Reviewerly/exHarmony huggingface dataset:
Predicting Ethereum prices with machine learning based on Blockchain information

With the growing interest in cryptocurrency and its fundamental algorithm, studies of cryptocurrency price predictions have been actively conducted in various academic disciplines. Since cryptocurrency is generated and consumed by Blockchain systems, Blockchain-specific information can be considered as the main component in forecasting the values of cryptocurrency. This perspective has been widely adopted in studies of Bitcoin price predictions. However, we find that Ethereum, a popular and leading cryptocurrency in the market, has Blockchain information that differs from that of Bitcoin. Hence, this study investigates the relationship between inherent Ethereum Blockchain information and Ethereum prices. Furthermore, we investigate how Blockchain information concerning other publicly available coins on the market is associated with Ethereum prices. Our key findings reveal that macro-economy factors, Ethereum-specific Blockchain information, and the Blockchain information of other cryptocurrency play important roles in the prediction of Ethereum prices.
Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction

A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. However, while existing video prediction models have produced promising results on small datasets, they suffer from severe underfitting when trained on large and diverse datasets. To address this underfitting challenge, we first observe that the ability to train larger video prediction models is often bottlenecked by the memory constraints of GPUs or TPUs. In parallel, deep hierarchical latent variable models can produce higher quality predictions by capturing the multi-level stochasticity of future observations, but end-to-end optimization of such models is notably difficult. Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction. We introduce Greedy Hierarchical Variational Autoencoders (GHVAEs), a method that learns highfidelity video predictions by greedily training each level of a hierarchical autoencoder. In comparison to state- of-the-art models, GHVAEs provide 17-55% gains in prediction performance on four video datasets, a 35–40% higher success rate on real robot tasks, and can improve performance monotonically by simply adding more modules. Visualization and more details are at https://sites.google.com/view/ghvae.
Algorithm of Classroom Teaching Quality Evaluation Based on Markov Chain

The Markov chain model teaching evaluation method is a quantitative analysis method based on probability theory and stochastic process theory, which establishes a stochastic mathematical model to analyse the quantitative relationship in the change and development process of real activities. Applying it to achieve a more comprehensive, reasonable, and effective evaluation of the classroom teaching quality of college teachers is of positive significance for promoting the continuous improvement of the teaching level of teachers and the teaching quality of schools. Therefore, after an in-depth study of Markov chain algorithm theory, this research proposes an improved Markov chain hybrid teaching quality evaluation model and designs comparative experiments and applies it to the hybrid teaching quality evaluation system of universities, designs a corresponding hybrid teaching quality evaluation model, and finally verifies its effectiveness through experiments. The mathematical model of mixed classroom teaching quality evaluation given in this research focuses on the development and change of the teaching process. For the teaching process that is closely related to the causality of teaching quality, the model established in this paper is more objective and reasonable for evaluating the quality of teaching.
Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries

Depth completion starts from a sparse set of known depth values and estimates the unknown depths for the remaining image pixels. Most methods model this as depth interpolation and erroneously interpolate depth pixels into the empty space between spatially distinct objects, resulting in depth-smearing across occlusion boundaries. Here we propose a multi-hypothesis depth representation that explicitly models both foreground and background depths in the difficult occlusion-boundary regions. Our method can be thought of as performing twin-surface extrapolation, rather than interpolation, in these regions. Next our method fuses these extrapolated surfaces into a single depth image leveraging the image data. Key to our method is the use of an asymmetric loss function that operates on a novel twin-surface representation. This enables us to train a network to simultaneously do surface extrapolation and surface fusion. We characterize our loss function and compare with other common losses. Finally, we validate our method on three different datasets; KITTI, an outdoor real-world dataset, NYU2, indoor real-world depth dataset and Virtual KITTI, a photo-realistic synthetic dataset with dense groundtruth, and demonstrate improvement over the state of the art.
Progressive Domain Expansion Network for Single Domain Generalization

