Reviewerly/exHarmony huggingface dataset:
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study

Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Unsupervised graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. However, to date, there has been little work exploring exactly which topological structures are being learned in the embeddings, which could be a possible way to bring interpretability to the process. In this paper, we investigate if graph embeddings are approximating something analogous to traditional vertex-level graph features. If such a relationship can be found, it could be used to provide a theoretical insight into how graph embedding approaches function. We perform this investigation by predicting known topological features, using supervised and unsupervised methods, directly from the embedding space. If a mapping between the embeddings and topological features can be found, then we argue that the structural information encapsulated by the features is represented in the embedding space. To explore this, we present extensive experimental evaluation with five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features. We demonstrate that several topological features are indeed being approximated in the embedding space, allowing key insight into how graph embeddings create good representations.
Virtual and Augmented Reality in Education

Over the past few years, VR and AR have been gaining popularity very fast.Implementations of new technologies caused the development of AR and VR.Both virtual reality and augmented reality is not yet widely used in higher education and the purpose of this paper is to provide a model for the development of integrated learning objects based on VR and AR approach where VR and AR objects could be integrated into other educational content as learning objects by creating integrated learning programs, learning scenarios or etc.This paper provides the literature review on virtual and augmented reality and analysis of integrated models for educational process implementation.The suggested model validates authors' conclusions and leads to recommendations for its implementation.
Enhancing the Measurement of Social Effects by Capturing Morality

We investigate the relationship between basic principles of human morality and the expression of opinions in user-generated text data. We assume that people’s backgrounds, culture, and values are associated with their perceptions and expressions of everyday topics, and that people’s language use reflects these perceptions. While personal values and social effects are abstract and complex concepts, they have practical implications and are relevant for a wide range of NLP applications. To extract human values (in this paper, morality) and measure social effects (morality and stance), we empirically evaluate the usage of a morality lexicon that we expanded via a quality controlled, human in the loop process. As a result, we enhanced the Moral Foundations Dictionary in size (from 324 to 4,636 syntactically disambiguated entries) and scope. We used both lexica for feature-based and deep learning classification (SVM, RF, and LSTM) to test their usefulness for measuring social effects. We find that the enhancement of the original lexicon led to measurable improvements in prediction accuracy for the selected NLP tasks.
Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks

For the image super-resolution method from a single channel, it is difficult to achieve both fast convergence and high-quality texture restoration. By mitigating the weaknesses of existing methods, the present paper proposes an image super-resolution algorithm based on dual-channel convolutional neural networks (DCCNN). The novel structure of the network model was divided into a deep channel and a shallow channel. The deep channel was used to extract the detailed texture information from the original image, while the shallow channel was mainly used to recover the overall outline of the original image. Firstly, the residual block was adjusted in the feature extraction stage, and the nonlinear mapping ability of the network was enhanced. The feature mapping dimension was reduced, and the effective features of the image were obtained. In the up-sampling stage, the parameters of the deconvolutional kernel were adjusted, and high-frequency signal loss was decreased. The high-resolution feature space could be rebuilt recursively using long-term and short-term memory blocks during the reconstruction stage, further enhancing the recovery of texture information. Secondly, the convolutional kernel was adjusted in the shallow channel to reduce the parameters, ensuring that the overall outline of the image was restored and that the network converged rapidly. Finally, the dual-channel loss function was jointly optimized to enhance the feature-fitting ability in order to obtain the final high-resolution image output. Using the improved algorithm, the network converged more rapidly, the image edge and texture reconstruction effect were obviously improved, and the Peak Signal-to-Noise Ratio (PSNR) and structural similarity were also superior to those of other solutions.
Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation

State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every pair of image domains, which limits the scalability in dealing with more than two image domains. In addition, the training stage of these methods has the common problem of model collapse that degrades the quality of the generated images. To tackle these issues, we propose a Dual Generator Generative Adversarial Network (G $$^2$$ GAN), which is a robust and scalable approach allowing to perform unpaired image-to-image translation for multiple domains using only dual generators within a single model. Moreover, we explore different optimization losses for better training of G $$^2$$ GAN, and thus make unpaired image-to-image translation with higher consistency and better stability. Extensive experiments on six publicly available datasets with different scenarios, i.e., architectural buildings, seasons, landscape and human faces, demonstrate that the proposed G $$^2$$ GAN achieves superior model capacity and better generation performance comparing with existing image-to-image translation GAN models.
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering

