Reviewerly/exHarmony huggingface dataset:
An Attention-Based Graph Neural Network for Heterogeneous Structural Learning

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions.
Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

State-of-the-art deep learning based stereo matching approaches treat disparity estimation as a regression problem, where loss function is directly defined on true disparities and their estimated ones. However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained. In this paper, we propose to directly add constraints to the cost volume by filtering cost volume with unimodal distribution peaked at true disparities. In addition, variances of the unimodal distributions for each pixel are estimated to explicitly model matching uncertainty under different contexts. The proposed architecture achieves state-of-the-art performance on Scene Flow and two KITTI stereo benchmarks. In particular, our method ranked the 1st place of KITTI 2012 evaluation and the 4th place of KITTI 2015 evaluation (recorded on 2019.8.20). The codes of AcfNet are available at: https://github.com/youmi-zym/AcfNet.
Data Augmentation for Graph Neural Networks

Data augmentation has been widely used to improve generalizability of machine learning models. However, comparatively little work studies data augmentation for graphs. This is largely due to the complex, non-Euclidean structure of graphs, which limits possible manipulation operations. Augmentation operations commonly used in vision and language have no analogs for graphs. Our work studies graph data augmentation for graph neural networks (GNNs) in the context of improving semi-supervised node-classification. We discuss practical and theoretical motivations, considerations and strategies for graph data augmentation. Our work shows that neural edge predictors can effectively encode class-homophilic structure to promote intra-class edges and demote inter-class edges in given graph structure, and our main contribution introduces the GAug graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction. Extensive experiments on multiple benchmarks show that augmentation via GAug improves performance across GNN architectures and datasets.
Blockchain-Enabled Authentication Handover With Efficient Privacy Protection in SDN-Based 5G Networks

5G mobile networks provide additional benefits in terms of lower latency, higher data rates, and more coverage, in comparison to 4G networks, and they are also coming close to standardization. For example, 5G has a new level of data transfer and processing speed that assures users are not disconnected when they move from one cell to another; thus, supporting faster connection. However, 5G comes with its own technical challenges, such as those relating to authentication handover and user privacy protection. In 5G, for example, the frequent displacement of the users among the cells as a result of repeated authentication handovers often lead to a delay, contradicting the 5G objectives. Using inefficient authentication handover could also cause performance degradation among heterogeneous 5G cells, and increases the possibility of occurring user privacy and security issues. In this paper, we propose a new authentication approach that utilizes blockchain and software defined networking (SDN) techniques to remove the unnecessary re-authentication in repeated handover among heterogeneous cells. The proposed approach is designed to assure the low delay, appropriate for the 5G network in which users are replaced with the least delay among heterogeneous cells using their public and private keys provided by the devised blockchain component while protecting their privacy. In our comparison between Proof-of-Work (POW)-based and network-based models, the delay of our authentication handover is shown to be less than 1 ms. Also, our approach demonstrates less signaling overhead and energy consumption compared to peer models.
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders

Named entity recognition and relation extraction are two important fundamental problems.Joint learning algorithms have been proposed to solve both tasks simultaneously, and many of them cast the joint task as a table-filling problem.However, they typically focused on learning a single encoder (usually learning representation in the form of a table) to capture information required for both tasks within the same space.We argue that it can be beneficial to design two distinct encoders to capture such two different types of information in the learning process.In this work, we propose the novel table-sequence encoders where two different encoders -a table encoder and a sequence encoder are designed to help each other in the representation learning process.Our experiments confirm the advantages of having two encoders over one encoder.On several standard datasets, our model shows significant improvements over existing approaches.
Identifying Users Across Social Tagging Systems

How much do tagging activities tell about a user? Is it possible to identify people in Delicious based on the tags, which they use in Flickr? In this paper we study those questions and investigate whether users can be identified across social tagging systems. We combine two kinds of information: their user ids and their tags. We introduce and compare a variety of approaches to measure the distance between user profiles for identification. With the best performing combination we achieve, depending on the actual settings, accuracies of between 60% and 80% which demonstrates that the traces of Web 2.0 users can reveal quite much about their identity.
Virtual Reality in Marketing: A Framework, Review, and Research Agenda

