Reviewerly/exHarmony huggingface dataset:
Semi-supervised node classification via adaptive graph smoothing networks

Inspections on current graph neural networks suggest us to reconsider the computational aspect of the final aggregation. We consider that such aggregations perform a prediction smoothing and impute their potential drawbacks to be the inter-class interference implied by the underlying graphs. We aim at weakening the inter-class connections so that aggregations focus more on intra-class relations and producing smooth predictions according to weakening results. We apply a metric learning module to learn new edge weights and combine entropy losses to ensure the correspondence between the predictions and the learnt distances so that the weights of inter-class edges are reduced and predictions are smoothed according to the modified graph. Experiments on four citation networks and a Wiki network show that in comparison with other state-of-the-art graph neural networks, the proposed algorithm can improve the classification accuracy.
CNN-LSTM neural network model for fine-grained negative emotion computing in emergencies

In the Web 2.0 era, governments are facing the challenge of analyzing the emotional tendency of online public opinion during emergencies to regulate people’s emotions more effectively and maintain social stability. When dealing with large-scale short, unordered texts and extracting text features, the existing studies often face the problem of sparse features, ignoring fine-grained negative emotions. Aiming at those drawbacks and inspired by the dependency relationship among Chinese words, an emotion computing algorithm based on a binary tree is designed to assign words with emotional intensity. Then, the paper proposes a CNN-LSTM model for Chinese language sentiment classification to conduct local feature extraction and maintain long-term dependencies. The proposed model is validated using different traditional models and classifiers. The results show that the CNN-LSTM model achieved competitive classification performance. Finally, our approach was applied to practical emergency management problems, exploring the impact of government information release on negative emotion regulation to test its reliability. The experimental results validated that compared with traditional methods, this approach improved the accuracy of sentiment classification and possesses higher classification performance. The empirical analysis demonstrated that the CNN-LSTM method was rapid, effective and feasible and could be more suitable for optimizing emotion regulation policies.
A Comprehensive Survey of Grammatical Error Correction

Grammatical error correction (GEC) is an important application aspect of natural language processing techniques, and GEC system is a kind of very important intelligent system that has long been explored both in academic and industrial communities. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning. However, there is not a survey that untangles the large amount of research works and progress in this field. We present the first survey in GEC for a comprehensive retrospective of the literature in this area. We first give the definition of GEC task and introduce the public datasets and data annotation schema. After that, we discuss six kinds of basic approaches, six commonly applied performance boosting techniques for GEC systems, and three data augmentation methods. Since GEC is typically viewed as a sister task of Machine Translation (MT), we put more emphasis on the statistical machine translation (SMT)-based approaches and neural machine translation (NMT)-based approaches for the sake of their importance. Similarly, some performance-boosting techniques are adapted from MT and are successfully combined with GEC systems for enhancement on the final performance. More importantly, after the introduction of the evaluation in GEC, we make an in-depth analysis based on empirical results in aspects of GEC approaches and GEC systems for a clearer pattern of progress in GEC, where error type analysis and system recapitulation are clearly presented. Finally, we discuss five prospective directions for future GEC researches.
Dependency distance measures in assessing L2 writing proficiency

Syntactic complexity is one of the key research foci in writing assessment. This study combines traditional syntactic complexity (SC) measures with newly-proposed dependency distance measures to better assess second language (L2) SC development. Based on a syntactically-annotated corpus of 400 compositions, we aim to investigate the extent to which the traditional SC measures and dependency distance measures can differentiate the writing proficiency of beginner, intermediate, and advanced learners. In terms of traditional SC measures, length-based measures fare better when pinpointing the different writing proficiency levels, but none of these can significantly differentiate all adjacent levels. As for dependency distance measures, the overall mean dependency distance can significantly discriminate all pairs of adjacent proficiency levels, serving as the best metric explored in our study. Moreover, dependency distance measures can further explain the findings of the traditional SC measures from the perspective of language processing.
On The Quality Of Cryptocurrency Markets: Centralized Versus Decentralized Exchanges

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Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization

