THE EFFECTS OF THINK-ALOUD STRATEGY ON EFL YOUNG LEARNERS’ READING SKILL PRACTICE

Think-aloud is described as a strategy in which students verbalize their thoughts while they read. This strategy is used to help students monitor their own thinking and comprehend the text. This current study aimed to examine the effects of the think-aloud strategy on EFL young learners’ reading skill practice and identify their attitudes towards the think-aloud strategy used by the teacher in teaching at a foreign language center in Can Tho city (Southern Vietnam’s Mekong Delta region). This study was experimental research using both quantitative and qualitative approaches. The quantitative approach was used to investigate the effects of the think-aloud strategy on young learners’ reading skill practice while the qualitative data was collected to understand the young learners’ attitudes towards the use of the think-aloud strategy in teaching and learning reading skills. Pre-test, post-test, and semi-structured interviews were used to collect the data. The study was conducted with one group of participants. The participants included 25 students who were at the age of 10-12 years old. The results from the pre-test and post-test showed that there was a significant difference in the students‘ reading comprehension performance after the intervention. Thus, the think-aloud strategy has a great impact on the improvement of students’ reading comprehension. Besides, there was no difference in reading comprehension achievement between males and females before and after the treatment. Especially, the results from the semi-structured interview revealed that all students had positive attitudes towards the think-aloud strategy.  Article visualizations

Hierarchical Policy Learning for Mechanical Search

Retrieving objects from clutters is a complex task, which requires multiple interactions with the environment until the target object can be extracted. These interactions involve executing action primitives like grasping or pushing as well as setting priorities for the objects to manipulate and the actions to execute. Mechanical Search (MS) is a framework for object retrieval, which uses a heuristic algorithm for pushing and rule-based algorithms for high-level planning. While rule-based policies profit from human intuition in how they work, they usually perform sub-optimally in many cases. Deep reinforcement learning (RL) has shown great performance in complex tasks such as taking decisions through evaluating pixels, which makes it suitable for training policies in the context of object-retrieval. In this work, we first formulate the MS problem in a principled formulation as a hierarchical POMDP. Based on this formulation, we propose a hierarchical policy learning approach for the MS problem. For demonstration, we present two main parameterized sub-policies: a push policy and an action selection policy. When integrated into the hierarchical POMDP's policy, our proposed sub-policies increase the success rate of retrieving the target object from less than 32% to nearly 80%, while reducing the computation time for push actions from multiple seconds to less than 10 milliseconds.Comment: ICRA 202

A Covariance Matrix Adaptation Evolution Strategy for Direct Policy Search in Reproducing Kernel Hilbert Space

The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well defined parameter space. In some problems, such parameter spaces are defined using function approximation in which feature functions are manually defined. Therefore, the performance of those techniques strongly depends on the quality of chosen features. Hence, enabling CMA-ES to optimize on a more complex and general function class of the objective has long been desired. Specifically, we consider modeling the input space for black-box optimization in reproducing kernel Hilbert spaces (RKHS). This modeling leads to a functional optimization problem whose domain is a function space that enables us to optimize in a very rich function class. In addition, we propose CMA-ES-RKHS, a generalized CMA-ES framework, that performs black-box functional optimization in the RKHS. A search distribution, represented as a Gaussian process, is adapted by updating both its mean function and covariance operator. Adaptive representation of the function and covariance operator is achieved with sparsification techniques. We evaluate CMA-ES-RKHS on a simple functional optimization problem and bench-mark reinforcement learning (RL) domains. For an application in RL, we model policies for MDPs in RKHS and transform a cumulative return objective as a functional of RKHS policies, which can be optimized via CMA-ES-RKHS. This formulation results in a black-box functional policy search framework

Opportunistic secure transmission for wireless relay networks with modify-and-forward protocol

This paper investigates the security at the physical layer in cooperative wireless networks (CWNs) where the data transmission between nodes can be realised via either direct transmission (DT) or relaying transmission (RT) schemes. Inspired by the concept of physical-layer network coding (PNC), a secure PNC-based modify-and-forward (SPMF) is developed to cope with the imperfect shared knowledge of the message modification between relay and destination in the conventional modify-and-forward (MF). In this paper, we first derive the secrecy outage probability (SOP) of the SPMF scheme, which is shown to be a general expression for deriving the SOP of any MF schemes. By comparing the SOPs of various schemes, the usage of the relay is shown to be not always necessary and even causes a poorer performance depending on target secrecy rate and quality of channel links. To this extent, we then propose an opportunistic secure transmission protocol to minimise the SOP of the CWNs. In particular, an optimisation problem is developed in which secrecy rate thresholds (SRTs) are determined to find an optimal scheme among various DT and RT schemes for achieving the lowest SOP. Furthermore, the conditions for the existence of SRTs are derived with respect to various channel conditions to determine if the relay could be relied on in practice

DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes

Robotic grasping is a fundamental skill required for object manipulation in robotics. Multi-fingered robotic hands, which mimic the structure of the human hand, can potentially perform complex object manipulation. Nevertheless, current techniques for multi-fingered robotic grasping frequently predict only a single grasp for each inference time, limiting computational efficiency and their versatility, i.e. unimodal grasp distribution. This paper proposes a differentiable multi-fingered grasp generation network (DMFC-GraspNet) with three main contributions to address this challenge. Firstly, a novel neural grasp planner is proposed, which predicts a new grasp representation to enable versatile and dense grasp predictions. Secondly, a scene creation and label mapping method is developed for dense labeling of multi-fingered robotic hands, which allows a dense association of ground truth grasps. Thirdly, we propose to train DMFC-GraspNet end-to-end using using a forward-backward automatic differentiation approach with both a supervised loss and a differentiable collision loss and a generalized Q 1 grasp metric loss. The proposed approach is evaluated using the Shadow Dexterous Hand on Mujoco simulation and ablated by different choices of loss functions. The results demonstrate the effectiveness of the proposed approach in predicting versatile and dense grasps, and in advancing the field of multi-fingered robotic grasping.Comment: Submitted IROS 2023 workshop "Policy Learning in Geometric Spaces

SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects

To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects. Most existing approaches have difficulties to extend predictions to scenarios where novel object instances are continuously introduced, especially with heavy occlusions. In this work, we propose a few-shot pose estimation (FSPE) approach called SA6D, which uses a self-adaptive segmentation module to identify the novel target object and construct a point cloud model of the target object using only a small number of cluttered reference images. Unlike existing methods, SA6D does not require object-centric reference images or any additional object information, making it a more generalizable and scalable solution across categories. We evaluate SA6D on real-world tabletop object datasets and demonstrate that SA6D outperforms existing FSPE methods, particularly in cluttered scenes with occlusions, while requiring fewer reference images

FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion

Sensor fusion can significantly improve the performance of many computer vision tasks. However, traditional fusion approaches are either not data-driven and cannot exploit prior knowledge nor find regularities in a given dataset or they are restricted to a single application. We overcome this shortcoming by presenting a novel deep hierarchical variational autoencoder called FusionVAE that can serve as a basis for many fusion tasks. Our approach is able to generate diverse image samples that are conditioned on multiple noisy, occluded, or only partially visible input images. We derive and optimize a variational lower bound for the conditional log-likelihood of FusionVAE. In order to assess the fusion capabilities of our model thoroughly, we created three novel datasets for image fusion based on popular computer vision datasets. In our experiments, we show that FusionVAE learns a representation of aggregated information that is relevant to fusion tasks. The results demonstrate that our approach outperforms traditional methods significantly. Furthermore, we present the advantages and disadvantages of different design choices.Comment: Accepted at ECCV 202

Multi-Arm Bin-Picking in Real-Time: A Combined Task and Motion Planning Approach

Automated bin-picking is a prerequisite for fully automated manufacturing and warehouses. To successfully pick an item from an unstructured bin the robot needs to first detect possible grasps for the objects, decide on the object to remove and consequently plan and execute a feasible trajectory to retrieve the chosen object. Over the last years significant progress has been made towards solving these problems. However, when multiple robot arms are cooperating the decision and planning problems become exponentially harder. We propose an integrated multi-arm bin-picking pipeline (IMAPIP), and demonstrate that it is able to reliably pick objects from a bin in real-time using multiple robot arms. IMAPIP solves the multi-arm bin-picking task first at high-level using a geometry-aware policy integrated in a combined task and motion planning framework. We then plan motions consistent with this policy using the BIT* algorithm on the motion planning level. We show that this integrated solution enables robot arm cooperation. In our experiments, we show the proposed geometry-aware policy outperforms a baseline by increasing bin-picking time by 28\% using two robot arms. The policy is robust to changes in the position of the bin and number of objects. We also show that IMAPIP to successfully scale up to four robot arms working in close proximity.Comment: 8 page

Beamforming in coexisting wireless systems with uncertain channel state information

This paper considers an underlay access strategy for coexisting wireless networks where the secondary system utilizes the primary spectrum to serve its users. We focus on the practical cases where there is uncertainty in the estimation of channel state information (CSI). Here the throughput performance of each system is limited by the interference imposed by the other, resulting in conflicting objectives. We first analyze the fundamental tradeoff between the tolerance interference level at the primary system and the total achievable throughput of the secondary users. We then introduce a beamforming design problem as a multiobjective optimization to minimize the interference imposed on each of the primary users while maximizing the intended signal received at every secondary user, taking into account the CSI uncertainty. We then map the proposed optimization problem to a robust counterpart under the maximum CSI estimation error. The robust counterpart is then transformed into a standard convex semi-definite programming. Simulation results confirm the effectiveness of the proposed scheme against various levels of CSI estimation error. We further show that in the proposed approach, the trade-off in the two systems modelled by Pareto frontier can be engineered by adjusting system parameters. For instance, the simulations show that at the primary system interference thresholds of -10 dBm (-5 dBm) by increasing number of antennas from 4 to 12, the secondary system throughput is increased by 3.3 bits/s/channel-use (5.3 bits/s/channel-use