Reviewerly/exHarmony huggingface dataset:
Physical Layer Anonymous Communications: An Anonymity Entropy Oriented Precoding Design (Invited Paper)

Different from traditional security-oriented designs, the aim of anonymizing techniques is to mask users' identities during communication, thereby providing users with unidentifiability and unlinkability. The existing anonymizing techniques are only designated at upper layers of networks, ignoring the risk of anonymity leakage at physical layer (PHY). In this paper, we address the PHY anonymity design with focus on a typical uplink scenario where the receiver is equipped with more antennas than the sender. With the increased degrees-of-freedom at the receiver side, we first propose a maximum likelihood estimation (MLE) signal trace-back detector, which only analyzes the signaling pattern of the received signal to disclose the sender's identity. Accordingly, an anonymity entropy anonymous (AEA) precoder is proposed, which manipulates the transmitted signalling pattern to counteract the receiver's trace-back detector and meanwhile to guarantee high receive signal-to-interference-plus-noise ratio for communication. More importantly, more data streams can be multiplexed than the number of transmit antennas, which is particularly suitable for the strong receiver configuration. Simulation demonstrates that the proposed AEA precoder can simultaneously provide high anonymity and communication performance.
CRPN: Distinguish Novel Categories Via Class-Relevant Region Proposal Network for Few-Shot Object Detection

Few-shot object detection (FSOD) has attracted more attention in computer vision, where only very few training examples are presented during model learning process. A commonly-overlooked issue in FSOD is that novel classes are usually classified as background clutters in the pre-training process. Another difficulty of FSOD is that the detection performance degrades especially under higher IoU thresholds since previous deep metric learning (DML) requires frozen region proposals without class-relevant box regression. In this work, we propose a Class-relevant Region Proposal Network (CRPN). The CRPN can derive network parameters for novel classes from pre-trained convolution kernels according to their feature similarity, which is used to eliminate the above mentioned adverse effects and improve the performance of few-shot object detection. The proposed CPRN is able to kill two birds with one stone and has two main contributions: (1) transfer a region proposal network pre-trained on base classes to novel classes; (2) perform class-dependent bounding-box regression which previous DML classifier lacks. For experimental testing, we achieve 12.7% AP75 in MS COCO dataset and 28.6% AP75 in ImageNet2015 dataset under the few-shot setting introduced by previous works, which exceeds the state-of-the-art by a certain margin.
Improving Spoken Language Understanding by Enhancing Text Representation

Language Model (LM) which is commonly trained on a large corpora has been proven the robustness and effectiveness for tasks of Natural Language Understanding (NLU) in many applications such as virtual assistant or recommendation system. These applications normally receive outputs of automatic speech recognition (ASR) module as spoken form inputs which generally lack both lexical and syntactic information. Pre-trained language models, for example BERT [1] or XLM-RoBERTa [2], which are often pre-trained on written form corpora perform decreased performance on NLU tasks with spoken form inputs. In this paper, we propose a novel model to train a language model namely CapuBERT that is able to deal with spoken form input from ASR module. The experimental results show that the proposed model achieves state-of-the-art results on several NLU tasks included Part-of-speech tagging, Named-entity recognition and Chunking in English, German, and Vietnamese languages.
VQA-BC: Robust Visual Question Answering Via Bidirectional Chaining

Current VQA models are suffering from the problem of overdependence on language bias, which severely reduces their robustness in real-world scenarios. In this paper, we analyze VQA models from the view of forward/backward chaining in the inference engine, and propose to enhance their robustness via a novel Bidirectional Chaining (VQA-BC) framework. Specifically, we introduce a backward chaining with hardnegative contrastive learning to reason from the consequence (answers) to generate crucial known facts (question-related visual region features). Furthermore, to alleviate the overconfident problem in answer prediction (forward chaining), we present a novel introspective regularization to connect forward and backward chaining with label smoothing. Extensive experiments verify that VQA-BC not only effectively overcomes language bias on out-of-distribution dataset, but also alleviates the over-correct problem caused by ensemble-based method on in-distribution dataset. Compared with competitive debiasing strategies, our method achieves state-of-the-art performance to reduce language bias on VQA-CP v2 dataset.
LatticeBART: Lattice-to-Lattice Pre-Training for Speech Recognition