End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.
Application of a Blockchain Platform to Manage and Secure Personal Genomic Data: A Case Study of LifeCODE.ai in China

Background The rapid development of genetic and genomic technologies, such as next-generation sequencing and genome editing, has made disease treatment much more precise and effective. The technologies’ value can only be realized by the aggregation and analysis of people’s genomic and health data. However, the collection and sharing of genomic data has many obstacles, including low data quality, information islands, tampering distortions, missing records, leaking of private data, and gray data transactions. Objective This study aimed to prove that emerging blockchain technology provides a solution for the protection and management of sensitive personal genomic data because of its decentralization, traceability, encryption algorithms, and antitampering features. Methods This paper describes the case of a blockchain-based genomic big data platform, LifeCODE.ai, to illustrate the means by which blockchain enables the storage and management of genomic data from the perspectives of data ownership, data sharing, and data security. Results Blockchain opens up new avenues for dealing with data ownership, data sharing, and data security issues in genomic big data platforms and realizes the psychological empowerment of individuals in the platform. Conclusions The blockchain platform provides new possibilities for the management and security of genetic data and can help realize the psychological empowerment of individuals in the process, and consequently, the effects of data self-governance, incentive-sharing, and security improvement can be achieved. However, there are still some problems in the blockchain that have not been solved, and which require continuous in-depth research and innovation in the future.
Switch-Based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences. The Dyna-Q algorithm extends Q-learning by integrating a world model, and thus can effectively boost training efficiency using simulated experiences generated by the world model. The effectiveness of Dyna-Q, however, depends on the quality of the world model - or implicitly, the pre-specified ratio of real vs. simulated experiences used for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ) framework by integrating a switcher that automatically determines whether to use a real or simulated experience for Q-learning. Furthermore, we explore the use of active learning for improving sample efficiency, by encouraging the world model to generate simulated experiences in the stateaction space where the agent has not (fully) explored. Our results show that by combining switcher and active learning, the new framework named as Switch-based Active Deep Dyna-Q (Switch-DDQ), leads to significant improvement over DDQ and Q-learning baselines in both simulation and human evaluations.1
A Distributed Energy Trading Authentication Mechanism Based on a Consortium Blockchain

With the rapid development of the energy internet, the transaction of distributed renewable energy (DRE) is playing an increasingly important role in the energy market. However, in the transaction model of distributed renewable energy combined with public blockchain technology, nodes in the trading network can join or leave the network at any time without any permission, which hinders the regulation of electricity institutions. Corresponding to the transaction principle, a distributed renewable energy transaction authentication mechanism based on consortium blockchain is proposed in this paper. First, certificate authority nodes were set in the transaction network to provide nodes with access authority by controlling the public keys and private keys of trading participants so that they can complete their identity authentication. Next, essential chaincodes in the transaction authentication were designed and deployed on a Hyperledger Fabric blockchain site, and a simulation experiment of a simple DRE transaction was used to elaborate the details on the transaction process. Finally, the proposed model was evaluated according to its performance and proved to be practical and effective.
Deeply-learned Hybrid Representations for Facial Age Estimation

In this paper, we propose a novel unified network named Deep Hybrid-Aligned Architecture for facial age estimation. It contains global, local and global-local branches. They are jointly optimized and thus can capture multiple types of features with complementary information. In each branch, we employ a separate loss for each sub-network to extract the independent features and use a recurrent fusion to explore correlations among those region features. Considering that the pose variations may lead to misalignment in different regions, we design an Aligned Region Pooling operation to generate aligned region features. Moreover, a new large age dataset named Web-FaceAge owning more than 120K samples is collected under diverse scenes and spanning a large age range. Experiments on five age benchmark datasets, including Web-FaceAge, Morph, FG-NET, CACD and Chalearn LAP 2015, show that the proposed method outperforms the state-of-the-art approaches significantly.
FABA: An Algorithm for Fast Aggregation against Byzantine Attacks in Distributed Neural Networks

