estudio utilizó tres cuestionarios para inspeccionar la relación entre estos tres constructos. También tuvo como objetivo mostrar si el deseo de los profesores de inglés como lengua extranjera (ILE) chinos de asistir a programas de desarrollo profesional está predicho por su satisfacción laboral y compromiso organizacional. Una muestra de 357 profesores de ILE participaron en la encuesta. Los resultados del Modelo de Ecuaciones Estructurales (SEM) y el análisis de correlación revelaron una correlación positiva y fuerte entre la satisfacción laboral de los profesores y su compromiso organizacional, y su deseo de asistir a programas de desarrollo profesional. Además, se encontró que tanto la satisfacción laboral como el compromiso organizacional podrían predecir conjuntamente alrededor del 73% de los cambios en el deseo de los profesores de asistir a programas de desarrollo profesional (β = .73, p < .002). Se discuten los resultados y las implicaciones para la teoría y la práctica de la educación en segundo/idioma extranjero (L2) a la luz de los factores psicoafectivos
Introducing Foundation Models as Surrogate Models: Advancing Towards More Practical Adversarial Attacks
Recently, the no-box adversarial attack, in which the attacker lacks access
to the model's architecture, weights, and training data, become the most
practical and challenging attack setup. However, there is an unawareness of the
potential and flexibility inherent in the surrogate model selection process on
no-box setting. Inspired by the burgeoning interest in utilizing foundational
models to address downstream tasks, this paper adopts an innovative idea that
1) recasting adversarial attack as a downstream task. Specifically, image noise
generation to meet the emerging trend and 2) introducing foundational models as
surrogate models. Harnessing the concept of non-robust features, we elaborate
on two guiding principles for surrogate model selection to explain why the
foundational model is an optimal choice for this role. However, paradoxically,
we observe that these foundational models underperform. Analyzing this
unexpected behavior within the feature space, we attribute the lackluster
performance of foundational models (e.g., CLIP) to their significant
representational capacity and, conversely, their lack of discriminative
prowess. To mitigate this issue, we propose the use of a margin-based loss
strategy for the fine-tuning of foundational models on target images. The
experimental results verify that our approach, which employs the basic Fast
Gradient Sign Method (FGSM) attack algorithm, outstrips the performance of
other, more convoluted algorithms. We conclude by advocating for the research
community to consider surrogate models as crucial determinants in the
effectiveness of adversarial attacks in no-box settings. The implications of
our work bear relevance for improving the efficacy of such adversarial attacks
and the overall robustness of AI systems
Integrating Visual Foundation Models for Enhanced Robot Manipulation and Motion Planning: A Layered Approach
This paper presents a novel layered framework that integrates visual
foundation models to improve robot manipulation tasks and motion planning. The
framework consists of five layers: Perception, Cognition, Planning, Execution,
and Learning. Using visual foundation models, we enhance the robot's perception
of its environment, enabling more efficient task understanding and accurate
motion planning. This approach allows for real-time adjustments and continual
learning, leading to significant improvements in task execution. Experimental
results demonstrate the effectiveness of the proposed framework in various
robot manipulation tasks and motion planning scenarios, highlighting its
potential for practical deployment in dynamic environments.Comment: 3 pages, 2 figures, IEEE Worksho
Low-Mid Adversarial Perturbation against Unauthorized Face Recognition System
In light of the growing concerns regarding the unauthorized use of facial
recognition systems and its implications on individual privacy, the exploration
of adversarial perturbations as a potential countermeasure has gained traction.
However, challenges arise in effectively deploying this approach against
unauthorized facial recognition systems due to the effects of JPEG compression
on image distribution across the internet, which ultimately diminishes the
efficacy of adversarial perturbations. Existing JPEG compression-resistant
techniques struggle to strike a balance between resistance, transferability,
and attack potency. To address these limitations, we propose a novel solution
referred to as \emph{low frequency adversarial perturbation} (LFAP). This
method conditions the source model to leverage low-frequency characteristics
through adversarial training. To further enhance the performance, we introduce
an improved \emph{low-mid frequency adversarial perturbation} (LMFAP) that
incorporates mid-frequency components for an additive benefit. Our study
encompasses a range of settings to replicate genuine application scenarios,
including cross backbones, supervisory heads, training datasets, and testing
datasets. Moreover, we evaluated our approaches on a commercial black-box API,
\texttt{Face++}. The empirical results validate the cutting-edge performance
achieved by our proposed solutions.Comment: published in Information Science
Adaptive Shape Servoing of Elastic Rods using Parameterized Regression Features and Auto-Tuning Motion Controls
In this paper, we present a new vision-based method to control the shape of
elastic rods with robot manipulators. Our new method computes parameterized
regression features from online sensor measurements that enable to
automatically quantify the object's configuration and establish an explicit
shape servo-loop. To automatically deform the rod into a desired shape, our
adaptive controller iteratively estimates the differential transformation
between the robot's motion and the relative shape changes; This valuable
capability allows to effectively manipulate objects with unknown mechanical
models. An auto-tuning algorithm is introduced to adjust the robot's shaping
motion in real-time based on optimal performance criteria. To validate the
proposed theory, we present a detailed numerical and experimental study with
vision-guided robotic manipulators.Comment: 13 pages, 22 figures, 2 table
A Novel Uncalibrated Visual Servoing Controller Baesd on Model-Free Adaptive Control Method with Neural Network
Nowadays, with the continuous expansion of application scenarios of robotic
arms, there are more and more scenarios where nonspecialist come into contact
with robotic arms. However, in terms of robotic arm visual servoing,
traditional Position-based Visual Servoing (PBVS) requires a lot of calibration
work, which is challenging for the nonspecialist to cope with. To cope with
this situation, Uncalibrated Image-Based Visual Servoing (UIBVS) frees people
from tedious calibration work. This work applied a model-free adaptive control
(MFAC) method which means that the parameters of controller are updated in real
time, bringing better ability of suppression changes of system and environment.
An artificial intelligent neural network is applied in designs of controller
and estimator for hand-eye relationship. The neural network is updated with the
knowledge of the system input and output information in MFAC method. Inspired
by "predictive model" and "receding-horizon" in Model Predictive Control (MPC)
method and introducing similar structures into our algorithm, we realizes the
uncalibrated visual servoing for both stationary targets and moving
trajectories. Simulated experiments with a robotic manipulator will be carried
out to validate the proposed algorithm.Comment: 16 pages, 8 figure
Pre-training also Transfers Non-Robustness
Pre-training has enabled state-of-the-art results on many tasks. In spite of
its recognized contribution to generalization, we observed in this study that
pre-training also transfers adversarial non-robustness from pre-trained model
into fine-tuned model in the downstream tasks. Using image classification as an
example, we first conducted experiments on various datasets and network
backbones to uncover the adversarial non-robustness in fine-tuned model.
Further analysis was conducted on examining the learned knowledge of fine-tuned
model and standard model, and revealed that the reason leading to the
non-robustness is the non-robust features transferred from pre-trained model.
Finally, we analyzed the preference for feature learning of the pre-trained
model, explored the factors influencing robustness, and introduced a simple
robust pre-traning solution
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