Reviewerly/exHarmony huggingface dataset:
Intra- and Inter-Reasoning Graph Convolutional Network for Saliency Prediction on 360° Images

Cubic projection can be utilized to divide 360° images into multiple rectilinear images, with little distortion and semantic relevance.However, the existing saliency prediction models fail to integrate semantic information.In this paper, we address this by proposing an intra-and inter-reasoning graph convolutional network for saliency prediction on 360° images (SalReGCN360).The whole framework contains six sub-networks, each of which contains two branches.In the training phase, after utilizing Multiple Cubic Projection (MCP), six rectilinear images are simultaneously put into corresponding sub-networks.In one of the branches, the global features of a single rectilinear image are extracted by the intra-graph inference module.In the other branch, the semantic information of the six rectilinear images is integrated by the inter-graph inference module.Then, the feature maps are generated by fusing the two branches, and six corresponding rectilinear saliency maps are predicted.In the testing phase, the Multiple Sphere Rotation (MSR) projection method is applied to obtain multiple new equirectangular images, and the images are predicted and fused to generate the saliency map.Extensive experiments on two datasets show that the proposed model has improved, with different degrees in the four saliency evaluation metrics.
Software-Defined Heterogeneous Quantum Key Distribution Chaining: An Enabler for Multi-Protocol Quantum Networks

Quantum key distribution (QKD) is an essential branch of quantum communications that facilitates future-proof security. In the near future, large-scale quantum networks will inevitably encounter diverse QKD protocols, ultimately resulting in a multi-protocol quantum network. Motivated by the evolution of advanced QKD protocols, this article introduces a pivotal enabler for multi-protocol quantum networks: software-defined heterogeneous QKD chaining (HQKDC). In contrast to the conventional QKD chain using a single protocol and a single connectivity pattern, a heterogeneous QKD chain is established relying on multiple protocols and diverse connectivity patterns. We elaborate the HQKDC framework by describing the structures of various QKD chains and proposing an architecture of software-defined HQKDC over optical networks. The software defined networking paradigm and optical networks can facilitate the orchestration and deployment of heterogeneous QKD chains, respectively. Furthermore, we present and evaluate the strategies for heterogeneous QKD chain deployment, as well as devise and demonstrate the workflow for heterogeneous QKD chain orchestration. Finally, we discuss open issues toward the practical QKD network and quantum Internet.
Medical Diagnosis and Pattern Recognition Based on Generalized Dice Similarity Measures for Managing Intuitionistic Hesitant Fuzzy Information

Pattern recognition is the computerized identification of shapes, designs, and reliabilities in information. It has applications in information compression, machine learning, statistical information analysis, signal processing, image analysis, information retrieval, bioinformatics, and computer graphics. Similarly, a medical diagnosis is a procedure to illustrate or identify diseases or disorders, which would account for a person’s symptoms and signs. Moreover, to illustrate the relationship between any two pieces of intuitionistic hesitant fuzzy (IHF) information, the theory of generalized dice similarity (GDS) measures played an important and valuable role in the field of genuine life dilemmas. The main influence of GDS measures is that we can easily obtain a lot of measures by using different values of parameters, which is the main part of every measure, called DGS measures. The major influence of this theory is to utilize the well-known and valuable theory of dice similarity measures (DSMs) (four different types of DSMs) under the assumption of the IHF set (IHFS), because the IHFS covers the membership grade (MG) and non-membership grade (NMG) in the form of a finite subset of [0, 1], with the rule that the sum of the supremum of the duplet is limited to [0, 1]. Furthermore, we pioneered the main theory of generalized DSMs (GDSMs) computed based on IHFS, called the IHF dice similarity measure, IHF weighted dice similarity measure, IHF GDS measure, and IHF weighted GDS measure, and computed their special cases with the help of parameters. Additionally, to evaluate the proficiency and capability of pioneered measures, we analyzed two different types of applications based on constructed measures, called medical diagnosis and pattern recognition problems, to determine the supremacy and consistency of the presented approaches. Finally, based on practical application, we enhanced the worth of the evaluated measures with the help of a comparative analysis of proposed and existing measures.
Computational Scientific Discovery in Psychology

Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.
Using deep temporal convolutional networks to just-in-time forecast technical debt principal

Technical debt is a widely used metaphor to summarize all the consequences of poorly written code. Managing technical debt is important for software developers to allow adequate planning for software maintenance and improvement activities, such as refactoring and preventing system degradation. Several studies in the literature investigate the identification of technical debt and its consequences. This work aims to explore a deep learning approach to just-in-time predict the impact on technical debt when changes are performed on the source code. In this way the developer can work better, trying to improve the quality of the code that is being modified. Knowing what the TD trend will be in just-in-time source code with the change made is the key to avoiding a project taking a long time to remediate or improve. The model exploits the knowledge of quality and ad-hoc process metrics evolution over time. To validate the approach, a large dataset, including metrics evaluated from commits of ten Java software projects, was built. The results obtained show the effectiveness of the proposed approach in predicting the Technical Debt accumulation within the source code.
DenseUNet: Improved image classification method using standard convolution and dense transposed convolution

U-Net series models have achieved considerable success in various fields such as image segmentation and image classification. However, the decoders in these models often use transposed convolution (TC) from level to level, reducing the representation ability of the features. Therefore, we proposed a DenseUNet model that adopts dense TC to ensure maximum information flow from lower layers to higher ones. DenseUNet comprises four blocks, the output of each block being transposed several times with different strides based on its size. All transposed results of the same size are concatenated together through a skip connection and then fused with the same size result of the first convolutional layer of the corresponding block. Multiscale TC operations restore the feature size at different scales to supplement the important features lost in the pooling layers. By progressively accumulating features from different paths, DenseUNet improves the representational ability of features and enhances the robustness of classification. We evaluated the model on four image classification benchmark datasets – namely, CIFAR-10, CIFAR-100, SVHN, and FMNIST – using three U-Net series models (U-Net, TernausNet, and CrackU-Net) and four classical classification models (VGG16, VGG19, ResNeXt, and DenseNet). The experimental results showed that our model has stable training performance and excellent test accuracy.
Using mobile applications in the study of vocal skills

This article reflects the results of a study involving second- and fourth-year students from Harbin University (Harbin, China), the Chinese Academy of Arts (Hangzhou, China), and the Central Academy of Drama (Beijing, China). The work analyzed the impact of specialized mobile applications Vox Tools: Learn to Sing and Swiftscales Vocal Trainer on the vocal learning process, depending on the age and gender of students. The study, which ran from February to June during the academic year 2020-2021, involved 180 students. The study confirms the effectiveness of multimedia, demonstrating the higher scores of the experimental groups on the five assessment criteria, compared with the control groups, which did not use mobile applications. It was also found that age in two groups of students (second and fourth year) had no effect on performance, regardless of whether the mobile app was used for vocal training. A correlation was found between age and overall student performance, with second-year students showing lower grades in all groups compared to their fourth-year counterparts. Research on current mobile vocal training apps can help improve vocal learning.
High-order cross-scale attention network for single image super-resolution

Deep convolutional neural networks (DCNN) have achieved remarkable performance for single image super-resolution (SISR). Although some recent works have introduced multi-scale representation into DCNN-based super-resolution, they neglect to explore the interdependencies across different scales. To address this issue, we propose a high-order cross-scale attention network (HOCSANet) for SISR reconstruction. Specifically, a novel high-order cross-scale attention block (HOCSABlock) is developed to integrate the multi-scale representation with attention mechanism, in which both in-scale and cross-scale feature correlations are exploited and utilized to adaptively rescale the multi-scale features. Moreover, we introduce a dual-nested residual block (DNRBlock), which combines plain residual block and wide-activated residual block and learns more informative features from the difference between them. Experimental results demonstrate the superiority of the proposed HOCSANet over several state-of-the-art SISR methods.
Class-specific representation based distance metric learning for image set classification

