240 research outputs found
ANALYSIS OF THE EFFECTIVENESS OF FINANCIAL AND MONETARY CHANNELS AND CREDIT CHANNELS ON CONSUMERSâ MENTAL ANXIETY
ANALYSIS OF THE EFFECTIVENESS OF FINANCIAL AND MONETARY CHANNELS AND CREDIT CHANNELS ON CONSUMERSâ MENTAL ANXIETY
Restructuring multimodal corrective feedback through Augmented Reality (AR)-enabled videoconferencing in L2 pronunciation teaching
The problem of cognitive overload is particularly pertinent in multimedia L2 classroom corrective feedback (CF), which involves rich communicative tools to help the class to notice the mismatch between the target input and learnersâ pronunciation. Based on multimedia design principles, this study developed a new multimodal CF model through augmented reality (AR)-enabled videoconferencing to eliminate extraneous cognitive load and guide learnersâ attention to the essential material. Using a quasi-experimental design, this study aims to examine the effectiveness of this new CF model in improving Chinese L2 studentsâ segmental production and identification of the targeted English consonants (dark /É«/, /Ă°/and /Ξ/), as well as their attitudes towards this application. Results indicated that the online multimodal CF environment equipped with AR annotation and filters played a significant role in improving the participantsâ production of the target segments. However, this advantage was not found in the auditory identification tests compared to the offline CF multimedia class. In addition, the learners reported that the new CF model helped to direct their attention to the articulatory gestures of the student being corrected, and enhance the class efficiency. Implications for computer-assisted pronunciation training and the construction of online/offline multimedia learning environments are also discussed
A Novel Perception and Semantic Mapping Method for Robot Autonomy in Orchards
In this work, we propose a novel framework for achieving robotic autonomy in
orchards. It consists of two key steps: perception and semantic mapping. In the
perception step, we introduce a 3D detection method that accurately identifies
objects directly on point cloud maps. In the semantic mapping step, we develop
a mapping module that constructs a visibility graph map by incorporating
object-level information and terrain analysis. By combining these two steps,
our framework improves the autonomy of agricultural robots in orchard
environments. The accurate detection of objects and the construction of a
semantic map enable the robot to navigate autonomously, perform tasks such as
fruit harvesting, and acquire actionable information for efficient agricultural
production
Energy Efficient Robust Beamforming for Vehicular ISAC with Imperfect Channel Estimation
This paper investigates robust beamforming for system-centric energy
efficiency (EE) optimization in the vehicular integrated sensing and
communication (ISAC) system, where the mobility of vehicles poses significant
challenges to channel estimation. To obtain the optimal beamforming under
channel uncertainty, we first formulate an optimization problem for maximizing
the system EE under bounded channel estimation errors. Next, fractional
programming and semidefinite relaxation (SDR) are utilized to relax the rank-1
constraints. We further use Schur complement and S-Procedure to transform
Cramer-Rao bound (CRB) and channel estimation error constraints into convex
forms, respectively. Based on the Lagrangian dual function and
Karush-Kuhn-Tucker (KKT) conditions, it is proved that the optimal beamforming
solution is rank-1. Finally, we present comprehensive simulation results to
demonstrate two key findings: 1) the proposed algorithm exhibits a favorable
convergence rate, and 2) the approach effectively mitigates the impact of
channel estimation errors.Comment: Submitted to IEEE for future publicatio
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Vertical Federated Learning (FL) is a new paradigm that enables users with
non-overlapping attributes of the same data samples to jointly train a model
without directly sharing the raw data. Nevertheless, recent works show that
it's still not sufficient to prevent privacy leakage from the training process
or the trained model. This paper focuses on studying the privacy-preserving
tree boosting algorithms under the vertical FL. The existing solutions based on
cryptography involve heavy computation and communication overhead and are
vulnerable to inference attacks. Although the solution based on Local
Differential Privacy (LDP) addresses the above problems, it leads to the low
accuracy of the trained model.
This paper explores to improve the accuracy of the widely deployed tree
boosting algorithms satisfying differential privacy under vertical FL.
Specifically, we introduce a framework called OpBoost. Three order-preserving
desensitization algorithms satisfying a variant of LDP called distance-based
LDP (dLDP) are designed to desensitize the training data. In particular, we
optimize the dLDP definition and study efficient sampling distributions to
further improve the accuracy and efficiency of the proposed algorithms. The
proposed algorithms provide a trade-off between the privacy of pairs with large
distance and the utility of desensitized values. Comprehensive evaluations show
that OpBoost has a better performance on prediction accuracy of trained models
compared with existing LDP approaches on reasonable settings. Our code is open
source
- âŠ