Reviewerly/exHarmony huggingface dataset:
Looking Back on the Actor–Critic Architecture

This retrospective describes the overall research project that gave rise to the authors' paper “Neuronlike adaptive elements that can solve difficult learning control problems” that was published in the 1983 Neural and Sensory Information Processing special issue of the IEEE Transactions on Systems, Man, and Cybernetics. This look back explains how this project came about, presents the ideas and previous publications that influenced it, and describes our most closely related subsequent research. It concludes by pointing out some noteworthy aspects of this article that have been eclipsed by its main contributions, followed by commenting on some of the directions and cautions that should inform future research.
Parallel Fully Convolutional Network for Semantic Segmentation

Fully convolutional networks (FCNs) have been widely applied for dense classification tasks such as semantic segmentation. As a large number of works based on FCNs are proposed, various semantic segmentation models have been improved significantly. However, duplicated upsampling and deconvolution operations in the FCNs will lead to information loss in semantic segmentation tasks and to problems such as ignoring the relationship between pixels and pixels and the lack of spatial consistency. In this study, we propose a parallel fully convolutional neural network that integrates holistically-nested edge detection (HED) network to capture image edge information, improving semantic segmentation performance. We carry out comprehensive experiments and achieve a better result on the PASCAL VOC 2012, PASCAL-Context and Cityscapes, comparing the results with some existing semantic segmentation methods.
Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs

Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images. However, data quantity and quality remain a key determinant, and a significant limit of the potential applications. In our previous work, we explored the synthesis of anatomic and molecular MR image networks (SAMR) in patients with post-treatment malignant gliomas. In this work, we extend this through a confidence-guided SAMR (CG-SAMR) that synthesizes data from lesion contour information to multi-modal MR images, including T1-weighted ( [Formula: see text]), gadolinium enhanced [Formula: see text] (Gd- [Formula: see text]), T2-weighted ( [Formula: see text]), and fluid-attenuated inversion recovery ( FLAIR ), as well as the molecular amide proton transfer-weighted ( [Formula: see text]) sequence. We introduce a module that guides the synthesis based on a confidence measure of the intermediate results. Furthermore, we extend the proposed architecture to allow training using unpaired data. Extensive experiments on real clinical data demonstrate that the proposed model can perform better than current the state-of-the-art synthesis methods. Our code is available at https://github.com/guopengf/CG-SAMR.
Minimum Excess Risk in Bayesian Learning

We analyze the best achievable performance of Bayesian learning under generative models by defining and upper-bounding the minimum excess risk (MER): the gap between the minimum expected loss attainable by learning from data and the minimum expected loss that could be achieved if the model realization were known. The definition of MER provides a principled way to define different notions of uncertainties in Bayesian learning, including the aleatoric uncertainty and the minimum epistemic uncertainty. Two methods for deriving upper bounds for the MER are presented. The first method, generally suitable for Bayesian learning with a parametric generative model, upper-bounds the MER by the conditional mutual information between the model parameters and the quantity being predicted given the observed data. It allows us to quantify the rate at which the MER decays to zero as more data becomes available. Under realizable models, this method also relates the MER to the richness of the generative function class, notably the VC dimension in binary classification. The second method, particularly suitable for Bayesian learning with a parametric predictive model, relates the MER to the minimum estimation error of the model parameters from data via various continuity arguments. We also extend the definition and analysis of MER to the setting with multiple model families and the setting with nonparametric models. Along the discussions we draw some comparisons between the MER in Bayesian learning and the excess risk in frequentist learning.
An Improved K-means Clustering Algorithm

Among the existing clustering algorithms, K-means algorithm has become one of the most widely used technologies, mainly because of its simplicity and effectiveness. However, the selection of the initial clustering centers and the sensitivity to noise will reduce the clustering effect. To solve these problems, this paper proposes an improved K-means clustering algorithm. The concept of CLIQUE grid is used to remove the noise and obtain the regional density. Then the initial center point is selected according to the method of Fast Search and Find of Density Peaks (CFSFDP). And the influence of grid density error on the selection of initial center point is reduced by the idea of granularity, while the selection of cluster center by manual participation in the peak density algorithm is then avoided. Compared with the original K-means algorithm, the improved algorithm proposed in this paper has higher precision, smaller difference in clustering effect for different data, and lower parameter dependence.
Authorship Identification of a Russian-Language Text Using Support Vector Machine and Deep Neural Networks