Single domain generalization is a challenging case of model generalization, where the models are trained on a single domain and tested on other unseen domains. A promising solution is to learn cross-domain invariant representations by expanding the coverage of the training domain. These methods have limited generalization performance gains in practical applications due to the lack of appropriate safety and effectiveness constraints. In this paper, we propose a novel learning framework called progressive domain expansion network (PDEN) for single domain generalization. The domain expansion subnetwork and representation learning subnetwork in PDEN mutually benefit from each other by joint learning. For the domain expansion subnetwork, multiple domains are progressively generated in order to simulate various photometric and geometric transforms in unseen domains. A series of strategies are introduced to guarantee the safety and effectiveness of the expanded domains. For the domain invariant representation learning subnetwork, contrastive learning is introduced to learn the domain invariant representation in which each class is well clustered so that a better decision boundary can be learned to improve it’s generalization. Extensive experiments on classification and segmentation have shown that PDEN can achieve up to 15.28% improvement compared with the state-of-the-art single-domain generalization methods. Codes will be released soon at https://github.com/lileicv/PDEN
A Survey on Concept-Level Sentiment Analysis Techniques of Textual Data

Text mining is one of the branches of data mining and refers to as the computing process of finding new patterns and relations among datasets which appear not to be related. Data mining is an interdisciplinary field which uses statistics, artificial intelligence, and database systems to generate new tools for discovering patterns among datasets. Similarly, when dealing with textual data, we need to use various methods in different branches of computer science (e.g. linguistics) and statistics. This study reviews the techniques of text-based sentiment analysis pipeline including preprocessing, aspect extraction, feature selection, and classification techniques used by scholars recently. It also surveys different applications of semantic analysis in the context of social media, marketing, and product reviews.
A<sup>2</sup>-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation

Learning pyramidal feature representations is crucial for recognizing object instances at different scales. Feature Pyramid Network (FPN) is the classic architecture to build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion inhibit FPN from further aggregating more discriminative features. In this work, we propose Attention Aggregation based Feature Pyramid Network (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -FPN), to improve multi-scale feature learning through attention-guided feature aggregation. In feature extraction, it extracts discriminative features by collecting-distributing multi-level global context features, and mitigates the semantic information loss due to drastically reduced channels. In feature fusion, it aggregates complementary information from adjacent features to generate location-wise reassembly kernels for content-aware sampling, and employs channel-wise reweighting to enhance the semantic consistency before element-wise addition. A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -FPN shows consistent gains on different instance segmentation frameworks. By replacing FPN with A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -FPN in Mask R-CNN, our model boosts the performance by 2.1% and 1.6% mask AP when using ResNet-50 and ResNet-101 as backbone, respectively. Moreover, A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -FPN achieves an improvement of 2.0% and 1.4% mask AP when integrated into the strong baselines such as Cascade Mask R-CNN and Hybrid Task Cascade.
Improving the Transferability of Adversarial Samples with Adversarial Transformations

Although deep neural networks (DNNs) have achieved tremendous performance in diverse vision challenges, they are surprisingly susceptible to adversarial examples, which are born of intentionally perturbing benign samples in a human-imperceptible fashion. It thus poses security concerns on the deployment of DNNs in practice, particularly in safety- and security-sensitive domains. To investigate the robustness of DNNs, transfer-based attacks have attracted a growing interest recently due to their high practical applicability, where attackers craft adversarial samples with local models and employ the resultant samples to attack a remote black-box model. However, existing transfer-based attacks frequently suffer from low success rates due to overfitting to the adopted local model. To boost the transferability of adversarial samples, we propose to improve the robustness of synthesized adversarial samples via adversarial transformations. Specifically, we employ an adversarial transformation network to model the most harmful distortions that can destroy adversarial noises and require the synthesized adversarial samples to become resistant to such adversarial transformations. Extensive experiments on the ImageNet benchmark showcase the superiority of our method to state-of-the-art baselines in attacking both undefended and defended models.
iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis

We introduce a new dataset for the emotional artificial intelligence research: identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE). Different from existing public datasets, iMiGUE focuses on nonverbal body gestures without using any identity information, while the predominant researches of emotion analysis concern sensitive biometric data, like face and speech. Most importantly, iMiGUE focuses on micro-gestures, i.e., unintentional behaviors driven by inner feelings, which are different from ordinary scope of gestures from other gesture datasets which are mostly intentionally performed for illustrative purposes. Furthermore, iMiGUE is designed to evaluate the ability of models to analyze the emotional states by integrating information of recognized micro-gesture, rather than just recognizing prototypes in the sequences separately (or isolatedly). This is because the real need for emotion AI is to understand the emotional states behind gestures in a holistic way. Moreover, to counter for the challenge of imbalanced sample distribution of this dataset, an unsupervised learning method is proposed to capture latent representations from the micro-gesture sequences themselves. We systematically investigate representative methods on this dataset, and comprehensive experimental results reveal several interesting insights from the iMiGUE, e.g., micro-gesture-based analysis can promote emotion understanding. We confirm that the new iMiGUE dataset could advance studies of micro-gesture and emotion AI.
From Shadow Generation to Shadow Removal

