pwc-archive/papers-with-abstracts huggingface dataset:
ArXivDigest: A Living Lab for Personalized Scientific Literature Recommendation
Authors: Kristian Gingstad, Øyvind Jekteberg, Krisztian Balog
Date: 2020-09-24 00:00:00

Providing personalized recommendations that are also accompanied by explanations as to why an item is recommended is a research area of growing importance. At the same time, progress is limited by the availability of open evaluation resources. In this work, we address the task of scientific literature recommendation. We present arXivDigest, which is an online service providing personalized arXiv recommendations to end users and operates as a living lab for researchers wishing to work on explainable scientific literature recommendations.
Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases
Authors: Gerhard Weikum, Luna Dong, Simon Razniewski, Fabian Suchanek
Date: 2020-09-24 00:00:00
Tasks: Knowledge Graphs, Question Answering

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.
Local Context Attention for Salient Object Segmentation
Authors: Jing Tan, Pengfei Xiong, Yuwen He, Kuntao Xiao, Zhengyi Lv
Date: 2020-09-24 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation

Salient object segmentation aims at distinguishing various salient objects from backgrounds. Despite the lack of semantic consistency, salient objects often have obvious texture and location characteristics in local area. Based on this priori, we propose a novel Local Context Attention Network (LCANet) to generate locally reinforcement feature maps in a uniform representational architecture. The proposed network introduces an Attentional Correlation Filter (ACF) module to generate explicit local attention by calculating the correlation feature map between coarse prediction and global context. Then it is expanded to a Local Context Block(LCB). Furthermore, an one-stage coarse-to-fine structure is implemented based on LCB to adaptively enhance the local context description ability. Comprehensive experiments are conducted on several salient object segmentation datasets, demonstrating the superior performance of the proposed LCANet against the state-of-the-art methods, especially with 0.883 max F-score and 0.034 MAE on DUTS-TE dataset.
Dynamic Similarity Search on Integer Sketches
Authors: Shunsuke Kanda, Yasuo Tabei
Date: 2020-09-24 00:00:00

Similarity-preserving hashing is a core technique for fast similarity searches, and it randomly maps data points in a metric space to strings of discrete symbols (i.e., sketches) in the Hamming space. While traditional hashing techniques produce binary sketches, recent ones produce integer sketches for preserving various similarity measures. However, most similarity search methods are designed for binary sketches and inefficient for integer sketches. Moreover, most methods are either inapplicable or inefficient for dynamic datasets, although modern real-world datasets are updated over time. We propose dynamic filter trie (DyFT), a dynamic similarity search method for both binary and integer sketches. An extensive experimental analysis using large real-world datasets shows that DyFT performs superiorly with respect to scalability, time performance, and memory efficiency. For example, on a huge dataset of 216 million data points, DyFT performs a similarity search 6,000 times faster than a state-of-the-art method while reducing to one-thirteenth in memory.
Multi-View Brain HyperConnectome AutoEncoder For Brain State Classification
Authors: Alin Banka, Inis Buzi, Islem Rekik
Date: 2020-09-24 00:00:00
Tasks: Classification, General Classification, Graph Embedding

