Reviewerly/exHarmony huggingface dataset:
VCRNet: Visual Compensation Restoration Network for No-Reference Image Quality Assessment

Guided by the free-energy principle, generative adversarial networks (GAN)-based no-reference image quality assessment (NR-IQA) methods have improved the image quality prediction accuracy. However, the GAN cannot well handle the restoration task for the free-energy principle-guided NR-IQA methods, especially for the severely destroyed images, which results in that the quality reconstruction relationship between the distorted image and its restored image cannot be accurately built. To address this problem, a visual compensation restoration network (VCRNet)-based NR-IQA method is proposed, which uses a non-adversarial model to efficiently handle the distorted image restoration task. The proposed VCRNet consists of a visual restoration network and a quality estimation network. To accurately build the quality reconstruction relationship between the distorted image and its restored image, a visual compensation module, an optimized asymmetric residual block, and an error map-based mixed loss function, are proposed for increasing the restoration capability of the visual restoration network. For further addressing the NR-IQA problem of severely destroyed images, the multi-level restoration features which are obtained from the visual restoration network are used for the image quality estimation. To prove the effectiveness of the proposed VCRNet, seven representative IQA databases are used, and experimental results show that the proposed VCRNet achieves the state-of-the-art image quality prediction accuracy. The implementation of the proposed VCRNet has been released at https://github.com/NUIST-Videocoding/VCRNet.
Long Short-Term Memory Recurrent Neural Network for Automatic Speech Recognition

Automatic speech recognition (ASR) is one of the most demanding tasks in natural language processing owing to its complexity.Recently, deep learning approaches have been deployed for this task and have been proven to outperform traditional machine learning approaches such as Artificial Neural Network (ANN).In particular, deep-learning methods such as long short-term memory (LSTM) have achieved improved ASR performance.However, this method is limited to processing continuous input streams.Traditional LSTM requires four (4) linear layers (multilayer perceptron (MLP) layer) per cell with a large memory bandwidth for each sequence time step.LSTM cannot accommodate the many computational units required for processing continuous input streams because the system does not have sufficient memory bandwidth to feed the computational units.In this study, an enhanced deep learning LSTM recurrent neural network (RNN) model was proposed to resolve this shortcoming.In the proposed model, the RNN is incorporated as a ''forget gate'' to the memory block to allow the resetting of cell states at the beginning of the sub-sequences.This enables the system to process continuous input streams efficiently without necessarily increasing the required bandwidths.In the proposed model, the standard architecture of the LSTM network is modified to effectively use the model parameters.Some CNN-based and sequential models were used on the same dataset, and the models were compared with the proposed model.LSTM-RNN outperformed the other deep learning models with an accuracy of 99.36% on the well-established public benchmark spoken English digit dataset.
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability, especially when working with large, high-dimensional datasets. To address these issues, we present a simple yet effective algorithm called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECOD</small> (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the “rare events” that appear in the tails of a distribution. In a nutshell, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECOD</small> first estimates the underlying distribution of the input data in a nonparametric fashion by computing the empirical cumulative distribution per dimension of the data. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECOD</small> then uses these empirical distributions to estimate tail probabilities per dimension for each data point. Finally, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECOD</small> computes an outlier score of each data point by aggregating estimated tail probabilities across dimensions. Our contributions are as follows: (1) we propose a novel outlier detection method called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECOD</small> , which is both parameter-free and easy to interpret; (2) we perform extensive experiments on 30 benchmark datasets, where we find that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECOD</small> outperforms 11 state-of-the-art baselines in terms of accuracy, efficiency, and scalability; and (3) we release an easy-to-use and scalable (with distributed support) Python implementation for accessibility and reproducibility.
A Multiscale Framework With Unsupervised Learning for Remote Sensing Image Registration