Training speaker-discriminative and robust speaker verification systems without speaker labels is still challenging and worthwhile to explore. In this study, we propose an effective self-supervised learning framework and a novel regularization strategy to facilitate self-supervised speaker representation learning. Different from contrastive learning-based self-supervised learning methods, the proposed self-supervised regularization (SSReg) focuses exclusively on the similarity between the latent representations of positive data pairs. We also explore the effectiveness of alternative online data augmentation strategies on both the time domain and frequency domain. With our strong online data augmentation strategy, the proposed SSReg shows the potential of self-supervised learning without using negative pairs and it can significantly improve the performance of self-supervised speaker representation learning with a simple Siamese network architecture. Comprehensive experiments on the VoxCeleb datasets demonstrate that our proposed self-supervised approach obtains a 23.4% relative improvement by adding the effective self-supervised regularization and outperforms other previous works.
No-Reference Point Cloud Quality Assessment via Domain Adaptation

We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds. For quality assessment, deep neural network (DNN) has shown compelling performance on no-reference metric design. However, the most challenging issue for no-reference PCQA is that we lack large-scale subjective databases to drive robust networks. Our motivation is that the human visual system (HVS) is the decision-maker regardless of the type of media for quality assessment. Leveraging the rich subjective scores of the natural images, we can quest the evaluation criteria of human perception via DNN and transfer the capability of prediction to 3D point clouds. In particular, we treat natural images as the source domain and point clouds as the target domain, and infer point cloud quality via unsupervised adversarial domain adaptation. To extract effective latent features and minimize the domain discrepancy, we propose a hierarchical feature encoder and a conditional-discriminative network. Considering that the ultimate pur-pose is regressing objective score, we introduce a novel con-ditional cross entropy loss in the conditional-discriminative network to penalize the negative samples which hinder the convergence of the quality regression network. Experi-mental results show that the proposed method can achieve higher performance than traditional no-reference metrics, even comparable results with full-reference metrics. The proposed method also suggests the feasibility of assessing the quality of specific media content without the expensive and cumbersome subjective evaluations. Code is available at https://github.com/Qi-Yangsjtu/IT-PCQA.
Optimal Rate Adaption in Federated Learning with Compressed Communications

Federated Learning (FL) incurs high communication overhead, which can be greatly alleviated by compression for model updates. Yet the tradeoff between compression and model accuracy in the networked environment remains unclear and, for simplicity, most implementations adopt a fixed compression rate only. In this paper, we for the first time systematically examine this tradeoff, identifying the influence of the compression error on the final model accuracy with respect to the learning rate. Specifically, we factor the compression error of each global iteration into the convergence rate analysis under both strongly convex and non-convex loss functions. We then present an adaptation framework to maximize the final model accuracy by strategically adjusting the compression rate in each iteration. We have discussed the key implementation issues of our framework in practical networks with representative compression algorithms. Experiments over the popular MNIST and CIFAR-10 datasets confirm that our solution effectively reduces network traffic yet maintains high model accuracy in FL.
Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder

Anomaly detection has been applied in the various disease of medical practice, such as breast cancer, retinal, lung lesion, and skin disease. However, in real-world anomaly detection, there exist a large number of healthy samples, and but very few sick samples. To alleviate the problem of data imbalance in anomaly detection, this paper proposes an unsupervised learning method for deep anomaly detection based on an improved adversarial autoencoder, in which a module called chain of convolutional block (CCB) is employed instead of the conventional skip-connections used in adversarial autoencoder. Such CCB connections provide considerable advantages via direct connections, not only preserving both global and local information but also alleviating the problem of semantic disparity between the encoding features and the corresponding decoding features. The proposed method is thus able to capture the distribution of normal samples within both image space and latent vector space. By means of minimizing the reconstruction error within both spaces during training phase, higher reconstruction error during test phase is indicative of an anomaly. Our method is trained only on the healthy persons in order to learn the distribution of normal samples and can detect sick samples based on high deviation from the distribution of normality in an unsupervised way. Experimental results for multiple datasets from different fields demonstrate that the proposed method yields superior performance to state-of-the-art methods.
Blockchain Framework for Secured On-Demand Patient Health Records Sharing

