Reviewerly/exHarmony huggingface dataset:
CNA: A Dataset for Parsing Discourse Structure on Chinese News Articles

Discourse structure analysis has shown to be useful for many artificial intelligence (AI) tasks such as text sum-marization and text categorization. However, for the Chinese news domain, the discourse structure analysis system is still immature due to the limitation of the lack of expert-annotated datasets. In this paper, we present CNA, a Chinese news corpus containing 1155 news articles annotated by human experts, which covers four domains and four news media sources. Next, we implement several text classification methods as baselines. Experimental results demonstrate that document-level method can achieve a better performance, and we further propose a document-level neural network model with multiple sentence features which achieves the state-of-the-art performance. In the end, we analyze the content type distribution of each sentence in CNA and the prediction errors of our model that occurred on the test set. The codes and dataset will be open-sourced at https://github.com/gzl98/Chinese_Discourse_Profiling.
Learning from Suboptimal Demonstration via Trajectory-Ranked Adversarial Imitation

Robots trained by Imitation Learning(IL) are used in many tasks(e.g., autonomous vehicle manipulation). Generative Adversarial Imitation Learning (GAIL) assumes that the demonstration set used for training is of high quality. However, such demonstrations are difficult and expensive to obtain. GAIL-related methods fail to learn effective strategies if non-high quality demonstrations are used because the performance of agents trained by this method is limited by the demonstrator's operations. Our idea is to enable the agent to learn strategy with better performance than the demonstrator from a suboptimal demonstration set, which contains non-high quality demonstrations that are easier to obtain. Inspired by this, we propose the Trajectory-Ranked Adversarial Imitation Learning (TRAIL) method. First, for demonstration set processing, we introduce a ranking process and define the concept of Performance Relative Advantage of suboptimal demonstrations to specify the ranking order. Second, for model training, we reconstruct the objective function of GAIL and use an experience replay buffer, enabling the agent to learn implicit features and ranking information from the ranked suboptimal demonstration set and possess the ability to outperform the demonstrator. Experiments show that in Mujoco's tasks, our method can learn from a suboptimal demonstration set and can achieve better performance than baseline methods.
HR-Net: A Landmark Based High Realistic Face Reenactment Network

In the past, GAN-based face reenactment methods concentrated mostly on transferring the facial expressions and positions of the source. However, the generated results were susceptible to blurring in some minute details of the face, such as teeth and hair, and their backgrounds were also not guaranteed to be consistent with the manipulated images in terms of light and shadow. Because of these issues, the generated results could be distinguishable as fakes. In this paper, we proposed a landmark based method named HR-Net, which can render source facial expressions and postures on any identity and simultaneously generate realistic face details. Firstly, a lightweight landmark identity conversion module (LIC) was designed to address the identity leakage problem, and it represented facial expressions and poses with only 68 2D landmarks. On this basis, a boundary-guided face reenactment module (BFR) was presented to only learn the background of the reference images; thus, the results generated by BFR can be consistent with the reference images’ light and shadow. Moreover, a novel local perceptual loss function was implemented to support the BFR module in generating more realistic details. Extensive experiments demonstrated that our method achieved the state of the art.
YOLOX++ for Transmission Line Abnormal Target Detection

The detection of abnormal targets in transmission lines plays a significant role in maintaining the stability and safety of transmission systems.To achieve improved detection performance for abnormal targets, we propose a new target detector based on YOLOX, called YOLOX++.First, a multiscale cross-stage partial network (MS-CSPNet) is designed, which fuses multiscale feature information and expands the receptive field of the target through channel combination.Furthermore, depthwise and dilated convolutions are introduced in an object decoupling head to better capture the long-range dependencies of objects in feature maps.Finally, the alpha loss function (-IoU) is introduced to optimize the localization of small objects.Experiments show that in a comparison with the YOLOX model, the YOLOX++ approach in this paper achieves 86.8% and 96.6% detection accuracies for high-voltage tower bird nest and power line insulator targets, respectively.On the PASCAL VOC dataset, the AP50 and APS are improved by 9.3% and 5.0% over those of YOLOX, respectively, showing that the YOLOX++ network possesses better robustness for small target detection.
Contrastive Graph Convolutional Networks with Degree-specific Augmentation

