Reviewerly/exHarmony huggingface dataset:
BASS: Boosting Abstractive Summarization with Unified Semantic Graph

Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases.Further, a graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process by leveraging the graph structure.Specifically, several graph augmentation methods are designed to encode both the explicit and implicit relations in the text while the graphpropagation attention mechanism is developed in the decoder to select salient content into the summary.Empirical results show that the proposed architecture brings substantial improvements for both long-document and multidocument summarization tasks.
Joint Depth and Density Guided Single Image De-Raining

Single image de-raining is an important and highly challenging problem. To address this problem, some depth or density guided single-image de-raining methods have been developed with encouraging performance. However, these methods individually use the depth or the density to guide the network to conduct image de-raining. In this paper, a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint depth and density guided de-raining</i> (JDDGD) method is technically developed. The JDDGD starts with a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">depth-density inference network</i> (DDINet) to extract the depth and density information from an input rainy image, followed by a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">depth-density-based</i> conditional generative adversarial network (DD-CGAN) to exploit the depth and density information provided by DDINet to achieve adaptive rain streak and fog removal. To prevent the spatially-varying local artifacts, an effective <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global-local discriminators</i> structure is introduced in the proposed DD-CGAN to globally and locally inspect the generated images. In addition, multiple loss functions including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-scale pixel loss</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-scale perceptual loss</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global-local generative adversarial loss</i> are also jointly used to train our model to achieve the best performance. Both quantitative and qualitative results show that the proposed JDDGD method achieves superior performance than previous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-guided</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">density-guided</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">depth-guided de-raining</i> methods.
Effect of Collaboration Mode and Position Arrangement on Immersive Analytics Tasks in Virtual Reality: A Pilot Study

[Background] Virtual reality (VR) technology can provide unique immersive experiences for group users, and especially for analytics tasks with visual information in learning. Providing a shared control/view may improve the task performance and enhance the user experience during VR collaboration. [Objectives] Therefore, this research explores the effect of collaborative modes and user position arrangements on task performance, user engagement, and collaboration behaviors and patterns in a VR learning environment that supports immersive collaborative tasks. [Method] The study involved two collaborative modes (shared and non-shared view and control) and three position arrangements (side-by-side, corner-to-corner, and back-to-back). A user study was conducted with 30 participants divided into three groups (Single, Shared, and Non-Shared) using a VR application that allowed users to explore the structural and transformational properties of 3D geometric shapes. [Results] The results showed that the shared mode would lead to higher task performance than single users for learning analytics tasks in VR. Besides, the side-by-side position got a higher score and more favor for enhancing the collaborative experience. [Conclusion] The shared view would be more suitable for improving task performance in collaborative VR. In addition, the side-by-side position may provide a higher user experience when collaborating in learning VR. From these results, a set of guidelines for the design of collaborative visualizations for VR environments are distilled and presented at the end of the paper. All in all, although our experiment is based on a colocated setting with two users, the results are applicable to both colocated and distributed collaborative scenarios with two or more users.
Sentiment Analysis of Code-Mixed Languages Leveraging Resource Rich Languages

Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages. In this paper, we propose a novel approach called Sentiment Analysis of Code-Mixed Text (SACMT) to classify sentences into their corresponding sentiment - positive, negative or neutral, using contrastive learning. We utilize the shared parameters of siamese networks to map the sentences of code-mixed and standard languages to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score.
Novel decoupling algorithm based on parallel voltage extreme learning machine (PV-ELM) for six-axis F/M sensors