Shadow removal is a computer-vision task that aims to restore the image content in shadow regions. While almost all recent shadow-removal methods require shadow-free images for training, in ECCV 2020 Le and Samaras introduces an innovative approach without this requirement by cropping patches with and without shadows from shadow images as training samples. However, it is still laborious and time-consuming to construct a large amount of such unpaired patches. In this paper, we propose a new G2R-ShadowNet which leverages shadow generation for weakly-supervised shadow removal by only using a set of shadow images and their corresponding shadow masks for training. The proposed G2R-ShadowNet consists of three sub-networks for shadow generation, shadow removal and refinement, respectively and they are jointly trained in an end-to-end fashion. In particular, the shadow generation sub-net stylises non-shadow regions to be shadow ones, leading to paired data for training the shadow-removal sub-net. Extensive experiments on the ISTD dataset and the Video Shadow Removal dataset show that the proposed G2R-ShadowNet achieves competitive performances against the current state of the arts and outperforms Le and Samaras’ patch-based shadow-removal method.
Open Domain Generalization with Domain-Augmented Meta-Learning

Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain’s annotated data are unavailable. We study a novel and practical problem of Open Domain Generalization (OpenDG), which learns from different source domains to achieve high performance on an unknown target domain, where the distributions and label sets of each individual source domain and the target domain can be different. The problem can be generally applied to diverse source domains and widely applicable to real-world applications. We propose a Domain-Augmented Meta-Learning framework to learn open-domain generalizable representations. We augment domains on both feature-level by a new Dirichlet mixup and label-level by distilled soft-labeling, which complements each domain with missing classes and other domain knowledge. We conduct meta-learning over domains by designing new meta-learning tasks and losses to preserve domain unique knowledge and generalize knowledge across domains simultaneously. Experiment results on various multi-domain datasets demonstrate that the proposed Domain-Augmented Meta-Learning (DAML) outperforms prior methods for unseen domain recognition.
Decomposition and Completion Network for Salient Object Detection

Recently, fully convolutional networks (FCNs) have made great progress in the task of salient object detection and existing state-of-the-arts methods mainly focus on how to integrate edge information in deep aggregation models. In this paper, we propose a novel Decomposition and Completion Network (DCN), which integrates edge and skeleton as complementary information and models the integrity of salient objects in two stages. In the decomposition network, we propose a cross multi-branch decoder, which iteratively takes advantage of cross-task aggregation and cross-layer aggregation to integrate multi-level multi-task features and predict saliency, edge, and skeleton maps simultaneously. In the completion network, edge and skeleton maps are further utilized to fill flaws and suppress noises in saliency maps via hierarchical structure-aware feature learning and multi-scale feature completion. Through jointly learning with edge and skeleton information for localizing boundaries and interiors of salient objects respectively, the proposed network generates precise saliency maps with uniformly and completely segmented salient objects. Experiments conducted on five benchmark datasets demonstrate that the proposed model outperforms existing networks. Furthermore, we extend the proposed model to the task of RGB-D salient object detection, and it also achieves state-of-the-art performance. The code is available at https://github.com/wuzhe71/DCN.
Enhanced Butterfly Optimization Algorithm with a New fuzzy Regulator Strategy and Virtual Butterfly Concept