In recent years, Graph Convolutional Networks (GCNs) have achieved tremendous success on semi-supervised tasks by effectively gathering local features for nodes. However, we find that the classification performance of nodes with different degrees on GCNs varies greatly: a considerable number of low-degree nodes perform poorly due to a lack of information, while high-degree nodes receive more noise due to their large receptive fields, resulting in suboptimal performance. Graph Convolutional Network (GCN) is widely used as an encoder in graph contrastive learning models, yet a large number of low-degree nodes are generated during crucial graph augmentation, resulting in suboptimal performance. To this end, we propose a novel graph contrastive representation learning method with degree-specific augmentation, which incorporates various priors for topological and semantic aspects of the graph. Specifically, on the topological level, we design a degree-specific augmentation scheme to expand the receptive field of low-degree nodes and reduce the noise received by high-degree nodes. On the node attribute level, we comprehensively consider node degree and feature variance to force the model to recognize the under-lying semantic information. We jointly utilize supervised and unsupervised contrastive losses to improve model performance. Compared with other advanced methods, the experimental results on various real datasets strongly verify the effectiveness of our method.
Sequential Recommendation with Dual Learning

Sequential recommendation, which aims to leverage users' historical behaviors to predict their next interaction, has become a research hotspot in the field of recommendation. Time is one of the important contextual information for interaction. However, most previous works only use time information as a model feature or time prediction as an auxiliary task and ignore the duality between sequential recommendation task and time prediction task. Compared with the method of sharing parameters in multi-task learning, this paper proposed a dual learning framework to jointly model two tasks and incorporate the probabilistic dual properties between them in the training stage. In addition, we design an appropriate base model for each task. Finally, experiments on two public datasets demonstrated the effectiveness of the proposed dual learning framework in sequential recommendation scenarios.
Attention Constraint Mechanism through Auxiliary Attention

In current translation tasks, we discover that the weight distribution of attention has a fuzziness problem, specif-ically, its imperfection in detecting definitive information. In-tuitively, the most straightforward idea to alleviate fuzziness is to increase the number of attention heads at the expense of model complexities. However, our experiments prove this approach to be inefficient, and the effect is barely measurable. Instead, to tackle this problem, we explore an attention con-straint mechanism by introducing auxiliary attention. Instead of simply accumulating or averaging the base attention with auxiliary attention, we build a mathematical constraint function between them. The constraint function calibrates the original attention and further improves the model performance. Extensive experiments on neural machine translation tasks and abstract summarization tasks demonstrate that our attention constraint method can substantially outperform the strong baselines. The exact mathematical formulas and experimental analysis reveal the influence of constraint function on attention: attention tends to assign more weights to the decisive inputs, becoming sparse and sharp. Our code is available on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/auxiliaryattention/AttentionConstraintMechanism.
Feature Transform Correlation Network for Object Detection

With the development of computer vision and the growth of image data volume, object detection is more and more widely used in people's life. Recently, object detection network base on transformer has good effect. However, it doesn't pay attention to objects' distinguishable features and the correlation of different distinguishable features, which makes its accuracy not satisfactory. In this paper, we propose a new transform correlation network (FTCN) based on rotational attention for object detection, which can be inserted into various modules of the object detection network theoretically. It can help the network to pay more attention to distinguishable features and their correlation, and increase the accuracy of the network. Compared to transformer, FTCN reduces the large computational cost and parameters that encoders and decoders require. Experimental results show that FTCN can increase the accuracy of the network to a certain extent in Microsoft COCO and Pascal VOC. The comparative results of GFLOPs and Param show that our approach has less computational cost and parameters compared with transformer methods.
Development of Robust Vehicle Classification and Tracking for Traffic Analysis