The article explores approaches to determining the author of a natural language text and the advantages and disadvantages of these approaches. The importance of the considered problem is due to the active digitalization of society and reassignment of most parts of the life activities online. Text authorship methods are particularly useful for information security and forensics. For example, such methods can be used to identify authors of suicide notes, and other texts are subjected to forensic examinations. Another area of application is plagiarism detection. Plagiarism detection is a relevant issue both for the field of intellectual property protection in the digital space and for the educational process. The article describes identifying the author of the Russian-language text using support vector machine (SVM) and deep neural network architectures (long short-term memory (LSTM), convolutional neural networks (CNN) with attention, Transformer). The results show that all the considered algorithms are suitable for solving the authorship identification problem, but SVM shows the best accuracy. The average accuracy of SVM reaches 96%. This is due to thoroughly chosen parameters and feature space, which includes statistical and semantic features (including those extracted as a result of an aspect analysis). Deep neural networks are inferior to SVM in accuracy and reach only 93%. The study also includes an evaluation of the impact of attacks on the method on models’ accuracy. Experiments show that the SVM-based methods are unstable to deliberate text anonymization. In comparison, the loss in accuracy of deep neural networks does not exceed 20%. Transformer architecture is the most effective for anonymized texts and allows 81% accuracy to be achieved.
CDD-Net: A Context-Driven Detection Network for Multiclass Object Detection

Unlike object detection in natural images that usually achieved great success, remote sensing imagery has its own challenges to detect and localize multiclass objects, such as large-scale change, uncertain direction, and high density. The context information of the objects is very worthwhile for solving these challenges in remote sensing images. In this letter, we propose a context-driven detection network (CDD-Net) to improve the accuracy of multiclass object detection in remote sensing images. For capturing the local neighboring objects and features, a local context feature network (LCFN) is proposed to learn the local context of the region of interest. Meanwhile, a hybrid attention pyramid network (HAPN) is designed, which can steer the focus to more valuable features. The HAPN inserts a squeeze and excitation block (SEB) and three asymmetric convolution blocks (ACBs) in the feature pyramid network (FPN). The experimental results over the DOTA-v1.5 data set demonstrate that the proposed CDD-Net yields promising results.
Performance Analysis of Different Neural Networks for Sentiment Analysis on IMDb Movie Reviews

With the huge expansion of text data sentiment analysis is playing a crucial role in analyzing the user’s perspective about a particular product, company or any other physical or virtual entity. Sentiment analysis helps us to analyze user review about an entity and then drawing out a conclusion based on the sentiments it extracted from the reviews. Convolution Neural Network (CNN) and Long-Short-Term Memory Network (LSTM) are two well-known deep neural networks used for sentiment analysis. In this paper, we have compared between CNN, LSTM and LSTM-CNN architectures for sentiment classification on the IMDb movie reviews in order to find the best-suited architecture for the dataset. Experimental results have shown that CNN has achieved an F-Score of 91% which has outperformed LSTM, LSTM-CNN and other state-of-the-art approaches for sentiment classification on IMDb movie reviews.
Robust Facial Landmark Detection by Multiorder Multiconstraint Deep Networks

Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facial landmark detection methods neglect to explore the high-order feature correlations, which is very important to learn more representative features and enhance shape constraints. Moreover, no explicit global shape constraints have been added to the final predicted landmarks, which leads to a reduction in accuracy. To address these issues, in this article, we propose a multiorder multiconstraint deep network (MMDN) for more powerful feature correlations and shape constraints' learning. Especially, an implicit multiorder correlating geometry-aware (IMCG) model is proposed to introduce the multiorder spatial correlations and multiorder channel correlations for more discriminative representations. Furthermore, an explicit probability-based boundary-adaptive regression (EPBR) method is developed to enhance the global shape constraints and further search the semantically consistent landmarks in the predicted boundary for robust facial landmark detection. It is interesting to show that the proposed MMDN can generate more accurate boundary-adaptive landmark heatmaps and effectively enhance shape constraints to the predicted landmarks for faces with large pose variations and heavy occlusions. Experimental results on challenging benchmark data sets demonstrate the superiority of our MMDN over state-of-the-art facial landmark detection methods.
Robust adversarial discriminative domain adaptation for real-world cross-domain visual recognition