To improve automatic speech recognition, increasing work has attempted to further fix the output of ASR systems with advanced sequence models. However, the output of ASR systems differs significantly from the input form of standard sequence models. To encompass richer information, the output of ASR systems is often a compact lattice structure containing multiple sentences. This mismatch in input form significantly limits sequence models’ ability. On the one hand, the widely used pre-trained models cannot directly input lattice structures and are therefore difficult to use for this task. On the other hand, the sparsity of the supervised training data forces the model to have the ability to learn from limited data. To address these problems, we propose LatticeBART, a model that decodes the sequence from the lattice in an end-to-end fashion and can use the pre-trained language models’ prior. In addition, this paper proposes the lattice-to-lattice pre-training method, which can be used when annotated data is missing, using easily generated lattice with the ASR system for training. The experimental results show that our model can effectively improve the output quality of the ASR system.
Dual Path Graph Convolutional Networks

Graph Convolutional Networks (GCNs) are a powerful approach for learning graph representations and show promising results in various applications. Despite their success, they are usually limited to shallow architectures due to the vanishing gradients, over-smoothing, and over-squashing problems. As Convolutional Neural Networks benefit tremendously from stacking very deep layers, recently techniques such as various types of residual connections and dense connections are proposed to tackle these problems and make GCNs go deeper. In this work, we further study the problem of designing deep architectures for GCNs. Firstly, we introduce the Higher Order Graph Recurrent Networks (HOGRNs), which can unify most existing architectures of GCNs. Then we show that ResGCN and DenseGCN are special cases of HOGRNs. To enjoy the benefits from both residual connections and dense connections and compensate for the drawbacks from each other, we propose Dual Path Graph Convolutional Networks (DPGCNs), which exploit a new topology of connection paths internally. In DPGCNs, we maintain both a residual path and a densely connected path while learning the graph representations. Extensive experiments on OGB datasets demonstrate superior performances of the proposed DPGCNs over competitive baseline methods on the large-scale graph learning tasks of node property prediction and graph property prediction.
Exploring Deeper Graph Convolutions for Semi-Supervised Node Classification

Graph convolutional networks (GCNs) have achieved impressive performance in learning from graph-structured data. Although GCN and its variants have shown promising results, they continue to remain shallow as their performance drops with an increasing number of layers - a problem popularly known as oversmoothing. This work introduces a simple yet effective idea of feature gating over graph convolution layers to facilitate deeper graph neural networks and address oversmoothing. The proposed feature gating is easy to implement without changing the underlying network architecture and is broadly applicable to GCN and almost any of its variants. Further, we demonstrate the use of feature gating in assigning importance to node features and the nodes for the node classification task. Quantitative analysis on real-world datasets shows that feature gating paves the way for constructing deeper GCNs.
A Question-Oriented Propagation Network for News Reading Comprehension

Machine reading comprehension of news articles remains to be a challenging task since the lengths of its context documents are long. Such reading comprehension task usually requires document-level language understanding while state-of-the-art, pretrained question answering models can only encode sequences with a predefined length limit. In this paper, we propose a novel Question-Oriented Propagation Network (QOPN) model for such task. Specifically, our proposed QOPN first uses a context encoding module to find local question-related clues. Then, it employs a multi-step reasoning module to aggregate question-focused information for iterative reasoning. The novel design put emphasis on capturing question-related information and allow long-range information integration, which is especially beneficial for long-context reading comprehension task. Experiments on two challenging machine comprehension datasets show that the proposed QOPN significantly outperforms previous state-of-the-art models.
Cross-Target Stance Detection Via Refined Meta-Learning

Cross-target stance detection (CTSD) aims to identify the stance of the text towards a target, where stance annotations are available for (though related but) different targets. Recently, models based on external semantic and emotion knowledge have been proposed for CTSD, achieving promising performance. However, such solutions rely on much external resources and harness only one source target, which is a waste of other available targets. To address the problem above, we propose a many-to-one CTSD model based on meta-learning. To make the most of meta-learning, we further refine it with a balanced and easy-to-hard learning pattern. Specifically, for multiple-target training, we feed the model according to the similarity among targets, and utilize two kinds of re-balanced strategies to deal with the imbalance in data. We conduct experiments on SemEval 2016 task 6, and results demonstrate that our method is effective and establishes a new state-of-the-art macro-f1 score for CTSD.
FSM: Feature Sampling Module for Object Detection

Challenges caused by the acquisition condition of the images, the state of the objects, or the noise in the transmission of the images commonly exist in object detection. In those situations, the features of the objects extracted by CNNs contain certain uncertainty, which increases the difficulty of subsequent classification and regression. Towards enhancing the quality of the features, we propose a Feature Sampling Module (FSM), which learns multiple two-dimensional Gaussian distributions by the sampling network (SN) and applies those Gaussian masks to extract valid information of the features. With this sampling scheme, our method avoids learning the decision boundary from the low-quality features, making the overall model classification performance more robust. To ensure that the SN is capable of sampling the highest quality region, we design a novel sampling loss (SL) to measure the quality of the sampled features. Extensive experimental results validate the effectiveness of our proposed method.
Gradient Staleness in Asynchronous Optimization Under Random Communication Delays

