Incidental learning of L2 technical vocabulary through repeated reading of academic texts

This is a case study investigating the effect of repeated reading of academic texts on incidental learning of L2 technical vocabulary. The impact of factors such as frequency of encounters and pictorial contexts on incidental learning of L2 technical vocabulary was also examined. Five adult EAP students were required to read academic texts from different disciplines multiple number of times. Participants’ vocabulary gains in receptive knowledge of form-meaning connection and productive knowledge of written form were measured. The results indicated that repeated reading of academic texts promotes incidental learning of L2 technical vocabulary. However, it is also necessary to be aware the impact of individual difference on incidental learning of L2 technical vocabulary. No correlation was found between either frequency of encounters or existence of pictorial contexts and vocabulary gains

Generative Adversarial Network for Photoplethysmography Reconstruction

Photoplethysmography (PPG) is an optical measurement method for blood pulse wave monitoring. The method has been widely applied in both clinical and wearable devices to collect physiological parameters, such as heart rate (HR) and heart rate variability (HRV). Unfortunately, the PPG signals are very vulnerable to motion artifacts, caused by inevitable movements of human users. To obtain reliable results from PPG-based monitoring, methods to denoise the PPG signals are necessary. Methods proposed in the literature, including signal decomposition, time-series analysis, and deep-learning based methods, reduce the effect of noise in PPG signals. However, their performance is insufficient for low signal-to-noise ratio PPG signals, or limited to noise from certain types of activities. Therefore, the aim of this study is to develop a method to remove the motion artifacts and reconstruct noisy PPG signals without any prior knowledge about the noise. In this thesis, a deep convolutional generative adversarial network (DC-GAN) based method is proposed to reconstruct the PPG signals corrupted by real-world motion artifacts. The proposed method leverages the temporal information from the distorted signal and its preceding data points to obtain the clean PPG signal. A GAN-based model is trained to generate succeeding clean PPG signals by previous data points. A sliding window moving at a fixed step on the noisy signal is used to select and update the input for the trained model by the information within the noisy signal. A PPG dataset collected by smartwatches in a health monitoring study is used to train, validate, and test the method in this study. A noisy dataset generated with real-world motion artifacts of different noise levels and lengths is used to evaluate the proposed and baseline methods. Three state-of-the-art PPG reconstruction methods are compared with our method. Two metrics, including maximum peak-to-peak error and RMSSD error, are extracted from the original and reconstructed signals to estimate the reconstruction error for HR and HRV. Our method outperforms state-of-the-art methods with the lowest values of the two evaluation matrices at all noise levels and lengths. The proposed method achieves 0.689, 1.352 and 1.821 seconds of maximum peak-to-peak errors for 5-second, 10-second, and 15-second noise at the highest noise level, respectively, and achieves 0.021, 0.048 and 0.067 seconds of RMSSD errors for the same noise cases. Consequently, our method performs the best in reconstructing distorted PPG signals and provides reliable estimation for both HR and HRV

Repulsion Loss: Detecting Pedestrians in a Crowd

Detecting individual pedestrians in a crowd remains a challenging problem since the pedestrians often gather together and occlude each other in real-world scenarios. In this paper, we first explore how a state-of-the-art pedestrian detector is harmed by crowd occlusion via experimentation, providing insights into the crowd occlusion problem. Then, we propose a novel bounding box regression loss specifically designed for crowd scenes, termed repulsion loss. This loss is driven by two motivations: the attraction by target, and the repulsion by other surrounding objects. The repulsion term prevents the proposal from shifting to surrounding objects thus leading to more crowd-robust localization. Our detector trained by repulsion loss outperforms all the state-of-the-art methods with a significant improvement in occlusion cases.Comment: Accepted to IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 201

Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models

To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service (QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition (K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bag-QoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoS-words, Locality Constrained Feature Coding (LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines (SVM) classifier. Our experimental results demonstrate the feasibility of the proposed classification method

A Two-Sided Quaternion Higher-Order Singular Value Decomposition

Higher-order singular value decomposition (HOSVD) is one of the most celebrated tensor decompositions that generalizes matrix SVD to higher-order tensors. It was recently extended to the quaternion domain \cite{miao2023quat} (we refer to it as L-QHOSVD in this work). However, due to the non-commutativity of quaternion multiplications, L-QHOSVD is not consistent with matrix SVD when the order of the quaternion tensor reduces to $2$; moreover, theoretical guaranteed truncated L-QHOSVD was not investigated. To derive a more natural higher-order generalization of the quaternion matrix SVD, we first utilize the feature that left and right multiplications of quaternions are inconsistent to define left and right quaternion tensor unfoldings and left and right mode-$k$ products. Then, by using these basic tools, we propose a two-sided quaternion higher-order singular value decomposition (TS-QHOSVD). TS-QHOSVD has the following two main features: 1) it computes two factor matrices at a time from SVDs of left and right unfoldings, inheriting certain parallel properties of the original HOSVD; 2) it is consistent with matrix SVD when the order of the tensor is $2$. In addition, we study truncated TS-QHOSVD and establish its error bound measured by the tail energy; correspondingly, we also present truncated L-QHOSVD and its error bound. Deriving the error bounds is nontrivial, as the proofs are more complicated than their real counterparts, again due to the non-commutativity of quaternion multiplications. %Numerical experiments on synthetic and color video data show the efficacy of the proposed TS-QHOSVD. Finally, we illustrate the derived properties of TS-QHOSVD and its efficacy via some numerical examples

Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect

How to handle time features shall be the core question of any time series forecasting model. Ironically, it is often ignored or misunderstood by deep-learning based models, even those baselines which are state-of-the-art. This behavior makes their inefficient, untenable and unstable. In this paper, we rigorously analyze three prevalent but deficient/unfounded deep time series forecasting mechanisms or methods from the view of time series properties, including normalization methods, multivariate forecasting and input sequence length. Corresponding corollaries and solutions are given on both empirical and theoretical basis. We thereby propose a novel time series forecasting network, i.e. RTNet, on the basis of aforementioned analysis. It is general enough to be combined with both supervised and self-supervised forecasting format. Thanks to the core idea of respecting time series properties, no matter in which forecasting format, RTNet shows obviously superior forecasting performances compared with dozens of other SOTA time series forecasting baselines in three real-world benchmark datasets. By and large, it even occupies less time complexity and memory usage while acquiring better forecasting accuracy. The source code is available at https://github.com/OrigamiSL/RTNet