Deep convolutional networks (CNNs) are able to learn robust representations and empower many computer vision tasks such as object recognition. However, when applying CNNs to industrial visual systems, they usually suffer from domain shift that exists between the training data and testing data. Such shift can be caused by different environment, types of cameras and exteriors of objects, leading to degrading performance and hindering the practical applications of CNNs in real-world visual recognition. To tackle this problem, Adversarial domain adaptation (ADA) reduces such shift by min–max optimization. However, current CNNs with ADA are hard to train due to training instability of adversarial network. In this paper, we propose a unified and easy-to-train domain adaptation framework, namely Attention-based Domain-confused Adversarial Domain ADaptation (AD3). Our method leverages both adversarial and statistical domain alignment, allows flexibility for source and target feature extractors and simultaneously performs feature-level and attention-level alignment. The statistical domain alignment promotes and stabilizes the adversarial domain learning, which reduces the manual work of tuning the hyper-parameters. The experimental results validate that our method performs better adaptation and faster convergence for adversarial domain learning than existing state-of-the-art methods on DIGITS, Office-31 and VisDA domain adaptation benchmarks.
Garbage Classification Detection Based on Improved YOLOV4

As the rate of garbage generation gradually increases, the past garbage disposal methods will be eliminated, so the classification of garbage has become an inevitable choice.The multi-category classification of garbage and the accuracy of recognition have also become the focus of attention.Aiming at the problems of single category, few types of objects and low accuracy in existing garbage classification algorithms.This paper proposes to use the improved YOLOV4 network framework to detect 3 categories, a total of 15 objects, and find that the average accuracy is 64%, Frame per second 92f/s.It turns out that the improved YOLOV4 can better detect garbage categories and is suitable for embedded devices.
Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.
Robust predictive visual servoing control for an inertially stabilized platform with uncertain kinematics

In this paper, a disturbance observer (DOB) based predictive control approach is developed for the image-based visual servoing of an inertially stabilized platform (ISP). As the limitation in degrees of freedom of a two-axes ISP, it is hard to estimate the variable feature depth of the target at each control cycle when using an uncalibrated camera, which brings the challenge in the design of the visual servoing controller. To this end, a depth-independent kinematic matrix that only involves nominal parameters is obtained by employing the partitioned scheme in the system modeling. The uncertain kinematics arising from the unknown feature depth, angular velocity tracking errors, and uncalibrated intrinsic parameters is considered as the lumped uncertainty. A discrete-time DOB is then constructed to estimate the lumped uncertainty in real time. Instead of taking an integral action to eliminate tracking errors induced by the uncertain kinematics, the disturbance estimation is actively incorporated into the receding optimization process of the predictive controller. The stability of the closed-loop system is fully analyzed. Experiments on tracking a moving target are performed to validate promising qualities of the proposed approach.
An Ensemble Model for Fake Online Review Detection Based on Data Resampling, Feature Pruning, and Parameter Optimization

With the widespread of fake online reviews, the detection of fake reviews has become a hot research issue. Despite the efforts of existing studies on fake review detection, the issues of imbalanced data and feature pruning still lack sufficient attention. To address these gaps, the present study proposes an ensemble model for the detection of fake online reviews. The model consists of four steps, and the first three steps are proposed to optimize the base classifiers: (i) Data resampling: We propose a novel way to address the data imbalance problem by combining the resampling and the grid search technique. (ii) Feature pruning: We propose an ablation study to drop unimportant features. (iii) Parameters optimization: We apply the grid search algorithm to determine suitable values of the relevant parameters for each base classifier. (iv) Classifier ensembling: We apply majority voting and stacking strategies to integrate the optimized base classifiers. The proposed data resampling method is also applied for the meta-classifier in the stacking ensemble model. This study produces advances in terms of combining different methods or algorithms into a model and the results show that the proposed ensemble model outperforms some existing techniques, thereby providing a new way to solve the data imbalance and feature pruning issues in the field of fake review detection.
Unsupervised Video Representation Learning by Bidirectional Feature Prediction