Registration for multisensor or multimodal image pairs with a large degree of distortions is a fundamental task for many remote sensing applications. To achieve accurate and low-cost remote sensing image registration, we propose a multiscale framework with unsupervised learning, named MU-Net. Without costly ground truth labels, MU-Net directly learns the end-to-end mapping from the image pairs to their transformation parameters. MU-Net stacks several deep neural network (DNN) models on multiple scales to generate a coarse-to-fine registration pipeline, which prevents the backpropagation from falling into a local extremum and resists significant image distortions. We design a novel loss function paradigm based on structural similarity, which makes MU-Net suitable for various types of multimodal images. MU-Net is compared with traditional feature-based and area-based methods, as well as supervised and other unsupervised learning methods on the optical-optical, optical-infrared, optical-synthetic aperture radar (SAR), and optical-map datasets. Experimental results show that MU-Net achieves more comprehensive and accurate registration performance between these image pairs with geometric and radiometric distortions. We share the code implemented by Pytorch at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yeyuanxin110/MU-Net</uri> .
A Two-Phase Learning-Based Swarm Optimizer for Large-Scale Optimization

In this article, a simple yet effective method, called a two-phase learning-based swarm optimizer (TPLSO), is proposed for large-scale optimization. Inspired by the cooperative learning behavior in human society, mass learning and elite learning are involved in TPLSO. In the mass learning phase, TPLSO randomly selects three particles to form a study group and then adopts a competitive mechanism to update the members of the study group. Then, we sort all of the particles in the swarm and pick out the elite particles that have better fitness values. In the elite learning phase, the elite particles learn from each other to further search for more promising areas. The theoretical analysis of TPLSO exploration and exploitation abilities is performed and compared with several popular particle swarm optimizers. Comparative experiments on two widely used large-scale benchmark datasets demonstrate that the proposed TPLSO achieves better performance on diverse large-scale problems than several state-of-the-art algorithms.
Semantics Disentangling for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training. To bridge the gap between the seen and unseen classes, most GZSL methods attempt to associate the visual features of seen classes with attributes or to generate unseen samples directly. Nevertheless, the visual features used in the prior approaches do not necessarily encode semantically related information that the shared attributes refer to, which degrades the model generalization to unseen classes. To address this issue, in this paper, we propose a novel semantics disentangling framework for the generalized zero-shot learning task (SDGZSL), where the visual features of unseen classes are firstly estimated by a conditional VAE and then factorized into semantic-consistent and semantic-unrelated latent vectors. In particular, a total correlation penalty is applied to guarantee the independence between the two factorized representations, and the semantic consistency of which is measured by the derived relation network. Extensive experiments conducted on four GZSL benchmark datasets have evidenced that the semantic-consistent features disentangled by the proposed SDGZSL are more generalizable in tasks of canonical and generalized zero-shot learning. Our source code is available at https://github.com/uqzhichen/SDGZSL.
Medical students’ attitudes and perceptions towards the effectiveness of mobile learning: A comparative information-need perspective

The rapid development of information and communication technologies has revolutionized the lifestyles and learning practices of the younger population worldwide. Various new mobile platforms and forms of social media have been so pervasive and influential in the world of higher education that they have contributed much to the training of the next generation of medical professionals. As such, the current study aimed to compare the adoption of mobile learning amongst three groups of medical science students at the University of Hong Kong – namely, students majoring in Clinical Science, Chinese Medicine and Nursing. For this study, the authors used a questionnaire survey to collect a total of 150 responses. The data was analysed using descriptive statistics, Pearson’s correlation test and multiple regression analysis. The results from the study revealed that the students in the three different medical majors at the University of Hong Kong engaged with their mobile devices at slightly different levels. Although a few significant differences were found, Clinical Science students tended to have more diverse information needs and use their mobile devices for a variety of learning-related activities. In comparison, Chinese Medicine students indicated that they were less active users of mobile devices in terms of both learning and non-learning activities.
Dynamic Outsourced Auditing Services for Cloud Storage Based on Batch-Leaves-Authenticated Merkle Hash Tree