Many times, training a large scale deep learning neural network on a single machine becomes more and more difficult for a complex network model. Distributed training provides an efficient solution, but Byzantine attacks may occur on participating workers. They may be compromised or suffer from hardware failures. If they upload poisonous gradients, the training will become unstable or even converge to a saddle point. In this paper, we propose FABA, a Fast Aggregation algorithm against Byzantine Attacks, which removes the outliers in the uploaded gradients and obtains gradients that are close to the true gradients. We show the convergence of our algorithm. The experiments demonstrate that our algorithm can achieve similar performance to non-Byzantine case and higher efficiency as compared to previous algorithms.
Adversarial Graph Embedding for Ensemble Clustering

Ensemble clustering generally integrates basic partitions into a consensus one through a graph partitioning method, which, however, has two limitations: 1) it neglects to reuse original features; 2) obtaining consensus partition with learnable graph representations is still under-explored. In this paper, we propose a novel Adversarial Graph Auto-Encoders (AGAE) model to incorporate ensemble clustering into a deep graph embedding process. Specifically, graph convolutional network is adopted as probabilistic encoder to jointly integrate the information from feature content and consensus graph, and a simple inner product layer is used as decoder to reconstruct graph with the encoded latent variables (i.e., embedding representations). Moreover, we develop an adversarial regularizer to guide the network training with an adaptive partition-dependent prior. Experiments on eight real-world datasets are presented to show the effectiveness of AGAE over several state-of-the-art deep embedding and ensemble clustering methods.
Scale adaptive image cropping for UAV object detection

Although deep learning methods have made significant breakthroughs in generic object detection, their performance on aerial images is not satisfactory. Unlike generic images, aerial images have smaller object relative scales (ORS), more low-resolution objects, and serious object scale diversity. Most researches focus on modifying network structures to address these challenges, while few studies pay attention to data enhancement which can be used in combination with model modification to further improve detection accuracy. In this work, a novel data enhancement method called scale adaptive image cropping (SAIC) is proposed to address these three challenges. Specifically, SAIC consists three steps: ORS estimation in which a specific neural network is designed to estimate ORS levels of images; image resizing in which a GAN-based super-resolution method is adopted to up-sample images with the smallest ORS level, easing low-resolution object detection; image cropping in which three cropping strategies are proposed to crop resized images, adjusting ORS. Extensive experiments are conducted to demonstrate the effectiveness of our method. SAIC improves the accuracy of feature pyramid network (FPN) by 9.65% (or relatively 37.06%). Without any major modification, FPN trained with SAIC won the 3rd rank on 2018 VisDrone challenge detection task.
Clustering and Dynamic Sampling Based Unsupervised Domain Adaptation for Person Re-Identification

Person Re-Identification (Re-ID) has witnessed great improvements due to the advances of the deep convolutional neural networks (CNN). Despite this, existing methods mainly suffer from the poor generalization ability to unseen scenes because of the different characteristics between different domains. To address this issue, a Clustering and Dynamic Sampling (CDS) method is proposed in this paper, which tries to transfer the useful knowledge of existing labeled source domain to the unlabeled target one. Specifically, to improve the discriminability of CNN model on source domain, we use the commonly shared pedestrian attributes (e.g., gender, hat and clothing color etc.) to enrich the information and resort to the margin-based softmax (e.g., A-Softmax) loss to train the model. For the unlabeled target domain, we iteratively cluster the samples into several centers and dynamically select informative ones from each center to fine-tune the source-domain model. Extensive experiments on DukeMTMC-reID and Market-1501 datasets show that the proposed method greatly improves the state of the arts in unsupervised domain adaptation.
Generating query-specific class API summaries