Graph embedding is a powerful method to represent graph neurological data (e.g., brain connectomes) in a low dimensional space for brain connectivity mapping, prediction and classification. However, existing embedding algorithms have two major limitations. First, they primarily focus on preserving one-to-one topological relationships between nodes (i.e., regions of interest (ROIs) in a connectome), but they have mostly ignored many-to-many relationships (i.e., set to set), which can be captured using a hyperconnectome structure. Second, existing graph embedding techniques cannot be easily adapted to multi-view graph data with heterogeneous distributions. In this paper, while cross-pollinating adversarial deep learning with hypergraph theory, we aim to jointly learn deep latent embeddings of subject0specific multi-view brain graphs to eventually disentangle different brain states. First, we propose a new simple strategy to build a hyperconnectome for each brain view based on nearest neighbour algorithm to preserve the connectivities across pairs of ROIs. Second, we design a hyperconnectome autoencoder (HCAE) framework which operates directly on the multi-view hyperconnectomes based on hypergraph convolutional layers to better capture the many-to-many relationships between brain regions (i.e., nodes). For each subject, we further regularize the hypergraph autoencoding by adversarial regularization to align the distribution of the learned hyperconnectome embeddings with that of the input hyperconnectomes. We formalize our hyperconnectome embedding within a geometric deep learning framework to optimize for a given subject, thereby designing an individual-based learning framework. Our experiments showed that the learned embeddings by HCAE yield to better results for brain state classification compared with other deep graph embedding methods methods.
Residual Feature Distillation Network for Lightweight Image Super-Resolution
Authors: Jie Liu, Jie Tang, Gangshan Wu
Date: 2020-09-24 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recent advances in single image super-resolution (SISR) explored the power of convolutional neural network (CNN) to achieve a better performance. Despite the great success of CNN-based methods, it is not easy to apply these methods to edge devices due to the requirement of heavy computation. To solve this problem, various fast and lightweight CNN models have been proposed. The information distillation network is one of the state-of-the-art methods, which adopts the channel splitting operation to extract distilled features. However, it is not clear enough how this operation helps in the design of efficient SISR models. In this paper, we propose the feature distillation connection (FDC) that is functionally equivalent to the channel splitting operation while being more lightweight and flexible. Thanks to FDC, we can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distillation network (RFDN). RFDN uses multiple feature distillation connections to learn more discriminative feature representations. We also propose a shallow residual block (SRB) as the main building block of RFDN so that the network can benefit most from residual learning while still being lightweight enough. Extensive experimental results show that the proposed RFDN achieve a better trade-off against the state-of-the-art methods in terms of performance and model complexity. Moreover, we propose an enhanced RFDN (E-RFDN) and won the first place in the AIM 2020 efficient super-resolution challenge. Code will be available at https://github.com/njulj/RFDN.
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training
Authors: Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, Lidong Bing
Date: 2020-09-24 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Domain Adaptation, Text Classification, Unsupervised Domain Adaptation

Adapting pre-trained language models (PrLMs) (e.g., BERT) to new domains has gained much attention recently. Instead of fine-tuning PrLMs as done in most previous work, we investigate how to adapt the features of PrLMs to new domains without fine-tuning. We explore unsupervised domain adaptation (UDA) in this paper. With the features from PrLMs, we adapt the models trained with labeled data from the source domain to the unlabeled target domain. Self-training is widely used for UDA which predicts pseudo labels on the target domain data for training. However, the predicted pseudo labels inevitably include noise, which will negatively affect training a robust model. To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered. We further extend CFd to a cross-language setting, in which language discrepancy is studied. Experiments on two monolingual and multilingual Amazon review datasets show that CFd can consistently improve the performance of self-training in cross-domain and cross-language settings.
Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Profiling
Authors: Mustafa Saglam, Islem Rekik
Date: 2020-09-24 00:00:00

The individual brain can be viewed as a highly-complex multigraph (i.e. a set of graphs also called connectomes), where each graph represents a unique connectional view of pairwise brain region (node) relationships such as function or morphology. Due to its multifold complexity, understanding how brain disorders alter not only a single view of the brain graph, but its multigraph representation at the individual and population scales, remains one of the most challenging obstacles to profiling brain connectivity for ultimately disentangling a wide spectrum of brain states (e.g., healthy vs. disordered). In this work, while cross-pollinating the fields of spectral graph theory and diffusion models, we unprecedentedly propose an eigen-based cross-diffusion strategy for multigraph brain integration, comparison, and profiling. Specifically, we first devise a brain multigraph fusion model guided by eigenvector centrality to rely on most central nodes in the cross-diffusion process. Next, since the graph spectrum encodes its shape (or geometry) as if one can hear the shape of the graph, for the first time, we profile the fused multigraphs at several diffusion timescales by extracting the compact heat-trace signatures of their corresponding Laplacian matrices. Here, we reveal for the first time autistic and healthy profiles of morphological brain multigraphs, derived from T1-w magnetic resonance imaging (MRI), and demonstrate their discriminability in boosting the classification of unseen samples in comparison with state-of-the-art methods. This study presents the first step towards hearing the shape of the brain multigraph that can be leveraged for profiling and disentangling comorbid neurological disorders, thereby advancing precision medicine.
MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection
Authors: Xin Lu, Quanquan Li, Buyu Li, Junjie Yan
Date: 2020-09-24 00:00:00
Proceeding: ECCV 2020 8
Tasks: object-detection, Object Detection