This paper introduces a novel method for self-supervised video representation learning via feature prediction. In contrast to the previous methods that focus on future feature prediction, we argue that a supervisory signal arising from unobserved past frames is complementary to one that originates from the future frames. The rationale behind our method is to encourage the network to explore the temporal structure of videos by distinguishing between future and past given present observations. We train our model in a contrastive learning framework, where joint encoding of future and past provides us with a comprehensive set of temporal hard negatives via swapping. We empirically show that utilizing both signals enriches the learned representations for the downstream task of action recognition. It out-performs independent prediction of future and past.
Applying Metalevel Argumentation Frameworks to Support Medical Decision Making

People are increasingly employing artificial intelligence as the basis for decision-support systems (DSSs) to assist them in making well-informed decisions.Adoption of DSS is challenging when such systems lack support, or evidence, for justifying their recommendations.DSSs are widely applied in the medical domain, due to the complexity of the domain and the sheer volume of data that render manual processing difficult.This paper proposes a metalevel argumentation-based decision-support system that can reason with heterogeneous data (e.g.body measurements, electronic health records, clinical guidelines), while incorporating the preferences of the human beneficiaries of those decisions.The system constructs template-based explanations for the recommendations that it makes.The proposed framework has been implemented in a system to support stroke patients and its functionality has been tested in a pilot study.User feedback shows that the system can run effectively over an extended period.
Contract-Theoretic Pricing for Security Deposits in Sharded Blockchain With Internet of Things (IoT)

A sharded blockchain with the Proof-of-Stake (PoS) consensus protocol has advantages in increasing throughput and reducing energy consumption, enabling the resource-limited participants to manage transactions and in a decentralized way and obtain rewards at a lower cost, e.g., Internet-of-Things (IoT) users. However, the latest PoS (e.g., Casper) requires a steep security deposit, which is the key to provide more robust security guarantees than Proof of Work, but not practical for the owners of heterogeneous IoT devices. This article considers any individual and institute who owns the IoT devices as the potential participant and focuses on designing the proper security deposits in a practical scenario with hidden information and hidden action. To bridge blockchain and the IoT users, we study the problem of balancing the security incentive and the economic incentive under two cases: 1) stake oriented and 2) effort oriented. We propose two joint models under the contract theory framework to efficiently address the problems: 1) joint adverse selection and moral hazard and 2) joint adverse selection and tournament. Both optimal contracts can provide a maximized profit for blockchain. The optimal rewards and security deposits for different types of participants can be determined accordingly. Simulations indicate that the proposed models can overcome asymmetric information and offer feasible contracts. Moreover, it demonstrates that both joint models can provide an economic incentive for the participants without reducing security incentives for the sharded blockchain.
AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings

In this paper, we propose a novel approach for generalized zero-shot learning in a multi-modal setting, where we have novel classes ofaudio/video during testing that are not seen during training. We use the semantic relatedness oftext embeddings as a means for zero-shot learning by aligning audio and video embeddings with the corresponding class label text feature space. Our approach uses a cross-modal decoder and a composite triplet loss. The cross-modal decoder enforces a constraint that the class label text features can be reconstructed from the audio and video embeddings of data points. This helps the audio and video embeddings to move closer to the class label text embedding. The composite triplet loss makes use of the audio, video, and text embeddings. It helps bring the embeddings from the same class closer and push away the embeddings from different classes in a multi-modal setting. This helps the network to perform better on the multi-modal zero-shot learning task. Importantly, our multi-modal zero-shot learning approach works even if a modality is missing at test time. We test our approach on the generalized zero-shot classification and retrieval tasks and show that our approach outperforms other models in the presence of a single modality as well as in the presence of multiple modalities. We validate our approach by comparing it with previous approaches and using various ablations.