Source code summaries are concise representations, in form of text and/or code, of complex code elements and are meant to help developers gain a quick understanding that in turns help them perform specific tasks. Generation of summaries that are task-specific is still a challenge in the automatic code summarization field. We propose an approach for generating on-demand, extrinsic hybrid summaries for API classes, relevant to a programming task, formulated as a natural language query. The summaries include the most relevant sentences extracted from the API reference documentation and the most relevant methods.
Universal gates for protected superconducting qubits using optimal control

We employ quantum optimal control theory to realize quantum gates for two protected superconducting circuits: the heavy-fluxonium qubit and the 0-$\ensuremath{\pi}$ qubit. Utilizing automatic differentiation facilitates the simultaneous inclusion of multiple optimization targets, allowing one to obtain high-fidelity gates with realistic pulse shapes. For both qubits, disjoint support of low-lying wave functions prevents direct population transfer between the computational-basis states. Instead, optimal control favors dynamics involving higher-lying levels, effectively lifting the protection for a fraction of the gate duration. For the 0-$\ensuremath{\pi}$ qubit, offset-charge dependence of matrix elements among higher levels poses an additional challenge for gate protocols. To mitigate this issue, we randomize the offset charge during the optimization process, steering the system towards pulse shapes insensitive to charge variations. Closed-system fidelities obtained are 99% or higher and show slight reductions in open-system simulations.
Neural News Recommendation with Heterogeneous User Behavior

News recommendation is important for online news platforms to help users find interested news and alleviate information overload.Existing news recommendation methods usually rely on the news click history to model user interest.However, these methods may suffer from the data sparsity problem, since the news click behaviors of many users in online news platforms are usually very limited.Fortunately, some other kinds of user behaviors such as webpage browsing and search queries can also provide useful clues of users' news reading interest.In this paper, we propose a neural news recommendation approach which can exploit heterogeneous user behaviors.Our approach contains two major modules, i.e., news representation and user representation.In the news representation module, we learn representations of news from their titles via CNN networks, and apply attention networks to select important words.In the user representation module, we propose an attentive multi-view learning framework to learn unified representations of users from their heterogeneous behaviors such as search queries, clicked news and browsed webpages.In addition, we use word-and record-level attentions to select informative words and behavior records.Experiments on a real-world dataset validate the effectiveness of our approach.
Collaborative Policy Learning for Open Knowledge Graph Reasoning

In recent years, there has been a surge of interests in interpretable graph reasoning methods.However, these models often suffer from limited performance when working on sparse and incomplete graphs, due to the lack of evidential paths that can reach target entities.Here we study open knowledge graph reasoning-a task that aims to reason for missing facts over a graph augmented by a background text corpus.A key challenge of the task is to filter out "irrelevant" facts extracted from corpus, in order to maintain an effective search space during path inference.We propose a novel reinforcement learning framework to train two collaborative agents jointly, i.e., a multi-hop graph reasoner and a fact extractor.The fact extraction agent generates fact triples from corpora to enrich the graph on the fly; while the reasoning agent provides feedback to the fact extractor and guides it towards promoting facts that are helpful for the interpretable reasoning.Experiments on two public datasets demonstrate the effectiveness of the proposed approach.
Stagewise Training Accelerates Convergence of Testing Error Over SGD

Stagewise training strategy is widely used for learning neural networks, which runs a stochastic algorithm (e.g., SGD) starting with a relatively large step size (aka learning rate) and geometrically decreasing the step size after a number of iterations. It has been observed that the stagewise SGD has much faster convergence than the vanilla SGD with a polynomially decaying step size in terms of both training error and testing error. {\it But how to explain this phenomenon has been largely ignored by existing studies.} This paper provides some theoretical evidence for explaining this faster convergence. In particular, we consider a stagewise training strategy for minimizing empirical risk that satisfies the Polyak-\L ojasiewicz (PL) condition, which has been observed/proved for neural networks and also holds for a broad family of convex functions. For convex loss functions and two classes of ``nice-behaviored non-convex objectives that are close to a convex function, we establish faster convergence of stagewise training than the vanilla SGD under the PL condition on both training error and testing error. Experiments on stagewise learning of deep residual networks exhibits that it satisfies one type of non-convexity assumption and therefore can be explained by our theory.