Image set classification, which compares the similarity between image sets with variable quantity, quality and unordered heterogeneous images, has drawn increased research attention in recent years. Although many effective image set classification algorithms have been developed, they struggle to overcome issues such as intra-set diversity, inter-set similarity, and input data that are not linearly separable. In this paper, we propose a class-specific representation based distance metric learning (CSRbDML) framework to improve the classification performance. Specifically, CSRbDML aims to learn an inter-set distance metric on a kernel space such that the distance between truly matching sets is smaller than that between incorrectly matching sets. Furthermore, we also propose a novel and powerful image set classifier based on the learned distance metric. Extensive experiments on several well-known benchmark datasets demonstrate the effectiveness of the proposed methods compared with the existing image set classification algorithms.
Variational Graph Author Topic Modeling

While Variational Graph Auto-Encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpretability. Exploring hidden topics within documents and discovering key words associated with each topic allow us to develop a semantic interpretation of the corpus. Moreover, documents are usually associated with authors. For example, news reports have journalists specializing in writing certain type of events, academic papers have authors with expertise in certain research topics, etc. Modeling authorship information could benefit topic modeling, since documents by the same authors tend to reveal similar semantics. This observation also holds for documents published on the same venues. However, most topic models ignore the auxiliary authorship and publication venues. Given above two challenges, we propose a Variational Graph Author Topic Model for documents to integrate both semantic interpretability and authorship and venue modeling into a unified VGAE framework. For authorship and venue modeling, we construct a hierarchical multi-layered document graph with both intra- and cross-layer topic propagation. For semantic interpretability, three word relations (contextual, syntactic, semantic) are modeled and constitute three word sub-layers in the document graph. We further propose three alternatives for variational divergence. Experiments verify the effectiveness of our model on supervised and unsupervised tasks.
MT-FlowFormer: A Semi-Supervised Flow Transformer for Encrypted Traffic Classification

With the increasing demand for the protection of personal network meta-data, encrypted networks have grown in popularity, so do the challenge of monitoring and analyzing encrypted network traffic. Currently, some deep learning-based methods have been proposed to leverage statistical features for encrypted traffic classification, which are barely affected by encryption techniques. However, these works still suffer from two main intrinsic limitations: (1) the feature extraction process lacks a mechanism to take into account correlations between flows in the flow sequence; and (2) a large volume of manually-labeled data is required for training an effective deep classifier. In this paper, we propose a novel semi-supervised framework to address these problems. To be specific, an efficient classifier with attention mechanism is proposed to extract features from flow sequences with low computational cost. Then, a Mean Teacher-style semi-supervised framework is adopted to exploit the unlabeled traffic data, where a spatiotemporal data augmentation method is designed as the key component to explore the spatial and temporal relationship within the unlabeled traffic data. Experimental results on two real-world traffic datasets demonstrate that our method outperforms state-of-the-art methods with a large margin.
Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation

Unsupervised domain adaptation (UDA) has become an appealing approach for knowledge transfer from a labeled source domain to an unlabeled target domain. However, when the classes in source and target domains are imbalanced, most existing UDA methods experience significant performance drop, as the decision boundary usually favors the majority classes. Some recent class-imbalanced domain adaptation (CDA) methods aim to tackle the challenge of biased label distribution by exploiting pseudo-labeled target samples during training process. However, these methods suffer from the issues with unreliable pseudo labels and error accumulation during training. In this paper, we propose a pairwise adversarial training approach for class-imbalanced domain adaptation. Unlike conventional adversarial training in which the adversarial samples are obtained from the lp ball of the original samples, we generate adversarial samples from the interpolated line of the aligned pairwise samples from source and target domains. The pairwise adversarial training (PAT) is a novel data-augmentation method which can be integrated into existing UDA models to tackle with the CDA problem. Experimental results and ablation studies show that the UDA models integrated with our method achieve considerable improvements on benchmarks compared with the original models as well as the state-of-the-art CDA methods. Our source code is available at: https://github.com/DamoSWL/Pairwise-Adversarial-Training
Streaming Graph Neural Networks with Generative Replay