Modern object detection methods can be divided into one-stage approaches and two-stage ones. One-stage detectors are more efficient owing to straightforward architectures, but the two-stage detectors still take the lead in accuracy. Although recent work try to improve the one-stage detectors by imitating the structural design of the two-stage ones, the accuracy gap is still significant. In this paper, we propose MimicDet, a novel and efficient framework to train a one-stage detector by directly mimic the two-stage features, aiming to bridge the accuracy gap between one-stage and two-stage detectors. Unlike conventional mimic methods, MimicDet has a shared backbone for one-stage and two-stage detectors, then it branches into two heads which are well designed to have compatible features for mimicking. Thus MimicDet can be end-to-end trained without the pre-train of the teacher network. And the cost does not increase much, which makes it practical to adopt large networks as backbones. We also make several specialized designs such as dual-path mimicking and staggered feature pyramid to facilitate the mimicking process. Experiments on the challenging COCO detection benchmark demonstrate the effectiveness of MimicDet. It achieves 46.1 mAP with ResNeXt-101 backbone on the COCO test-dev set, which significantly surpasses current state-of-the-art methods.
Adversarial Brain Multiplex Prediction From a Single Network for High-Order Connectional Gender-Specific Brain Mapping
Authors: Ahmed Nebli, Islem Rekik
Date: 2020-09-24 00:00:00
Tasks: Gender Classification

Brain connectivity networks, derived from magnetic resonance imaging (MRI), non-invasively quantify the relationship in function, structure, and morphology between two brain regions of interest (ROIs) and give insights into gender-related connectional differences. However, to the best of our knowledge, studies on gender differences in brain connectivity were limited to investigating pairwise (i.e., low-order) relationship ROIs, overlooking the complex high-order interconnectedness of the brain as a network. To address this limitation, brain multiplexes have been introduced to model the relationship between at least two different brain networks. However, this inhibits their application to datasets with single brain networks such as functional networks. To fill this gap, we propose the first work on predicting brain multiplexes from a source network to investigate gender differences. Recently, generative adversarial networks (GANs) submerged the field of medical data synthesis. However, although conventional GANs work well on images, they cannot handle brain networks due to their non-Euclidean topological structure. Differently, in this paper, we tap into the nascent field of geometric-GANs (G-GAN) to design a deep multiplex prediction architecture comprising (i) a geometric source to target network translator mimicking a U-Net architecture with skip connections and (ii) a conditional discriminator which classifies predicted target intra-layers by conditioning on the multiplex source intra-layers. Such architecture simultaneously learns the latent source network representation and the deep non-linear mapping from the source to target multiplex intra-layers. Our experiments on a large dataset demonstrated that predicted multiplexes significantly boost gender classification accuracy compared with source networks and identifies both low and high-order gender-specific multiplex connections.
Grounded Compositional Outputs for Adaptive Language Modeling
Authors: Nikolaos Pappas, Phoebe Mulcaire, Noah A. Smith
Date: 2020-09-24 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Language Modeling, Language Modelling

Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains and tasks. A language model's vocabulary$-$typically selected before training and permanently fixed later$-$affects its size and is part of what makes it resistant to such adaptation. Prior work has used compositional input embeddings based on surface forms to ameliorate this issue. In this work, we go one step beyond and propose a fully compositional output embedding layer for language models, which is further grounded in information from a structured lexicon (WordNet), namely semantically related words and free-text definitions. To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary. We evaluate the model on conventional language modeling as well as challenging cross-domain settings with an open vocabulary, finding that it matches or outperforms previous state-of-the-art output embedding methods and adaptation approaches. Our analysis attributes the improvements to sample efficiency: our model is more accurate for low-frequency words.
Artificial Intelligence for UAV-enabled Wireless Networks: A Survey
Authors: Mohamed-Amine Lahmeri, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2020-09-24 00:00:00
Tasks: Survey

Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAV networks.
EPNE: Evolutionary Pattern Preserving Network Embedding
Authors: Junshan Wang, Yilun Jin, Guojie Song, Xiaojun Ma
Date: 2020-09-24 00:00:00
Tasks: Network Embedding