Marketing scholars and practitioners are showing increasing interest in Extended Reality (XR) technologies (XRs), such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), as very promising technological tools for producing satisfactory consumer experiences that mirror those experienced in physical stores. However, most of the studies published to date lack a certain measure of methodological rigor in their characterization of XR technologies and in the assessment techniques used to characterize the consumer experience, which limits the generalization of the results. We argue that it is necessary to define a rigorous methodological framework for the use of XRs in marketing. This article reviews the literature on XRs in marketing, and provides a conceptual framework to organize this disparate body of work.
AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition

Sensor-based human activity recognition is a fundamental research problem in ubiquitous computing, which uses the rich sensing data from multimodal embedded sensors such as accelerometer and gyroscope to infer human activities. The existing activity recognition approaches either rely on domain knowledge or fail to address the spatial-temporal dependencies of the sensing signals. In this paper, we propose a novel attention-based multimodal neural network model called AttnSense for multimodal human activity recognition. AttnSense introduce the framework of combining attention mechanism with a convolutional neural network (CNN) and a Gated Recurrent Units (GRU) network to capture the dependencies of sensing signals in both spatial and temporal domains, which shows advantages in prioritized sensor selection and improves the comprehensibility. Extensive experiments based on three public datasets show that AttnSense achieves a competitive performance in activity recognition compared with several state-of-the-art methods.
Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

This study aims to propose an effective intelligent model for predicting entrepreneurial intention, which can provide a reasonable reference for the formulation of talent training programs and the guidance of entrepreneurial intention of students.The prediction model is mainly based on the kernel extreme learning machine (KELM) optimized by the improved Harris hawk's optimizer (HHO).In order to obtain better parameters and feature subsets, the Gaussian barebone (GB) strategy is introduced to improve the HHO algorithm, so as to strengthen the optimization ability for tuning parameters of KELM and identifying the compact feature subsets.Then, an optimal KELM model (GBHHO-KELM) is established according to the obtained optimal parameters and feature subsets to predict the entrepreneurial intention of students.In the experiment, GBHHO is compared with the other nine well-known methods in 30 CEC 2014 benchmark problems.The experimental findings suggest that the proposed GBHHO method is significantly superior to the existing methods in most problems.At the same time, GBHHO-KELM is compared with other machine learning methods in the prediction of entrepreneurial intention.The experimental results indicate that the proposed GBHHO-KELM can achieve better classification performance and higher stability in accordance with the four metrics.Therefore, we can conclude that the GBHHO-KELM model is expected to be an effective tool for the prediction of entrepreneurial intention.
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embedding operations with unique irregular memory access patterns that pose a fundamental challenge to accelerate. This paper proposes a lightweight, commodity DRAM compliant, near-memory processing solution to accelerate personalized recommendation inference. The in-depth characterization of production-grade recommendation models shows that embedding operations with high model-, operator and data-level parallelism lead to memory bandwidth saturation, limiting recommendation inference performance. We propose RecNMP which provides a scalable solution to improve system throughput, supporting a broad range of sparse embedding models. RecNMP is specifically tailored to production environments with heavy co-location of operators on a single server. Several hardware/software cooptimization techniques such as memory-side caching, tableaware packet scheduling, and hot entry profiling are studied, providing up to 9.8× memory latency speedup over a highly-optimized baseline. Overall, RecNMP offers 4.2× throughput improvement and 45.8% memory energy savings.
Virtual World as a Resource for Hybrid Education