Accurate, time-effective calibration and decoupling procedures of multi-axis Force/Moment (F/M) sensors are critical to the implementation of these sensors. Recently, many decoupling methods have been proposed by researchers, but the inherent coupling relationship among components of multi-axis F/M sensors has not been taken into account. In this paper, we thus proposed a novel Sparse Voltage Maximum Inter-class Variance (SVMIV) algorithm, which took advantage of the inherent relationship nature. Furthermore, a novel nonlinear decoupling method based on the Parallel Voltage-Extreme Learning Machine (PV-ELM) was also presented. To demonstrate the utility of the proposed approach, extensive comparisons were made between the proposed PV-ELM decoupling method and several conventional decoupling approaches such as Least Square (LS), Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), and Extreme Learning Machine (ELM). Results of real decoupling experiments demonstrated that the proposed the PV-ELM decoupling algorithm outperforms linear decoupling algorithms for six-axis F/M sensors. In addition, it was also proved that the PV-ELM decoupling method can decouple the outputs of six-axis F/M sensors with higher precision, faster speed, improved robustness, and faster convergence than the state-of-the-art nonlinear decoupling algorithms such as SVR, BP and ELM. Overall, this paper proposed a novel way to deal with the inherent coupling relationship of six-axis F/M sensors, and the experimental results demonstrated that the maximum I-type and II-type errors were below 0.356% and 0.270%, respectively, of full scale in all measured variables.
Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications. However, current BCNNs are not able to fully explore their corresponding full-precision models, causing a significant performance gap between them. In this paper, we propose rectified binary convolutional networks (RBCNs), towards optimized BCNNs, by combining full-precision kernels and feature maps to rectify the binarization process in a unified framework. In particular, we use a GAN to train the 1-bit binary network with the guidance of its corresponding full-precision model, which significantly improves the performance of BCNNs. The rectified convolutional layers are generic and flexible, and can be easily incorporated into existing DCNNs such as WideResNets and ResNets. Extensive experiments demonstrate the superior performance of the proposed RBCNs over state-of-the-art BCNNs. In particular, our method shows strong generalization on the object tracking task.
Better Concrete Security for Half-Gates Garbling (in the Multi-instance Setting)

We study the concrete security of high-performance implementations of half-gates garbling, which all rely on (hardware-accelerated) AES. We find that current instantiations using k-bit wire labels can be completely broken—in the sense that the circuit evaluator learns all the inputs of the circuit garbler—in time $$O(2^k/C)$$ , where C is the total number of (non-free) gates that are garbled, possibly across multiple independent executions. The attack can be applied to existing circuit-garbling libraries using $$k=80$$ when $$C \approx 10^9$$ , and would require $$267$$ machine-months and cost about $ $$3500$$ to implement on the Google Cloud Platform. Since the attack can be fully parallelized, it could be carried out in about a month using $${\approx }250$$ machines. With this as our motivation, we seek a way to instantiate the hash function in the half-gates scheme so as to achieve better concrete security. We present a construction based on AES that achieves optimal security in the single-instance setting (when only a single circuit is garbled). We also show how to modify the half-gates scheme so that its concrete security does not degrade in the multi-instance setting. Our modified scheme is as efficient as prior work in networks with up to 2 Gbps bandwidth.
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension

In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.Our system is built on a framework of pretraining and fine-tuning, namely D-NET.The techniques of pre-trained language models and multi-task learning are explored to improve the generalization of MRC models and we conduct experiments to examine the effectiveness of these strategies.Our system is ranked at top 1 of all the participants in terms of averaged F1 score.Our codes and models will be released at PaddleNLP 1 .
Neural Graph Embedding for Neural Architecture Search

Existing neural architecture search (NAS) methods often operate in discrete or continuous spaces directly, which ignores the graphical topology knowledge of neural networks. This leads to suboptimal search performance and efficiency, given the factor that neural networks are essentially directed acyclic graphs (DAG). In this work, we address this limitation by introducing a novel idea of neural graph embedding (NGE). Specifically, we represent the building block (i.e. the cell) of neural networks with a neural DAG, and learn it by leveraging a Graph Convolutional Network to propagate and model the intrinsic topology information of network architectures. This results in a generic neural network representation integrable with different existing NAS frameworks. Extensive experiments show the superiority of NGE over the state-of-the-art methods on image classification and semantic segmentation.
A Blockchain-Based System for Anti-Fraud of Healthcare Insurance