The Japanese government plans to replace the manual survey method of counting vehicles with CCTV images in order to obtain traffic volume by vehicle types automatically. The current method of manually checking vehicles and counting them as small or large vehicles is time-consuming, and there is a shortage of surveyors. Automating this process will allow for more accurate and constant monitoring of traffic volume. Existing research can be found in developing vehicle detection and tracking models using machine learning. However, these models have not yet achieved the accuracy required for practical use. This paper aims to develop a vehicle classification and tracking model with high accuracy for 7 classes of vehicle types. YOLOv5 is used for vehicle detection and classification, along with DeepSORT for vehicle tracking. The results showed most of the classes reached 95% accuracy both for classification and tracking.
Lightweight Lattice-Based Signature for VANET

Vehicle ad hoc network (VANET) realizes information sharing through the interconnection between vehicles, vehicles and roads and other entities, so as to improve traffic efficiency. However, attackers can steal and tamper with messages sent by vehicles, thereby threatening the safety of the Internet of Vehicles and possibly causing serious traffic accidents. Due to the high-speed mobility of vehicles, the communication between nodes must be authenticated within the effective time. Therefore, it is particularly important to design a safe and efficient message authentication scheme for the Internet of Vehicles. Signature scheme is an effective authentication method, which is expected to greatly improve the security of VANET. The rapid development of quantum computers poses a serious threat to traditional signature schemes. There are some signature schemes that can resist quantum attacks, however, compared with traditional signature, lattice-based signature has bigger signature size, which could be challenging in resource constrained applications, like Internet-of-Things (IoT). Message recovery technology can embed some or all of the message into signature which can reduce the size of the signed message. In this paper, we present a new NTRU lattice-based signature scheme with message recovery. This scheme is more efficient than other lattice-based signature schemes in terms of signature size and energy cost. We prove its security in the random oracle model under short integer solution assumption that is as hard as approximating several worst-case lattice problems.
Explicit History Selection for Conversational Question Answering

Topic shift is very common in multi-turn dialogues, making it a great challenge in the filed of conversational question answering. Existing methods usually select the most adjacent turns as history information, however, it is useless or even harmful in case of topic shift. This paper proposes two explicit history selection models: SHSM and DHSM, to address this issue. The former is a simple history selection model, which only selects <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{k}$</tex> previous history turns; and the latter is a dependent history selection model, which selects the most relevant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{k}$</tex> history turns through a turn-dependent graph. The two models are then trained in a consistency framework. Experimental results on QuAC show that our model can cope with topic shift problem, and it outperforms existing state-of-the-art methods by 0.8 on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{F}_{\mathbf{1}}$</tex> score, 0.7 on HEQ-Q score, and 1.4 on HEQ-D score.
IDEA: Interactive Double Attentions from Label Embedding for Text Classification

Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in the label text. As a matter of fact, humans classify documents primarily based on the semantic meaning of the subcategories. We propose a novel model structure via siamese BERT and interactive double attentions named IDEA (Interactive DoublE Attentions) to capture the information exchange of text and label names. Interactive double attentions enable the model to exploit the inter-class and intra-class information from coarse to fine, which involves distinguishing among all labels and matching the semantical subclasses of ground truth labels. Our proposed method outperforms the state-of-the-art methods using label texts significantly with more stable results.
Implicit Feature Alignment For Knowledge Distillation

Knowledge distillation is a technique of transferring knowledge from a large teacher network to a light student one. Existing studies purely use immediate layers' features for distillation and may fail to gain insufficient semantic knowledge from the teacher. Inspired by recent advances in contrastive learning, we propose to introduce extra light embedding layers of the teacher to enforce its generalization ability and further align the mixup-type features for knowledge distillation in an implicit fashion (IFKD). IFKD allows the student to learn richer structural knowledge, thanks to the learned embedding layers of the teacher. Crucially, benefitting from a plethora of mixed samples, we can further adequately mine much semantic knowledge of the teacher. For efficiency, we propose a simple reversed mixup scheme to organize images and implicitly ensure complete positive information comparisons. Extensive experiments on image classification on two popular datasets including CIFAR-100 and ImageNet verify the effectiveness of our approach as compared to the previous methods.
EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.
PBSK (Parity Bit Interleaved Shift Key) based Distribution for Controlling side channel attacks