The healthcare sector is constantly improving patient health record systems. However, these systems face a significant challenge when confronted with patient health record (PHR) data due to its sensitivity. In addition, patient’s data is stored and spread generally across various healthcare facilities and among providers. This arrangement of distributed data becomes problematic whenever patients want to access their health records and then share them with their care provider, which yields a lack of interoperability among various healthcare systems. Moreover, most patient health record systems adopt a centralized management structure and deploy PHRs to the cloud, which raises privacy concerns when sharing patient information over a network. Therefore, it is vital to design a framework that considers patient privacy and data security when sharing sensitive information with healthcare facilities and providers. This paper proposes a blockchain framework for secured patient health records sharing that allows patients to have full access and control over their health records. With this novel approach, our framework applies the Ethereum blockchain smart contracts, the Inter-Planetary File System (IPFS) as an off-chain storage system, and the NuCypher protocol, which functions as key management and blockchain-based proxy re-encryption to create a secured on-demand patient health records sharing system effectively. Results show that the proposed framework is more secure than other schemes, and the PHRs will not be accessible to unauthorized providers or users. In addition, all encrypted data will only be accessible to and readable by verified entities set by the patient.
A calibration methodology for light sources aimed at using immersive virtual reality game engine as a tool for lighting design in buildings

Immersive virtual reality represents one of the most promising technologic aids in the development of a functional lighting design, especially when considering different points of view as users' satisfaction. This paper presents a new methodology to use Unreal Engine 4.22 as a tool for lighting applications, allowing correct reproduction of artificial light distribution in the game engine by identifying and properly setting a restricted set of parameters. A real office was carefully reproduced, in both the Unreal Engine and DIALux evo software, as comparative case study. Then, the luminance distribution is calculated with both programs on the same surfaces in the virtual environment and compared with the real one. The results suggest a good reliability of the Unreal Engine in reproducing the light distribution, underlining slight differences under certain conditions. In light of these findings, the proposed method seems to validate the use of Unreal Engine as a light design tool.
Why P values are not measures of evidence

Muff et al. [1] propose to implement the recommendation 'to regard P values as what they are, namely, continuous measures of statistical evidence'. This is a surprising recommendation, given that P values are not valid measures of evidence [2]. If the null-hypothesis is true, P values are uniformly distributed, and it is just as likely to observe a P value of 0.001 as it is to observe a P value of 0.999. Therefore, the interpretation of P = 0.001 as strong evidence cannot be defended just because the probability to observe this P value is small.
Roles, applications, and trends of concept map-supported learning: a systematic review and bibliometric analysis of publications from 1992 to 2020 in selected educational technology journals

This study investigates the roles, applications, and trends of concept map-supported education (CMSE) by reviewing the studies published from 1992 to 2020. Several dimensions, such as the media of concept maps, roles of concepts, sample groups, research methods, application domains, learning contexts, research foci, as well as the citation and co-citation relationships, are taken into account based on the technology-based learning model. From the review results, two thematic clusters were found, namely concept map-facilitated knowledge construction in collaborative learning, and concept map-based learning design to improve learning achievement and motivation. The research focused more on the applications in engineering courses, followed by science and language courses, and potential was also found in arts, business or management, and mathematics. Learning contexts for CMSE are mainly in-class activities, followed by online discussion activities; moreover, it was found that the number of applications in digital game-based learning contexts is increasing. In terms of research foci, more studies investigated learners’ performance, attitude/motivation, and perceptions, while less emphasis was put on developing learners’ skills, higher order thinking, as well as collaboration and communication competences. Accordingly, several suggestions for future research of CMSE are provided as a reference for researchers and policy makers.
Phase-modulated Autler-Townes splitting in a giant-atom system within waveguide QED

The nonlocal emitter-waveguide coupling, which gives birth to the so called giant atom, represents a new paradigm in the field of quantum optics and waveguide QED. We investigate the single-photon scattering in a one-dimensional waveguide on a two-level or three-level giant atom. Thanks to the natural interference induced by the back and forth photon transmitted/reflected between the atom-waveguide coupling points, the photon transmission can be dynamically controlled by the periodic phase modulation via adjusting the size of the giant atom. For the two-level giant-atom setup, we demonstrate the energy shift which is dependent on the atomic size. For the driven three-level giant-atom setup, it is of great interest that, the Autler-Townes splitting is dramatically modulated by the giant atom, in which the width of the transmission valleys (reflection range) is tunable in terms of the atomic size. Our investigation will be beneficial to the photon or phonon control in quantum network based on mesoscopical or even macroscopical quantum nodes involving the giant atom.
ASCNet: 3D object detection from point cloud based on adaptive spatial context features