Cloud computing encourages users to outsource their data to cloud storage. Data outsourcing means that users lose physical autonomy on their own data, which makes remote data integrity verification become a critical challenge for potential cloud users. To free user from the burden incurred by frequent integrity verifications, Third Party Auditor (TPA) is introduced to perform verifications on behalf of user for data integrity assurance. However, existing public auditing schemes rely on the assumption that TPA is trusted, thus these schemes cannot be directly extended to support the outsourced auditing model, where TPA might be dishonest and any two of the three involved entities (i.e. user, TPA, and cloud service provider) might be in collusion. In this paper, we propose a dynamic outsourced auditing scheme which cannot only protect against any dishonest entity and collision, but also support verifiable dynamic updates to outsourced data. We present a new approach, based on batch-leaves-authenticated Merkle Hash Tree (MHT), to batch-verify multiple leaf nodes and their own indexes all together, which is more appropriate for the dynamic outsourced auditing system than traditional MHT-based dynamism approaches that can only verify many leaf nodes one by one. Experimental results show that our solution minimizes the costs of initialization for both user and TPA (compared to existing static outsourced auditing scheme), and incurs a lower price of dynamism at user side.
Feature-based Compositing Memory Networks for Aspect-based Sentiment Classification in Social Internet of Things

Sentiment analysis is an important research field in natural language processing. Aspect-based sentiment classification can efficiently solve fine-grained sentiment recognition, however, its classification accuracy becomes decreasing for large-scale corpus. To solve this problem, we propose a new memory network model, called Feature-based Compositing Memory Networks (FCMN). Differing from typical memory networks, we extract three kinds of features to enrich the word representation of each context word. We design compositing strategies combining feature representations and word embedding to improve the performance of attention mechanism. Experiments on laptops and restaurants datasets in SemEval 2014 show that our approach outperforms the feature-based SVM, TD-LSTM and Deep Memory Networks. Especially, FCMN gets better results with less hops than Deep Memory Networks. Experiments results demonstrate that FCMN can ignore words without sentiment and pay more attention on correct words in a sentence.
Boosting deep attribute learning via support vector regression for fast moving crowd counting

Crowd counting has recently attracted extensive attention in research. However, the existing research mainly focuses on investigating crowd counting of static or slow moving crowd estimating, while fast moving crowd counting is left unexplored. The fast moving crowd counting is indeed extremely important for urban public safety management. In this paper, we propose a novel more effective fast moving crowd counting algorithm. The proposed approach utilizes support vector regression and spatial-temporal multi-features to boost deep cumulative attribute learning. To this end, first a novel spatial-temporal multi-feature is proposed by joining super-pixel based multi-appearance features and multi-motion features to solve fast moving crowd counting. Second, a novel deep accumulated attributes learning architecture is proposed based on very deep learning architecture VGG16. Third, a novel boosting deep attribute Learning algorithm is proposed based on late fusion of proposed deep cumulative attribute learning and proposed spatial-temporal multi-features based support vector regression for improving predication performance of deep learning. We perform corresponding experiments on three public datasets including UCSD dataset, PEST2009 dataset and Mall dataset. The experimental results demonstrate that proposed Boosting DAL-SVR method is effective to cover the shortage of deep learning in solving regression problems. Meanwhile it demonstrates that proposed Boosting DAL-SVR is more effective and robust rather than other state-of-the art methods for fast moving crowd counting problem.
CROification: Accurate Kernel Classification with the Efficiency of Sparse Linear SVM

Kernel methods have been shown to be effective for many machine learning tasks such as classification and regression. In particular, support vector machines with the Gaussian kernel have proved to be powerful classification tools. The standard way to apply kernel methods is to use the kernel trick, where the inner product of the vectors in the feature space is computed via the kernel function. Using the kernel trick for SVMs, however, leads to training that is quadratic in the number of input vectors and classification that is linear with the number of support vectors. We introduce a new kernel, the CRO (Concomitant Rank Order) kernel that approximates the Gaussian kernel on the unit sphere. We also introduce a randomized feature map, called the CRO feature map that produces sparse, high-dimensional feature vectors whose inner product asymptotically equals the CRO kernel. Using the Discrete Cosine Transform for computing the CRO feature map ensures that the cost of computing feature vectors is low, allowing us to compute the feature map explicitly. Combining the CRO feature map with linear SVM we introduce the CROification algorithm which gives us the efficiency of a sparse high-dimensional linear SVM with the accuracy of the Gaussian kernel SVM.
A Clustering-Based Nonlinear Ensemble Approach for Exchange Rates Forecasting