Butterfly Optimization Algorithm (BOA) is a recently developed metaheuristic search algorithm that mimics the food-search process of the butterflies in nature. The studies reveal that BOA shows significant performance on the non-constrained and unbiased mathematical functions, but in the cases of shifted and rotated functions and/or constrained optimization problems, its search capacity is considerably restricted. To address this shortcoming, the present study deals with developing a new fuzzy decision-making strategy and introducing a new auxiliary concept called “virtual butterfly” to enhance the search capability of the standard BOA. The developed fuzzy strategy permanently monitors the optimization process and tries to adjust each butterfly’s search behavior based on the governing conditions of the current problem. Also, the virtual butterfly concept involving the information of the whole colony tries to provide alternative promising search directions for the other butterflies. The new reinforced method is named Fuzzy Butterfly Optimization Algorithm (FBOA). To evaluate the search performance of the proposed FBOA, it is tested on solving a suit of constrained and non-constrained optimization problems and the achieved outcomes are compared with those given by some other well-established techniques including its parent method (i.e. BOA). The results show that the implemented improvements significantly increase the search capability of the regular BOA, especially in the case of constrained engineering problems.
Spoken language interaction with robots: Recommendations for future research

With robotics rapidly advancing, more effective human–robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be part of the solution, our ability to provide spoken language interaction capabilities is still very limited. In this article, based on the report of an interdisciplinary workshop convened by the National Science Foundation, we identify key scientific and engineering advances needed to enable effective spoken language interaction with robotics. We make 25 recommendations, involving eight general themes: putting human needs first, better modeling the social and interactive aspects of language, improving robustness, creating new methods for rapid adaptation, better integrating speech and language with other communication modalities, giving speech and language components access to rich representations of the robot’s current knowledge and state, making all components operate in real time, and improving research infrastructure and resources. Research and development that prioritizes these topics will, we believe, provide a solid foundation for the creation of speech-capable robots that are easy and effective for humans to work with.
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond

The class of random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 2017 and the ICML Best Paper Finalist in 2019. The body of work on random features has grown rapidly, and hence it is desirable to have a comprehensive overview on this topic explaining the connections among various algorithms and theoretical results. In this survey, we systematically review the work on random features from the past ten years. First, the motivations, characteristics and contributions of representative random features based algorithms are summarized according to their sampling schemes, learning procedures, variance reduction properties and how they exploit training data. Second, we review theoretical results that center around the following key question: how many random features are needed to ensure a high approximation quality or no loss in the empirical/expected risks of the learned estimator. Third, we provide a comprehensive evaluation of popular random features based algorithms on several large-scale benchmark datasets and discuss their approximation quality and prediction performance for classification. Last, we discuss the relationship between random features and modern over-parameterized deep neural networks (DNNs), including the use of high dimensional random features in the analysis of DNNs as well as the gaps between current theoretical and empirical results. This survey may serve as a gentle introduction to this topic, and as a users' guide for practitioners interested in applying the representative algorithms and understanding theoretical results under various technical assumptions. We hope that this survey will facilitate discussion on the open problems in this topic, and more importantly, shed light on future research directions. Due to the page limit, we suggest the readers refer to the full version of this survey https://arxiv.org/abs/2004.11154.
k-means clustering and kNN classification based on negative databases

Nowadays, privacy protection has become an important issue in data mining. k-means clustering and kNN classification are two popular data mining algorithms, which have been widely studied in the past decade. In this paper, we mainly study the problem of privacy protection during k-means clustering and kNN classification. Negative database (NDB) is a new type of data representation which can protect privacy as well as support distance estimation, so it is promising to apply NDBs to privacy-preserving k-means clustering and kNN classification. Existing privacy-preserving k-means clustering and kNN classification algorithms based on NDBs could effectively protect data privacy, but their clustering or classification performance has a non-negligible degradation. To alleviate this problem, we propose a new NDB generation algorithm (named QK-hidden algorithm). Compared to existing NDB generation algorithms, the QK-hidden algorithm employs a new set of parameters to control the selection of different bits when generating NDB records, and this enables a fine-grained control of the accuracy of distance estimation. By the QK-hidden algorithm, private data are converted into NDBs before inputting to k-means and kNN algorithms. Then, we propose an approach specialized for estimating Euclidean distance from the NDBs generated by the QK-hidden algorithm, and this approach is used instead of calculating Euclidean distance on plain text. Moreover, we design a model for estimating the cluster centers from NDBs for the k-means algorithm. The remaining steps of k-means and kNN algorithms keep the same to original versions. Experimental results demonstrate that the proposed algorithms could achieve more than 30% improvement on the clustering performance (in terms of average Davies–Bouldin Index) and 9% improvement on the classification performance (in terms of macro precision, recall and F1 score) in comparison with some existing works.