Training Graph Neural Networks (GNNs) incrementally is a particularly urgent problem, because real-world graph data usually arrives in a streaming fashion, and inefficiently updating of the models results in out-of-date embeddings, thus degrade its performance in downstream tasks. Traditional incremental learning methods will gradually forget old knowledge when learning new patterns, which is the catastrophic forgetting problem. Although saving and revisiting historical graph data alleviates the problem, the storage limitation in real-world applications reduces the amount of saved data, causing GNN to forget other knowledge. In this paper, we propose a streaming GNN based on generative replay, which can incrementally learn new patterns while maintaining existing knowledge without accessing historical data. Specifically, our model consists of the main model (GNN) and an auxiliary generative model. The generative model based on random walks with restart can learn and generate fake historical samples (i.e., nodes and their neighborhoods), which can be trained with real data to avoid the forgetting problem. Besides, we also design an incremental update algorithm for the generative model to maintain the graph distribution and for GNN to capture the current patterns. Our model is evaluated on different streaming data sets. The node classification results prove that our model can update the model efficiently and achieve comparable performance to model retraining. Code is available at https://github.com/Junshan-Wang/SGNN-GR.
Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning

Driven by the exponential increase of software and the advent of the pull-based development system Git, a large amount of open-source software has emerged on various social coding platforms. GitHub, as the largest platform, not only attracts developers and researchers to contribute legitimate software and research-related source code but has also become a popular platform for an increasing number of cybercriminals to perform continuous cyberattacks. Hence, some tools have been developed to learn representations of repositories on GitHub for various related applications (e.g., malicious repository detection) recently. However, most of them merely focus on code content while ignoring the rich relational data among repositories. In addition, they usually require a mass of resources to obtain sufficient labeled data for model training while ignoring the usefully handy unlabeled data. To this end, we propose a novel model Rep2Vec which integrates the code content, the structural relations, and the unlabeled data to learn the repository representations. First, to comprehensively model the repository data, we build a repository heterogeneous graph (Rep-HG) which is encoded by a graph neural network. Afterwards, to fully exploit unlabeled data in Rep-HG, we introduce adversarial attacks to generate more challenging contrastive pairs for the contrastive learning module to train the encoder in node view and meta-path view simultaneously. To alleviate the workload of the encoder against attacks, we further design a dual-stream contrastive learning module that integrates contrastive learning on adversarial graph and original graph together. Finally, the pre-trained encoder is fine-tuned to the downstream task, and further enhanced by a knowledge distillation module. Extensive experiments on the collected dataset from GitHub demonstrate the effectiveness of Rep2Vec in comparison with state-of-the-art methods for multiple repository tasks.
Uni-Retriever: Towards Learning the Unified Embedding Based Retriever in Bing Sponsored Search

Embedding based retrieval (EBR) is a fundamental building block in many web applications. However, EBR in sponsored search is distinguished from other generic scenarios and technically challenging due to the need of serving multiple retrieval purposes: firstly, it has to retrieve high-relevance ads, which may exactly serve user's search intent; secondly, it needs to retrieve high-CTR ads so as to maximize the overall user clicks. In this paper, we present a novel representation learning framework Uni-Retriever developed for Bing Search, which unifies two different training modes knowledge distillation and contrastive learning to realize both required objectives. On one hand, the capability of making high-relevance retrieval is established by distilling knowledge from the "relevance teacher model''. On the other hand, the capability of making high-CTR retrieval is optimized by learning to discriminate user's clicked ads from the entire corpus. The two training modes are jointly performed as a multi-objective learning process, such that the ads of high relevance and CTR can be favored by the generated embeddings. Besides the learning strategy, we also elaborate our solution for EBR serving pipeline built upon the substantially optimized DiskANN, where massive-scale EBR can be performed with competitive time and memory efficiency, and accomplished in high-quality. We make comprehensive offline and online experiments to evaluate the proposed techniques, whose findings may provide useful insights for the future development of EBR systems. Uni-Retriever has been mainstreamed as the major retrieval path in Bing's production thanks to the notable improvements on the representation and EBR serving quality.