The number of healthcare insurance frauds increases year by year, which causes tremendous concern in society. Healthcare insurance fraud exists in various forms, such as falsifying information, concealing third-party liability, falsified electronic bill and so on. The current healthcare insurance system requires a lot of manpower and resources which bears heavily on the system. In this paper, referring to the various forms of healthcare insurance fraud, we propose a healthcare insurance anti-fraud system based on blockchain. The system adopts the architecture based on cloud computing. And organizations such as 120-command centers, public security traffic control departments, judicial organs, healthcare insurance agencies, etc., are included in the blockchain system. And the data of medical expenses, prescriptions, inspection reports, treatment records in the medical process is employed to establish a healthcare insurance blockchain. Based on the blockchain, the system provides medical process inspection service, excessive medical behavior analysis, third-party liability inspection service and medical invoice data review service.
Classical Misuse Attacks on NIST Round 2 PQC

The US National Institute of Standards and Technology (NIST) recently announced the public-key cryptosystems ( $$\mathsf {PKC}$$ ) that have passed to the second round of the post-quantum standardization process. Most of these $$\mathsf {PKC}$$ come in two flavours: a weak IND-CPA version and a strongly secure IND-CCA construction. For the weaker scheme, no level of security is claimed in the plaintext-checking attack (PCA) model. However, previous works showed that, for several NIST candidates, only a few PCA queries are sufficient to recover the secret key. In order to create a more complete picture, we design new key-recovery PCA against several round 2 candidates. Our attacks against CRYSTALS-Kyber, HQC, LAC and SABER are all practical and require only a few thousand queries to recover the full secret key. In addition, we present another KR-PCA attack against the rank-based scheme RQC, which needs roughly $$O(2^{38})$$ queries. Hence, this type of scheme seems to resist better than others to key recovery. Motivated by this observation, we prove an interesting result on the rank metric. Namely, that the learning problem with the rank distance is hard for some parameters, thus invalidating a common strategy for reaction attacks.
How Does Incomplete Composite Refactoring Affect Internal Quality Attributes?

Program refactoring consists of code changes applied to improve the internal structure of a program and, as a consequence, its comprehensibility. Recent studies indicate that developers often perform composite refactorings, i.e., a set of two or more interrelated single refactorings. Recent studies also recommend certain patterns of composite refactorings to fully remove poor code structures, i.e, code smells, thus further improving the program comprehension. However, other recent studies report that composite refactorings often fail to fully remove code smells. Given their failure to achieve this purpose, these composite refactorings are considered incomplete, i.e, they are not able to entirely remove a smelly structure. Unfortunately, there is no study providing an in-depth analysis of the incompleteness nature of many composites and their possibly partial impact on improving, maybe decreasing, internal quality attributes. This paper identifies the most common forms of incomplete composites, and their effect on quality attributes, such as coupling and cohesion, which are known to have an impact on program comprehension. We analyzed 353 incomplete composite refactorings in 5 software projects, two common code smells (Feature Envy and God Class), and four internal quality attributes. Our results reveal that incomplete composite refactorings with at least one Extract Method are often (71%) applied without Move Methods on smelly classes. We have also found that most incomplete composite refactorings (58%) tended to at least maintain the internal structural quality of smelly classes, thereby not causing more harm to program comprehension. We also discuss the implications of our findings to the research and practice of composite refactoring.
Overview of BioASQ 2021: The Ninth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

Advancing the state-of-the-art in large-scale biomedical semantic indexing and question answering is the main focus of the BioASQ challenge. BioASQ organizes respective tasks where different teams develop systems that are evaluated on the same benchmark datasets that represent the real information needs of experts in the biomedical domain. This paper presents an overview of the ninth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2021. In this year, a new question answering task, named Synergy, is introduced to support researchers studying the COVID-19 disease and measure the ability of the participating teams to discern information while the problem is still developing. In total, 42 teams with more than 170 systems were registered to participate in the four tasks of the challenge. The evaluation results, similarly to previous years, show a performance gain against the baselines which indicates the continuous improvement of the state-of-the-art in this field.
Sparse GANs for Thermal Infrared Image Generation From Optical Image