Information networks are ubiquitous and are ideal for modeling relational data. Networks being sparse and irregular, network embedding algorithms have caught the attention of many researchers, who came up with numerous embeddings algorithms in static networks. Yet in real life, networks constantly evolve over time. Hence, evolutionary patterns, namely how nodes develop itself over time, would serve as a powerful complement to static structures in embedding networks, on which relatively few works focus. In this paper, we propose EPNE, a temporal network embedding model preserving evolutionary patterns of the local structure of nodes. In particular, we analyze evolutionary patterns with and without periodicity and design strategies correspondingly to model such patterns in time-frequency domains based on causal convolutions. In addition, we propose a temporal objective function which is optimized simultaneously with proximity ones such that both temporal and structural information are preserved. With the adequate modeling of temporal information, our model is able to outperform other competitive methods in various prediction tasks.
Improving Query Efficiency of Black-box Adversarial Attack
Authors: Yang Bai, Yuyuan Zeng, Yong Jiang, Yisen Wang, Shu-Tao Xia, Weiwei Guo
Date: 2020-09-24 00:00:00
Proceeding: ECCV 2020 8
Tasks: Adversarial Attack

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting). As plenty of machine learning models have been deployed via online services that only provide query outputs from inaccessible models (e.g. Google Cloud Vision API2), black-box adversarial attacks (inaccessible target model) are of critical security concerns in practice rather than white-box ones. However, existing query-based black-box adversarial attacks often require excessive model queries to maintain a high attack success rate. Therefore, in order to improve query efficiency, we explore the distribution of adversarial examples around benign inputs with the help of image structure information characterized by a Neural Process, and propose a Neural Process based black-box adversarial attack (NP-Attack) in this paper. Extensive experiments show that NP-Attack could greatly decrease the query counts under the black-box setting.
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems
Authors: Wei Zhao, Mingyue Shang, Yang Liu, Liang Wang, Jingming Liu
Date: 2020-09-24 00:00:00
Tasks: Diversity, Math, Math Word Problem Solving, Natural Language Understanding

Automatic math word problem solving has attracted growing attention in recent years. The evaluation datasets used by previous works have serious limitations in terms of scale and diversity. In this paper, we release a new large-scale and template-rich math word problem dataset named Ape210K. It consists of 210K Chinese elementary school-level math problems, which is 9 times the size of the largest public dataset Math23K. Each problem contains both the gold answer and the equations needed to derive the answer. Ape210K is also of greater diversity with 56K templates, which is 25 times more than Math23K. Our analysis shows that solving Ape210K requires not only natural language understanding but also commonsense knowledge. We expect Ape210K to be a benchmark for math word problem solving systems. Experiments indicate that state-of-the-art models on the Math23K dataset perform poorly on Ape210K. We propose a copy-augmented and feature-enriched sequence to sequence (seq2seq) model, which outperforms existing models by 3.2% on the Math23K dataset and serves as a strong baseline of the Ape210K dataset. The gap is still significant between human and our baseline model, calling for further research efforts. We make Ape210K dataset publicly available at https://github.com/yuantiku/ape210k
ThreatZoom: CVE2CWE using Hierarchical Neural Network
Authors: Ehsan Aghaei, Waseem Shadid, Ehab Al-Shaer
Date: 2020-09-24 00:00:00

The Common Vulnerabilities and Exposures (CVE) represent standard means for sharing publicly known information security vulnerabilities. One or more CVEs are grouped into the Common Weakness Enumeration (CWE) classes for the purpose of understanding the software or configuration flaws and potential impacts enabled by these vulnerabilities and identifying means to detect or prevent exploitation. As the CVE-to-CWE classification is mostly performed manually by domain experts, thousands of critical and new CVEs remain unclassified, yet they are unpatchable. This significantly limits the utility of CVEs and slows down proactive threat mitigation. This paper presents the first automatic tool to classify CVEs to CWEs. ThreatZoom uses a novel learning algorithm that employs an adaptive hierarchical neural network which adjusts its weights based on text analytic scores and classification errors. It automatically estimates the CWE classes corresponding to a CVE instance using both statistical and semantic features extracted from the description of a CVE. This tool is rigorously tested by various datasets provided by MITRE and the National Vulnerability Database (NVD). The accuracy of classifying CVE instances to their correct CWE classes are 92% (fine-grain) and 94% (coarse-grain) for NVD dataset, and 75% (fine-grain) and 90% (coarse-grain) for MITRE dataset, despite the small corpus.