Reviewerly/exHarmony huggingface dataset:
Effective Deep Attributed Network Representation Learning With Topology Adapted Smoothing

Attributed networks are ubiquitous in the real world, such as social networks. Therefore, many researchers take the node attributes into consideration in the network representation learning to improve the downstream task performance. In this article, we mainly focus on an untouched "oversmoothing" problem in the research of the attributed network representation learning. Although the Laplacian smoothing has been applied by the state-of-the-art works to learn a more robust node representation, these works cannot adapt to the topological characteristics of different networks, thereby causing the new oversmoothing problem and reducing the performance on some networks. In contrast, we adopt a smoothing parameter that is evaluated from the topological characteristics of a specified network, such as small worldness or node convergency and, thus, can smooth the nodes' attribute and structure information adaptively and derive both robust and distinguishable node features for different networks. Moreover, we develop an integrated autoencoder to learn the node representation by reconstructing the combination of the smoothed structure and attribute information. By observation of extensive experiments, our approach can preserve the intrinsical information of networks more effectively than the state-of-the-art works on a number of benchmark datasets with very different topological characteristics.
Unsupervised meta-learning for few-shot learning

Meta-learning is an effective tool to address the few-shot learning problem, which requires new data to be classified considering only a few training examples. However, when used for classification, it requires large labeled datasets, which are not always available in practice. In this paper, we propose an unsupervised meta-learning algorithm that learns from an unlabeled dataset and adapts to downstream human-specific tasks with few labeled data. The proposed algorithm constructs tasks using clustering embedding methods and data augmentation functions to satisfy two critical class distinction requirements. To alleviate the biases and the weak diversity problem introduced by data augmentation functions, the proposed algorithm uses two methods, which are shifting the feeding data between the inner-outer loops and a novel data augmentation function. We further provide theoretical analysis of the effect of augmentation data in the inner/outer loop. Experiments on the MiniImagenet and Omniglot datasets demonstrate that the proposed unsupervised meta-learning approach outperforms other tested unsupervised representation learning approaches and two recent unsupervised meta-learning baselines. Compared with supervised meta-learning approaches, certain results produced by our method are quite close to those produced by such methods trained on the human-designed labeled tasks.
Visual question answering model based on graph neural network and contextual attention

Visual Question Answering (VQA) has recently appeared as a hot research area in the field of computer vision and natural language processing. A VQA model uses both image and question features and fuses them to predict an answer for a given natural question related to an image. However, most VQA approaches using attention mechanism mainly concentrate on extraction of visual information from regions of interests for answer prediction and ignore the relation between the regions of interests together with the reasoning among these regions. Apart from this limitation, VQA approaches also ignore the regions which are previously attended for answer generation. These regions which are attended in past can guide the selection of the subsequent regions of attention. In this paper, a novel VQA model is presented and formulated that utilizes this relationship between the regions and employs visual context based attention that takes into account the previously attended visual content. Experimental results demonstrate that the proposed VQA model boosts the accuracy of answer prediction on publically available datasets VQA 1.0 and VQA 2.0.
A visually secure image encryption scheme based on 2D compressive sensing and integer wavelet transform embedding

Using a certain image encryption algorithm to encrypt plain image into visually meaningless noise image is a common way to protect the security of image data. Nevertheless, meaningless noisy image will cause more attention during the transmission process and thus results in lots of attacks. Here, we propose a visually secure image encryption scheme based on two-dimensional compressive sensing (2DCS) and integer wavelet transform (IWT) embedding. During the encryption process, the confusion and diffusion of plain image are realized through random scrambling and grayscale transformation, then a pre-encrypted image is obtained, and then the pre-encrypted image is re-encrypted into a secret image via 2DCS. During the embedding process, the carrier image is converted into a coefficient matrix through IWT, then embedding technology is used to embed the secret image into the coefficient matrix to obtain the modified coefficient matrix, and then it is transformed into the spatial domain through inverse IWT to achieve the final meaningful cipher image. The application of 2DCS can ensure data security and increase the speed of the decryption algorithm, and the IWT embedding can achieve visual security without loss of information. The effectiveness, data security, visual security and real-time performance of the scheme have been tested in simulation results and comprehensive performance analysis.
Enhancing underwater image via adaptive color and contrast enhancement, and denoising