Thermal infrared (TIR) images are not influenced by the illumination variations and can be used in total darkness.With these advantages, TIR technology has a wide application in surveillance systems and various defense systems.However, there are not enough TIR images for wide range of application because the equipment for thermal infrared imaging is expensive and demands strict imaging conditions.To address this problem, we propose a sparse generative model based on pix2pix framework to produce synthetic TIR data from optical RGB images.Considering little texture and color information in TIR images, this model uses a U-net architecture but only selects partial low-level and high-level information for symmetric connections.Specially, we integrate intensity and gradient losses into the objective to train models, which assists generation models to learn more infrared images' characteristics.The experiments on public datasets prove that this proposed method can generate TIR data from optical images.Compared with current pix2pix networks, this method achieves increases by over 6.5% and over 1.2% separately on the metrics of SSIM and PSNR based on the public datasets.The SSIM value even gets an increase by 7% for daytime images.Meanwhile the network parameters decent by 13%.
Learning to Create Better Ads

In the online advertising industry, the process of designing an ad creative i.e., ad text and image) requires manual labor. Typically, each advertiser launches multiple creatives via online A/B tests to infer effective creatives for the target audience, that are then refined further in an iterative fashion. Due to the manual nature of this process, it is time-consuming to learn, refine, and deploy the modified creatives. Since major ad platforms typically run A/B tests for multiple advertisers in parallel, we explore the possibility of collaboratively learning ad creative refinement via A/B tests of multiple advertisers. In particular, given an input ad creative, we study approaches to refine the given ad text and image by: (i) generating new ad text, (ii) recommending keyphrases for new ad text, and (iii) recommending image tags (objects in the image) to select new ad image. Based on A/B tests conducted by multiple advertisers, we form pairwise examples of inferior and superior ad creatives and use such pairs to train models for the above tasks. For generating new ad text, we demonstrate the efficacy of an encoder-decoder architecture with copy mechanism, which allows some words from the (inferior) input text to be copied to the output while incorporating new words associated with higher click-through-rate. For the keyphrase and image tag recommendation task, we demonstrate the efficacy of a deep relevance matching model, as well as the relative robustness of ranking approaches compared to ad text generation in cold-start scenarios with unseen advertisers. We also share broadly applicable insights from our experiments using data from the Yahoo Gemini ad platform.
Distill and Replay for Continual Language Learning

Accumulating knowledge to tackle new tasks without necessarily forgetting the old ones is a hallmark of human-like intelligence.But the current dominant paradigm of machine learning is still to train a model that works well on static datasets.When learning tasks in a stream where data distribution may fluctuate, fitting on new tasks often leads to forgetting on the previous ones.We propose a simple yet effective framework that continually learns natural language understanding tasks with one model.Our framework distills knowledge and replays experience from previous tasks when fitting on a new task, thus named DnR (distill and replay).The framework is based on language models and can be smoothly built with different language model architectures.Experimental results demonstrate that DnR outperfoms previous state-of-the-art models in continually learning tasks of the same type but from different domains, as well as tasks of radically different types.With the distillation method, we further show that it's possible for DnR to incrementally compress the model size while still outperforming most of the baselines.We hope that DnR could promote the empirical application of continual language learning, and contribute to building human-level language intelligence minimally bothered by catastrophic forgetting.
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball

Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. Furthermore, a hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. In addition, we propose an adaptive and fine-grained regularization mechanism to adaptively regularize items and their neighboring representations. Via a comparison using three real-world datasets with state-of-the-art methods, we show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.
Grandparents and Grandchildren Meeting Online: The Role of Material Things in Remote Settings

Grandparents and grandchildren, who cannot meet face-to-face (e.g., due to dislocation or physical distancing induced by a pandemic), often use audio-visual communication tools in order to maintain their relationship online. In a qualitative online survey (n = 85), we inquired into the various ways that grandparents and grandchildren came up with when being physically distant; many of them are tangible in nature as they include “things” or incorporate “spaces”. In this paper, we illustrate related temporal and spatial trajectories and unpack how online meetings are characterized by constant negotiations of agency. We discuss how online meetings could complement face-to-face meetings, instead of mimicking or replacing them. We finally articulate a collection of design sensitivities with the aim to both inspire and question designing for intergenerational online meetings.