A clustering-based nonlinear ensemble (CNE) learning approach is proposed in this paper to forecast exchange rates. In the proposed CNE learning approach: 1) a self-organizing map neural network is introduced to cluster the in-sample component forecasts; 2) kernel-based extreme learning machine is employed to calculate the in-sample ensemble weights for each cluster; and 3) the corresponding clusters’ in-sample ensemble weights are used for out-of-sample component forecasts to obtain the ensemble forecasts. To illustrate and verify the effectiveness of our proposed model, we test its directional and level forecasting accuracy using four major exchange rates. The out-of-sample forecasting performance results show that the proposed CNE learning approach consistently outperforms the component models and other ensemble learning approaches in terms of the directional forecasting accuracy and the level forecasting accuracy.
ML-HDP: A Hierarchical Bayesian Nonparametric Model for Recognizing Human Actions in Video

Action recognition from videos is an important area of computer vision research due to its various applications, ranging from visual surveillance to human-computer interaction. To address action recognition problems, this paper presents a framework that jointly models multiple complex actions and motion units at different hierarchical levels. We achieve this by proposing a generative topic model, namely, multi-label hierarchical Dirichlet process (ML-HDP). The ML-HDP model formulates the co-occurrence relationship of actions and motion units, and enables highly accurate recognition. In particular, our topic model possesses the three-level representation in action understanding, where low-level local features are connected to high-level actions via mid-level atomic actions. This allows the recognition model to work discriminatively. In our ML-HDP, atomic actions are treated as latent topics and automatically discovered from data. In addition, we incorporate the notion of class labels into our model in a semi-supervised fashion to effectively learn and infer multi-labeled videos. Using discovered topics and inferred labels, which are jointly assigned to local features, we present the straightforward methods to perform three recognition tasks including action classification, joint classification and segmentation of continuous actions, and spatiotemporal action localization. In experiments, we explore the use of three different features and demonstrate the effectiveness of our proposed approach for these tasks on four public datasets: KTH, MSR-II, Hollywood2, and UCF101.
Accurate detection of sitting posture activities in a secure IoT based assisted living environment

In this work, we present a technique as well as a dataset for improving daily life assistive activities in a smart Internet of Things (IoT) driven environment. We propose that augmenting data from multiple sensing devices such as Microsoft Kinect and Smartwatches can significantly improve the detection performance once incorporated in the context of an IoT framework. Kinect, being the feature-wise richest input device in the IoT world, is a favorite pick of most of the researchers for detecting postural activities. However, there are certain activity classes in IoT based smart environments on which Kinect based solutions result in high misclassifications. This is due to the similarities in 3D joint position space. For such scenarios, Kinect must be augmented with additional sensor(s) to achieve the desired level of accuracy. In this work, we improve the detection of the assistive activities related to sitting posture in general and dining-related activities in particular. Our research focus is to enable a robot to understand the activities of a human at the dining table and plan the assistive tasks accordingly. This is a two-step process; in the first step, Kinect sensor data is augmented with a collection of motion sensors’ data. Then, this data is analyzed for discrimination power through cross-validation of the Hidden Markov Model (HMM). In addition, we propose a two-level security scheme consisting of key establishment and two-factor authentication for the IoT based activity recognition environment. Our experiments show that Kinect, when complemented by motion sensors’ data, reduces the confusion instances by up to 12% on average. Moreover, we demonstrate the data quality through clustering properties of the data using an unsupervised neural network.
Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-Operative Prostate Cancer

In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.
Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health data

The adoption of advanced technologies in the healthcare sector has brought about many improvements in the industry, including better communication between healthcare providers, improved quality of treatment, and reduced cost. For the most part, these improvements have come about due to collaboration between healthcare providers and the sharing of healthcare data. However, this introduces various security and privacy concerns pertaining to the data in question. Preserving the privacy of the patients while simultaneously sharing data that would facilitate medical research is absolutely essential, for it is not just an ethical requirement but is also dictated by the regulations. In this paper, we propose a new anonymization scheme of data privacy for e-health records which differs from existing approaches in its ability to prevent from identity disclosure even faced with adversaries having pertinent background knowledge. The proposed scheme is based on transforming data into fixed intervals and then replacing original values with averages. As a result, the proposed scheme offers improved data privacy and utility in privacy preserving data publishing. The simulation results show the effectiveness of the scheme and verify the aforementioned claims.