The virtual worlds in the educational environment, have opened new possibilities to innovation in the teaching-learning process, where the flexibility of access to information inside or outside the classroom through an avatar, tends to a paradigm shift in education traditional, in terms of the way of transmitting and acquiring knowledge through technological means. In this sense, an investigation was carried out on the design, development and use of a metaverse as a digital tool of didactic support to students of the Faculty of Systems Engineering of the University of Cundinamarca, with a view to establishing its viability to be implemented in a course of mathematics and demonstrate the possibilities offered by this type of resource to motivate the student in his training. The metaverse design included various ICT tools, thereby promoting self-learning and collaborative learning. Finally, it was evaluated what was the appreciation of a group of students against their interaction with the metaverse, which validated its usefulness and functionality inside and outside the classroom.
Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models

In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision, and Recall are often insufficient as they fail to give a complete picture of the model’s behavior. We present a probabilistic extension of Precision, Recall, and F1 score, which we refer to as confidence-Precision (cPrecision), confidence-Recall (cRecall), and confidence-F1 (cF1) respectively. The proposed metrics address some of the challenges faced when evaluating large-scale NLP systems, specifically when the model’s confidence score assignments have an impact on the system’s behavior. We describe four key benefits of our proposed metrics as compared to their threshold-based counterparts. Two of these benefits, which we refer to as robustness to missing values and sensitivity to model confidence score assignments are self-evident from the metrics’ definitions; the remaining benefits, generalization, and functional consistency are demonstrated empirically.
GSum: A General Framework for Guided Neural Abstractive Summarization

Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control.While previous studies attempt to provide different types of guidance to control the output and increase faithfulness, it is not clear how these strategies compare and contrast to each other.In this paper, we propose a general and extensible guided summarization framework (GSum) that can effectively take different kinds of external guidance as input, and we perform experiments across several different varieties.Experiments demonstrate that this model is effective, achieving state-of-the-art performance according to ROUGE on 4 popular summarization datasets when using highlighted sentences as guidance.In addition, we show that our guided model can generate more faithful summaries and demonstrate how different types of guidance generate qualitatively different summaries, lending a degree of controllability to the learned models. 1
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation

Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremendous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixellevel annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attentionbased Multi-Context Guiding (A-MCG) network, which consists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between support and query branches, enforcing a better guidance from the support set. In addition, we also adopt a spatial attention along the fusion branch to highlight context information from several scales, enhancing self-supervision in one-shot learning. To address the fusion problem in multi-shot learning, Conv-LSTM is adopted to collaboratively integrate the sequential support features to elevate the final accuracy. Our architecture obtains state-of-the-art on unseen classes in a variant of PASCAL VOC12 dataset and performs favorably against previous work with large gains of 1.1%, 1.4% measured in mIoU in the 1-shot and 5-shot setting.
Impact of Work-Life Balance, Happiness at Work, on Employee Performance

The purpose of this research is to investigate the Relation of work-life balance, happiness, and employee performance, Accordingly, a questionnaire-based survey was designed to test the aforementioned model based on dataset of 289 employees&amp;rsquo; from the ( Med Pharma), Pharmaceutical industries in Jordan, Multiple regression was conducted to examined the research hypotheses. The results indicated that work-life balance and happiness positively and significantly affect employee performance. However, job satisfaction non-impact in employee performance. The results have enormous implication for the Pharmaceutical industries sector in Jordan.
Transferable Adversarial Attacks for Image and Video Object Detection

Identifying adversarial examples is beneficial for understanding deep networks and developing robust models. However, existing attacking methods for image object detection have two limitations: weak transferability---the generated adversarial examples often have a low success rate to attack other kinds of detection methods, and high computation cost---they need much time to deal with video data, where many frames need polluting. To address these issues, we present a generative method to obtain adversarial images and videos, thereby significantly reducing the processing time. To enhance transferability, we manipulate the feature maps extracted by a feature network, which usually constitutes the basis of object detectors. Our method is based on the Generative Adversarial Network (GAN) framework, where we combine a high-level class loss and a low-level feature loss to jointly train the adversarial example generator. Experimental results on PASCAL VOC and ImageNet VID datasets show that our method efficiently generates image and video adversarial examples, and more importantly, these adversarial examples have better transferability, therefore being able to simultaneously attack two kinds of representative object detection models: proposal based models like Faster-RCNN and regression based models like SSD.
A poor man’s coherent Ising machine based on opto-electronic feedback systems for solving optimization problems