Distributed optimization is widely used to solve large-scale optimization problems by parallelizing gradient-based algorithms across multiple computing nodes. In asynchronous optimization, the optimization parameter is updated using stale gradients, which are gradients calculated with respect to out-of-date parameters. Although large degrees of staleness can slow convergence, little is known about the impact of staleness and its relation to other system parameters. In this work, we study and analyze centralized asynchronous optimization. We show that the process of gradient arrival to the master node is similar in nature to a renewal process. We derive bounds on expected staleness and show its connection to other system parameters such as the number of workers, expected compute time and communication delays. Our derivations can be used in existing convergence analyses to express convergence rates in terms of other known system parameters. Such an expression gives further details on what factors impact convergence.
Pseudo-Interacting Guided Network for Few-Shot Segmentation

Few-shot segmentation has got a lot of concerns recently. Existing methods mainly locate and recognize the target object based on a cross-guided way that applies masked target object features of support(query) images to make a feature matching with query(support) images. However, there are some differences between support images and query images because of large appearance and scale variation, which will lead to inaccurate and incomplete segmentation. This problem inspired us to explore the local coherence of the image to guide the segmentation. We try to get some target pixels in the query image and apply these pixels to search for more target pixels in the query image. In this work, we propose a novel network that combines a universal cross-guided branch with a new pseudo-interacting guided branch. Specifically, we first employ the universal cross-guided branch to produce a pseudo-labeling that represents the probability of each pixel belonging to the target object. Then we design a pseudo-interacting guided branch, which applies some pixels with high probabilities based on generated pseudo-labeling to segment the target object in the query image and revises the results of the cross-guided branch simultaneously. Extensive experiments show that our approach outperforms state-of-the-art methods on both PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> datasets.
Partial Arithmetic Consensus based Distributed Intensity Particle Flow SMC-PHD Filter for Multi-Target Tracking

Intensity Particle Flow (IPF) SMC-PHD has been proposed recently for multi-target tracking. In this paper, we extend IPF-SMC-PHD filter to distributed setting, and develop a novel consensus method for fusing the estimates from individual sensors, based on Arithmetic Average (AA) fusion. Different from conventional AA method which may be degraded when unreliable estimates are presented, we develop a novel arithmetic consensus method to fuse estimates from each individual IPF-SMC-PHD filter with partial consensus. The proposed method contains a scheme for evaluating the reliability of the sensor nodes and preventing unreliable sensor information to be used in fusion and communication in sensor network, which help improve fusion accuracy and reduce sensor communication costs. Numerical simulations are performed to demonstrate the advantages of the proposed algorithm over the uncooperative IPF-SMC-PHD and distributed particle-PHD with AA fusion.
Cross-Layer Aggregation with Transformers for Multi-Label Image Classification

Multi-label image classification task aims to predict multiple object labels in a given image and faces the challenge of variable-sized objects. Limited by the size of CNN convolution kernels, existing CNN-based methods have difficulty capturing global dependencies and effectively fusing multiple layers features, which is critical for this task. Recently, transformers have utilized multi-head attention to extract feature with long range dependencies. Inspired by this, this paper proposes a Cross-layer Aggregation with Transformers (CAT) framework, which leverages transformers to capture the long range dependencies of CNN-based features with Long Range Dependencies module and aggregate the features layer by layer with Cross-Layer Fusion module. To make the framework efficient, a multi-head pre-max attention is designed to reduce the computation cost when fusing the high-resolution features of lower-layers. On two widely-used benchmarks (i.e., VOC2007 and MS-COCO), CAT provides a stable improvement over the baseline and produces a competitive performance.
Investigating the Potential of Auxiliary-Classifier Gans for Image Classification in Low Data Regimes

Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural network (CNN) performance on image classification tasks. But they introduce more hyperparameters to tune as well as the need for additional time and computational power to train, supplementary to the CNN. In this work, we examine the potential for Auxiliary-Classifier GANs (AC-GANs) as a ’one-stop-shop’ architecture for image classification, particularly in low data regimes. Additionally, we explore modifications to the typical AC-GAN framework, changing the generator’s latent space sampling scheme and employing a Wasserstein loss with gradient penalty to stabilize the simultaneous training of image synthesis and classification. Through experiments on images of varying resolutions and complexity, we demonstrate that AC-GANs show promise in image classification, achieving competitive performance with standard CNNs. These methods can be employed as an ’all-in-one’ framework with particular utility in the absence of large amounts of training data.
Privacy-Preserving Action Recognition