Images captured under water are often characterized by low contrast, color distortion, and noise, hindering some visual tasks carried out on it. Despite remarkable breakthrough has been made in recent years, effective and robust enhancement of degraded image remains a challenging problem. To improve the quality of underwater images, we propose a novel scheme by constructing an adaptive color and contrast enhancement, and denoising (ACCE-D) framework. In the proposed framework, Difference of Gaussian (DoG) filter and bilateral filter are respectively employed to decompose the high-frequency and low-frequency components. Benefited from this separation, we utilize soft-thresholding operation to suppress the noise in the high-frequency component. Specially, the low-frequency component is enhanced by using an adaptive color and contrast enhancement (ACCE) strategy. Moreover, we derive a numerical solution for ACCE, and adopt a pyramid-based strategy to accelerate the solving procedure. Both qualitative and quantitative experiments demonstrate that our strategy is effective in color correction, contrast enhancement, and detail revealing. In the quantitative evaluations, by performing on the 890 real-world underwater images from UIEBD, the proposed method obtains 0.65 UCIQE, 1.59 UIQM, 0.81 FDUM, 1.34 PCQI, 0.62 CBPD, and 7.75 entropy scores, achieving average increase of 5% comparing with several state-of-the-art methods. Furthermore, we have verified the utility of our proposed ACCE-D for enhancing other types of degraded scenes, including foggy scene, sandstorm scene and low-light scene.
Alignment Restricted Streaming Recurrent Neural Network Transducer

There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications. RNN-T is trained with a loss function that does not enforce temporal alignment of the training transcripts and audio. As a result, RNN-T models built with uni-directional long short term memory (LSTM) encoders tend to wait for longer spans of input audio, before streaming already decoded ASR tokens. In this work, we propose a modification to the RNN-T loss function and develop Alignment Restricted RNN-T (Ar-RNN-T) models, which utilize audio-text alignment in-formation to guide the loss computation. We compare the proposed method with existing works, such as monotonic RNN-T, on LibriSpeech and in-house datasets. We show that the Ar-RNN-T loss provides a refined control to navigate the trade-offs between the token emission delays and the Word Error Rate (WER). The Ar-RNN-T models also improve downstream applications such as the ASR End-pointing by guaranteeing token emissions within any given range of latency. Moreover, the Ar-RNN-T loss allows for bigger batch sizes and 4 times higher throughput for our LSTM model architecture, enabling faster training and convergence on GPUs.
Multivariate analysis of teachers’ digital information skills - The importance of available resources

Digital information skills are prerequisites for success in the information society. Previous research on 21st century skills has shown that digital skills evolve sequentially, building on digital information skills, making them the key to future skills that teachers should acquire and be able to pass on to future generations. In the research on digital skills, shortcomings of bivariate approaches have been widely identified and interest has largely shifted to multivariate methods. However, the development of effective targeted interventions still requires a deeper understanding of the social resources of individuals and the interdependencies of predictors. This study examined the extent to which sociodemographic factors and available resources predict digital information skills and which subgroups emerge as potential targets for interventions, by analysing the interdependence of the predictor variables. The empirical data used in the study consisted of 4988 Finnish teachers' questionnaire responses and performance test results. Utilising the multiple regression analysis, the study found that digital activity and age explain most of the variation in teachers' digital information skills. Their effect was found to be the inverse of each other, with abundant digital activity increasing and age reducing teachers’ mastery of these skills. Digital self-efficacy and in-service training also emerged as promising predictors, highlighting that teachers' information skills are explained more strongly by the available resources than by sociodemographic factors. At the end, the practical significance and recommendations for more targeted interventions as well as for further research are considered on the basis of the results obtained.
Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation

Quantum metrology promises unprecedented measurement precision but suffers in practice from the limited availability of resources such as the number of probes, their coherence time, or nonclassical quantum states. The adaptive Bayesian approach to parameter estimation allows an efficient use of resources because of adaptive experiment design. For its practical success, fast numerical solutions for the Bayesian update and the adaptive experiment design are crucial. Here we show that neural networks can be trained to become fast and strong experiment-design heuristics using a combination of an evolutionary strategy and reinforcement learning. Neural-network heuristics are shown to outperform established heuristics for the technologically important example of frequency estimation of a qubit that suffers from dephasing. Our method of creating neural-network heuristics is very general and complements the well-studied sequential Monte Carlo method for Bayesian updates to form a complete framework for adaptive Bayesian quantum estimation.Received 27 May 2020Accepted 17 March 2021DOI:https://doi.org/10.1103/PRXQuantum.2.020303Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum controlQuantum metrologyQuantum sensingQuantum Information
Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art recommendation performance. In the ubiquitous implicit feedback based CF, users' unobserved behaviors are treated as unlinked edges in the user-item bipartite graph. As users' unobserved behaviors are mixed with dislikes and unknown positive preferences, the fixed graph structure input is missing with potential positive preference links. In this paper, we study how to better learn enhanced graph structure for CF. We argue that node embedding learning and graph structure learning can mutually enhance each other in CF, as updated node embeddings are learned from previous graph structure, and vice versa ~(i.e., newly updated graph structure are optimized based on current node embedding results). Some previous works provided approaches to refine the graph structure. However, most of these graph learning models relied on node features for modeling, which are not available in CF. Besides, nearly all optimization goals tried to compare the learned adaptive graph and the original graph from a local reconstruction perspective, whether the global properties of the adaptive graph structure are modeled in the learning process is still unknown. To this end, in this paper, we propose an enhanced graph learning network EGLN approach for CF via mutual information maximization. The key idea of EGLN is two folds: First, we let the enhanced graph learning module and the node embedding module iteratively learn from each other without any feature input. Second, we design a local-global consistency optimization function to capture the global properties in the enhanced graph learning process. Finally, extensive experimental results on three real-world datasets clearly show the effectiveness of our proposed model.
Multi-label Arabic text classification in Online Social Networks

Online Social Networks (OSNs) are the most popular interactive media for communicating, posting, and sharing indefinite amounts of personal information. However, along with interesting and attractive topics and contents, some users neither like the fact that certain topics that are not among their interests can fill their personal pages nor do they wish to see disappointing negative posts that may appear repeatedly. Also, people sometimes post inappropriate or abusive content on these networks, such as insults or pornography. Most of the efforts in the field of text classification have focused on the English language, while research on the Arabic language, which has numerous challenges is scarce. In this paper, we constructed a standard multi-label Arabic dataset using manual annotation and a semi-supervised annotation technique that can be used for short text classification, sentiment analysis, and multilabel classification. Then, we evaluated the topics classification, sentiment analysis, and multilabel classification. Based on that evaluation we found a relationship between topics published in OSNs and hate speech. The experimental results validate the effectiveness of the proposed technique. • We construct a standard multi-label Arabic dataset using manual annotation and semi-supervised annotation techniques. • We train machine-learning models for topic classification, sentiment analysis, and multilabel classification in OSNs. • We examine the relationship between topics published in OSNs and hate speech. • We propose a technique to filter social networks contents.
Sample-level Data Selection for Federated Learning

Federated learning (FL) enables participants to collaboratively construct a global machine learning model without sharing their local training data to the remote server. In FL systems, the selection of training samples has a significant impact on model performances, e.g., selecting participants whose datasets have erroneous samples, skewed categorical distributions, and low content diversity would result in low accuracy and unstable models. In this work, we aim to solve the exigent optimization problem that selects a collection of high-quality training samples for a given FL task under a monetary budget in a privacy-preserving way, which is extremely challenging without visibility to participants' local data and training process. We provide a systematic analysis of important data related factors affecting the model performance and propose a holistic design to privately and efficiently select high-quality data samples considering all these factors. We verify the merits of our proposed solution with extensive experiments on a real AIoT system with 50 clients, including 20 edge computers, 20 laptops, and 10 desktops. The experimental results validates that our solution achieves accurate and efficient selection of high-quality data samples, and consequently an FL model with a faster convergence speed and higher accuracy than that achieved by existing solutions.
An ISM Modeling of Barriers for Blockchain/Distributed Ledger Technology Adoption in Supply Chains towards Cybersecurity