Abstract Coherent Ising machines (CIMs) constitute a promising approach to solve computationally hard optimization problems by mapping them to ground state searches of the Ising model and implementing them with optical artificial spin-networks. However, while CIMs promise speed-ups over conventional digital computers, they are still challenging to build and operate. Here, we propose and test a concept for a fully programmable CIM, which is based on opto-electronic oscillators subjected to self-feedback. Contrary to current CIM designs, the artificial spins are generated in a feedback induced bifurcation and encoded in the intensity of coherent states. This removes the necessity for nonlinear optical processes or large external cavities and offers significant advantages regarding stability, size and cost. We demonstrate a compact setup for solving MAXCUT optimization problems on regular and frustrated graphs with 100 spins and can report similar or better performance compared to CIMs based on degenerate optical parametric oscillators.
Best practices for the human evaluation of automatically generated text

Currently, there is little agreement as to how Natural Language Generation (NLG) systems should be evaluated, with a particularly high degree of variation in the way that human evaluation is carried out.This paper provides an overview of how human evaluation is currently conducted, and presents a set of best practices, grounded in the literature.With this paper, we hope to contribute to the quality and consistency of human evaluations in NLG.
Neural Machine Reading Comprehension: Methods and Trends

Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years. Although research on MRC based on deep learning is flourishing, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the work presented in this article. Specifically, we give a thorough review of this research field, covering different aspects including (1) typical MRC tasks: their definitions, differences, and representative datasets; (2) the general architecture of neural MRC: the main modules and prevalent approaches to each; and (3) new trends: some emerging areas in neural MRC as well as the corresponding challenges. Finally, considering what has been achieved so far, the survey also envisages what the future may hold by discussing the open issues left to be addressed.
A training-integrity privacy-preserving federated learning scheme with trusted execution environment

Machine learning models trained on sensitive real-world data promise improvements to everything from medical screening to disease outbreak discovery. In many application domains, learning participants would benefit from pooling their private datasets, training precise machine learning models on the aggregate data, and sharing the profits of using these models. Considering privacy and security concerns often prevent participants from contributing sensitive data for training, researchers proposed several techniques to achieve data privacy in federated learning systems. However, such techniques are susceptible to causative attacks, whereby malicious participants can inject false training results with the aim of corrupting the well-learned model. To end this, in this paper, we propose a new privacy-preserving federated learning scheme that guarantees the integrity of deep learning processes. Based on the Trusted Execution Environment (TEE), we design a training-integrity protocol for this scheme, in which causative attacks can be detected. Thus, each participant is compelled to execute the privacy-preserving learning algorithm of the scheme correctly. We evaluate the performance of our scheme by prototype implementations. The experimental result shows that the scheme is training-integrity and practical.
COVID-19 contact tracing apps: a stress test for privacy, the GDPR, and data protection regimes

Digital surveillance has played a key role in containing the COVID-19 outbreak in China, Singapore, Israel, and South Korea. Google and Apple recently announced the intention to build interfaces to allow Bluetooth contact tracking using Android and iPhone devices. In this article, we look at the compatibility of the proposed Apple/Google Bluetooth exposure notification system with Western privacy and data protection regimes and principles, including the General Data Protection Regulation (GDPR). Somewhat counter-intuitively, the GDPR's expansive scope is not a hindrance, but rather an advantage in conditions of uncertainty such as a pandemic. Its principle-based approach offers a functional blueprint for system design that is compatible with fundamental rights. By contrast, narrower, sector-specific rules such as the US Health Insurance Portability and Accountability Act (HIPAA), and even the new California Consumer Privacy Act (CCPA), leave gaps that may prove difficult to bridge in the middle of an emergency.