This paper presents a novel two-stage 3D point cloud object detector named ASCNet for autonomous driving. Most current works project 3D point clouds to 2D space, whereas the quantization loss in the transformation is inevitable. A Pillar-wise Spatial Context Feature Encoding (PSCFE) module is proposed in the paper to drive the learning of discriminative features and reduce the detailed information loss. The inhomogeneity that existed in 3D object detection from the point clouds, such as the inconsistent number of points in the pillars, the diverse size of Regions of Interest (RoI), should be treated wisely due to the sparsity and the individual specificity. We introduce a length-adaptive RNN-based module to solve the inhomogeneity. A novel backbone combining encoder-decoder and shortcut connection is designed in the paper to learn the multi-scale features for 3D object detection. Additionally, we utilize multiple RoI heads and class-wise NMS to deal with the class imbalance in scenes. Extensive experiments on the KITTI dataset demonstrate that our algorithm achieves competitive performance in 3D bounding box detection and BEV detection.
Accurate Surface Condition Classification of High Voltage Insulators based on Deep Convolutional Neural Networks

Outdoor insulators in high voltage power lines serve as electrical insulation barriers and mechanical supports for live conductors. They are exposed to environmental contaminants and physical deterioration or damage. Hence, polluted insulator analysis is a fundamental concern for proper power system operation. This study harvests a comprehensive insulator surface dataset composed of 4500 images under different surface conditions: clean surface, clean surface with the water droplets, contaminated surfaces with the soil and cement, as well as a wet surface, which is mixed with the soil and cement contaminants. Convolutional neural networks (CNNs) are employed, and a systematic model selection methodology is introduced to construct an accurate classifier of insulator surface conditions while taking into consideration the potential implementation of the constructed CNN in resource-limited embedded devices. The results show the proposed model complexity reduction technique leads to a lighter architecture by a factor of ~3 at the expense of a slight reduction of 6.5% in classification accuracy.
GraSSNet: Graph Soft Sensing Neural Networks

In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obtained in practice is usually time-series data collected by soft sensors, which are highly nonlinear, nonstationary, imbalanced, and noisy. Most existing soft-sensing machine learning models focus on capturing either intra-series temporal dependencies or pre-defined inter-series correlations, while ignoring the correlation between labels as each instance is associated with multiple labels simultaneously. In this paper, we propose a novel graph based soft-sensing neural network (GraSSNet) for multivariate time-series classification of noisy and highly-imbalanced soft-sensing data. The proposed GraSSNet is able to 1) capture the inter-series and intra-series dependencies jointly in the spectral domain; 2) exploit the label correlations by superimposing label graph that built from statistical co-occurrence information; 3) learn features with attention mechanism from both textual and numerical domain; and 4) leverage unlabeled data and mitigate data imbalance by semi-supervised learning. Comparative studies with other commonly used classifiers are carried out on Seagate soft sensing data, and the experimental results validate the competitive performance of our proposed method.
Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review

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Issuer-Hiding Attribute-Based Credentials

Attribute-based credential systems enable users to authenticate in a privacy-preserving manner. However, in such schemes verifying a user’s credential requires knowledge of the issuer’s public key, which by itself might already reveal private information about the user.In this paper, we tackle this problem by introducing the notion of issuer-hiding attribute-based credential systems. In such a system, the verifier can define a set of acceptable issuers in an ad-hoc manner, and the user can then prove that her credential was issued by one of the accepted issuers – without revealing which one. We then provide a generic construction, as well as a concrete instantiation based on Groth’s structure preserving signature scheme (ASIACRYPT’15) and simulation-sound extractable NIZK, for which we also provide concrete benchmarks in order to prove its practicability.The online complexity of all constructions is independent of the number of acceptable verifiers, which makes it also suitable for highly federated scenarios.