Over the last few years, the increasing level of cyber risks derived from the growing connectedness of Industry 4.0 has led to the emergence of blockchain technology as a major innovation in supply chain cybersecurity. The main purpose of this study is to identify and rank the significant barriers affecting the implementation of blockchain technology as a key component of cyber supply chain risk management (CSCRM). This research relied on the “interpretive structural modeling (ISM)” technique in the structure of a hierarchical model to investigate the contextual relationships of identified challenges for blockchain adoption in CSCRM; it also classifies the influential challenges based on their driving and dependence powers. The results highlight that “cryptocurrency volatility” is the challenge at the top level of the hierarchy, implying weak driving power but it is strongly dependent on the other challenges. “Poor regulatory provisions”, “technology immaturity”, “dependent on input information from external oracles”, “scalability and bandwidth issues”, and “smart contract issues” are significant challenges for the adoption of blockchain in cyber supply chain risk management and are located at the bottom level of the hierarchy with higher driving power. The implications for theory and practice of the research are also highlighted.
Novel Feature Fusion Module-Based Detector for Small Insulator Defect Detection

The failure of an insulator may compromise the safety of the entire power transmission system. Therefore, insulator defect detection is vital for the safe operation of power systems. However, insulator defects in an insulator image may have varying sizes, and several currently available methods do not have satisfactory detection accuracy for small defects. To address this issue, we propose an improved detection network for small insulator defects with a batch normalization convolutional block attention module (BN-CBAM) and a feature fusion module. The BN-CBAM is designed to better exploit channel information and enhance the effect of different channels on the feature map. In addition, we propose a feature fusion module that fuses multi-scale features from different layers to improve small object detection performance. Moreover, to address the scarcity of aerial images, a data augmentation method based on the fusion of the target segment and background is introduced. Experiments demonstrate that the proposed method achieves better small insulator defect detection performance than other state-of-the-art approaches. In addition, data augmentation methods enrich sample diversity and enhance the generalizability of the network.
A<sup>2</sup>L: Anonymous Atomic Locks for Scalability in Payment Channel Hubs

Payment channel hubs (PCHs) constitute a promising solution to the inherent scalability problem of blockchain technologies, allowing for off-chain payments between sender and receiver through an intermediary, called the tumbler. While state-of-the-art PCHs provide security and privacy guarantees against a malicious tumbler, they do so by relying on the scripting-based functionality available only at few cryptocurrencies, and they thus fall short of fundamental properties such as backwards compatibility and efficiency.In this work, we present the first PCH protocol to achieve all aforementioned properties. Our PCH builds upon A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L, a novel cryptographic primitive that realizes a three-party protocol for conditional transactions, where the tumbler pays the receiver only if the latter solves a cryptographic challenge with the help of the sender, which implies the sender has paid the tumbler. We prove the security and privacy guarantees of A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L (which carry over to our PCH construction) in the Universal Composability framework and present a provably secure instantiation based on adaptor signatures and randomizable puzzles. We implemented A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L and compared it to TumbleBit, the state-of-the-art Bitcoin-compatible PCH. Asymptotically, A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L has a communication complexity that is constant, as opposed to linear in the security parameter like in TumbleBit. In practice, A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L requires ~33x less bandwidth than TumleBit, while retaining the computational cost (or providing 2x speedup with a preprocessing technique). This demonstrates that A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L (and thus our PCH construction) is ready to be deployed today.In theory, we demonstrate for the first time that it is possible to design a secure and privacy-preserving PCH while requiring only digital signatures and timelock functionality from the underlying scripting language. In practice, this result makes our PCH backwards compatible with virtually all cryptocurrencies available today, even those offering a highly restricted form of scripting language such as Ripple or Stellar. The practical appealing of our construction has resulted in a proof-of-concept implementation in the COMIT Network, a blockchain technology focused on cross-currency payments.
Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation

Self-attention networks (SANs) have been intensively applied for sequential recommenders, but they are limited due to: (1) the quadratic complexity and vulnerability to over-parameterization in self-attention; (2) inaccurate modeling of sequential relations between items due to the implicit position encoding. In this work, we propose the low-rank decomposed self-attention networks (LightSANs) to overcome these problems. Particularly, we introduce the low-rank decomposed self-attention, which projects user's historical items into a small constant number of latent interests and leverages item-to-interest interaction to generate the context-aware representation. It scales linearly w.r.t. the user's historical sequence length in terms of time and space, and is more resilient to over-parameterization. Besides, we design the decoupled position encoding, which models the sequential relations between items more precisely. Extensive experimental studies are carried out on three real-world datasets, where LightSANs outperform the existing SANs-based recommenders in terms of both effectiveness and efficiency.