Recent technological advancements have made it possible to communicate effectively. Communication's primary goal is to share information among users. Security in data communication is currently plagued by numerous issues. Quantum cryptography, a state-of-the-art cryptography technique, is used to solve problems. In this, the security of key transmission systems is safeguarded using quantum techniques. Approaches used for Practical Quantum Key Distribution are vulnerable to side channel attacks. Analyzing side channel assaults is a difficult task. We examine in detail the secure communication of the key in this system. The parity bit interleaved shift key technique will provide a higher level of protection against side channel attacks.
Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting

Deep neural networks (DNNs) have shown to provide superb performance in many real life applications, but their large computation cost and storage requirement have prevented them from being deployed to many edge and internet-of-things (IoT) devices. Sparse deep neural networks, whose majority weight parameters are zeros, can substantially reduce the computation complexity and memory consumption of the models. In real-use scenarios, devices may suffer from large fluctuations of the available computation and memory resources under different environment, and the quality of service (QoS) is difficult to maintain due to the long tail inferences with large latency. Facing the real-life challenges, we propose to train a sparse model that supports multiple sparse levels. That is, a hierarchical structure of weights are satisfied such that the locations and the values of the non-zero parameters of the more-sparse sub-model are a subset of the less-sparse sub-model. In this way, one can dynamically select the appropriate sparsity level during inference, while the storage cost is capped by the least sparse sub-model. We have verified our methodologies on a variety of DNN models and tasks, including the ResNet-50, PointNet++, GNMT, and graph attention networks. We obtain sparse sub-models with an average of 13.38% weights and 14.97% FLOPs, while the accuracies are as good as their dense counterparts. More-sparse sub-models with 5.38% weights and 4.47% of FLOPs, which are subsets of the less-sparse ones, can be obtained with only 3.25% relative accuracy loss. In addition, our proposed hierarchical model structure supports the mechanism to inference the first part of the model with less sparsity, and dynamically reroute to the more-sparse level if the real-time latency constraint is estimated to be violated. Preliminary analysis shows that we can improve the QoS by one or two nines depending on the task and the computation-memory resources of the inference engine.
CDRL: Contrastive Disentangled Representation Learning Scheme for Facial Action Unit Detection

Facial action unit (AU) detection is a hot topic in computer vision, but it remains challenging due to individual characteristics. Facial action features are a vital informative factor to explain facial anatomical variations but are often entangled with other facial attribution information leading to representation inconsistency within one category. We propose a novel Contrastive Disentangled Representation Autoencoder (CDAE) to learn discriminative identity-invariant representation for AU detection by factorizing face images into temporally varying action parts and stationary facial attributions components. Facial image space is mapped onto the facial action subspace and action-independent identity subspace to disentangle facial action information from identity information. In addition, we design a contrastive learning scheme to obtain a semantic-aware AU manifold by mapping the facial action features onto the continuous space of the latent variables, thus minimizing the misalignment between subjects and reducing the dimension of the facial action features. Experiment results show that CDAE outperforms or is comparable to previous AU detection methods on the challenging BP4D and DISFA benchmarks, demonstrating that the learned facial action representation is discriminative for AU detection.
Pre-Avatar: An Automatic Presentation Generation Framework Leveraging Talking Avatar

Since the beginning of the COVID-19 pandemic, remote conferencing and school-teaching have become important tools. The previous applications aim to save the commuting cost with real-time interactions. However, our application is going to lower the production and reproduction costs when preparing the communication materials. This paper proposes a system called Pre-Avatar, generating a presentation video with a talking face of a target speaker with 1 front-face photo and a 3-minute voice recording. Technically, the system consists of three main modules, user experience interface (UEI), talking face module and few-shot text-to-speech (TTS) module. The system firstly clones the target speaker's voice, and then generates the speech, and finally generate an avatar with appropriate lip and head movements. Under any scenario, users only need to replace slides with different notes to generate another new video. The demo has been released here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://pre-avatar.github.io/ and will be published as free software for use.