As the amount of data shared on the network increases, these data pose a threat to our privacy. This paper focuses on the privacy-preserving issues of action recognition for humans. Generally, the face is considered the most identifiable visual cue for a human. However, removing face information is not enough for many privacy-preserving scenes. Thus, we replace the human body with his poses and explore the pose presentation in the action recognition task. In privacy scenes, many human actions could not access in advance. To recognize these unseen actions, we study the zero-shot action recognition in the strict condition of privacy preservation. Specifically, we propose to use unified actor score (UAS) to enhance the action recognition accuracy. The experimental results show that UAS outperforms most of the state-of-the-art methods in standard datasets without sacrificing privacy.
Byzantine-Robust Federated Deep Deterministic Policy Gradient

Federated reinforcement learning (FRL) combines multi-agent reinforcement learning (MARL) and federated learning (FL) so that multiple agents can exchange messages with a central server for co-operatively learning their local policies. However, a number of malicious agents may deliberately modify the messages transmitted to the central server so as to hinder the learning process, which is often described by the Byzantine attacks model. To address this issue, we propose to employ robust aggregation to replace the simple average aggregation rule in FRL and enhance Byzantine robustness. To be specific, we focus on the episodic task where the environment and agents are reset in the beginning each episode. First, we extend deep deterministic policy gradient (DDPG) to FRL (termed as F-DDPG), which maintains a global critic and multiple local actors, and is thus computation- and communication-efficient. Then, we introduce geometric median and median to aggregate the gradients received from the agents and propose RF-DDPG, a class of Byzantine-robust FRL methods. Finally, we conduct numerical experiments to validate the robustness of RF-DDPG to Byzantine attacks.
A Novel Lightweight Network for Fast Monocular Depth Estimation

Depth estimation is of growing interest in many sectors, from robotics to wearable augmented reality gears. Monocular depth estimation attracts more attention due to its cost efficiency and low complexity. Most recent research has developed very large and resource intensive networks which are not suitable for small systems with limited resources. In this paper, we propose a lightweight network which leverages the advantages of dimension-wise convolutions and depthwise separable convolutions to reduce complexity in the architecture. In particular, the proposed depth estimation architecture utilizes a novel DICE unit-based encoder, optimized for a lightweight encoder-decoder structure. Furthermore, we propose a DICE unit-based decoder structure as well as an optimized depthwise separable convolution-based decoder. Both decoders follow a similar five-layer architecture. In the experiments, we have demonstrated the effectiveness of the proposed architecture as well as the comparison between the two proposed decoders. Our novel lightweight network has a significant decrease in both size and complexity at a marginal cost to accuracy when compared to other state-of-the-art lightweight networks.
Deterministic Transform Based Weight Matrices for Neural Networks

We propose to use deterministic transforms as weight matrices for several feedforward neural networks. The use of deterministic transforms helps to reduce the computational complexity in two ways: (1) matrix-vector product complexity in forward pass, helping real time complexity, and (2) fully avoiding backpropagation in the training stage. For each layer of a feedforward network, we pro-pose two unsupervised methods to choose the most appropriate deterministic transform from a set of transforms (a bag of well-known transforms). Experimental results show that the use of deterministic transforms is as good as traditional random matrices in the sense of providing similar classification performance.
Variational Bayesian Graph Convolutional Network for Robust Collaborative Filtering

This paper presents a variational Bayesian graph convolutional network for robust collaborative filtering (VBGCF). Conventional graph convolutional network (GCN)-based recommendation models fully trust the observed interaction graph. However, the data used in real-world applications (e.g., video streaming services) are often incomplete and unreliable. To deal with this realistic situation, we newly introduce the probabilistic model based on variational Bayesian inference to GCN-based recommendation. VBGCF can use various generated graphs instead of the observed interaction graph to learn users’ preferences. Therefore, VBGCF is not affected by the incompleteness and the unreliability and can provide robust recommendation. The results of experiments conducted under the realistic situation show the effectiveness of VBGCF.
AERS: Adaptive and Efficient Hybrid Recommendation System based on Web usage Mining

Recommendation systems are used for filtering of information since they predict user items according to user behavior and their preferences. Recommendation systems are intelligent system which provides suggestion according to user adaptability. This paper proposed a Hybrid recommendation system-based approach based on web usage mining that will solve the sparsity problem that was exist in previous techniques. To make the recommendation system hybrid, Collaborative filtering and Content Filtering are integrated with weighing fusion technique. To validate the efficiency of Proposed and existing technique use RMSE and MAE as performance error matrix. The comparative analysis shows that the proposed AERS Recommendation System provides better results in terms of RMSE and MAE in comparison to Collaborative and Content Based Recommendation System.