Reviewerly/exHarmony huggingface dataset:
QVDDPG: QV Learning with Balanced Constraint in Actor-Critic Framework

Actor-critic framework has achieved tremendous success in a great many of decision-making scenarios. Nevertheless, when updating the value of new states and actions in the long-term scene, these methods suffer from misestimate problem and gradient variance problem, significantly reducing convergence speed and robustness of the policy. These problems severely limit the application scope of these methods. In this paper, we first proposed QVDDPG, a deep RL algorithm based on the iterative target value update process. The QV learning method alleviates the problem of misestimate by making use of the guidance of Q value and the fast convergence of V value, thus accelerating the convergence speed. In addition, the actor utilizes a constrained balanced gradient and establishes a hidden state for the continuous action space network for the sake of robustness of the model. We give the update relation among the value functions and the constraint conditions of gradient estimation. We measure our method on the PyBullet and achieved state-of-the-art performance. Moreover, we demonstrate that, our method has higher robustness and convergence speed across different tasks compared to other algorithms.
A Comparison of Neural Word Embedding Language Models for Classifying Social Media Users in the Healthcare Context

In the era of generalist social media, finding users who share the same diseases and the same related experiences during their course is one of the main objectives of patients. In this reference framework, in applications related to recommender systems or infoveillance, just to name a few, it is useful to synthesize language models capable of capturing the semantic relationships in short texts written by patients in various posts, with the dual goal of training well-performing classification systems. In this work, a series of semantic text representation approaches - both traditional and advanced - are compared through NLP techniques, in order to classify Italian users belonging to discussion groups on medical topics. The classification and semantic evaluation experiments of the models are satisfactory above all, especially by considering that the collected dataset is unbalanced.
A Sequence Tagging based Framework for Few-Shot Relation Extraction

Relation Extraction (RE) refers to extracting the relation triples in the input text. Existing neural work based systems for RE rely heavily on manually labeled training data, but there are still a lot of domains where sufficient labeled data does not exist. Inspired by the distance-based few-shot named entity recognition methods, we put forward the definition of the few-shot RE task based on the sequence tagging joint extraction approaches, and propose a few-shot RE framework for the task. Besides, we apply two actual sequence tagging models to our framework (called Few-shot TPLinker and Few-shot BiTT), and achieve solid results on two few-shot RE tasks constructed from a public dataset.
GCN-based End-to-End Model for Comparative Opinion Quintuple Extraction

Comparative Opinion Quintuple Extraction (COQE) is a task of recognizing comparative relationships in a sentence-level comment. It is additionally required to extract the quintuple constituents that attribute to a specific comparative relation, including a subject and object, as well as comparative aspect, opinion and preference. Previous study employ pipeline models in addressing the COQE task, which tend to suffer from error propagation. To address the issue, in this paper, we propose a BERT-based end-to-end neural model as the alternative. Specifically, we first boil COQE down to a set prediction problem, due to the finding that all the quintuple constituents fail to hold coherent or sequential relationships. In other word, we consider a group of quintuple components as a set, and intent to bag-and-drag them as a whole, instead individually and one-by-one. Furthermore, we leverage Graph Convolutional Network (GCN) to enhance the end-to-end model, which plays the role of perceiving and representing the relevant relations among the quintuple components. We experiment on three benchmark datasets, including Camera-COQE, Car-COQE and Ele-COQE. The experimental results show that our model (GCN-E2E) yields a significant improvement in most cases, compared to the BERT-based pipeline baseline. The performance reaches the F1-scores of 14.10%, 36.46% and 39.29%, with the improvements of 0.74%, 6.71% and 8.56%.
Learning Arc-Length Value Function for Fast Time-Optimal Pick and Place Sequence Planning and Execution

This paper presents a real-time algorithm for computing the optimal sequence and motion plans for a fixed-base manipulator to pick and place a set of given objects. The optimality is defined in terms of the total execution time of the sequence or its proxy, the arc-length in the joint-space. The fundamental complexity stems from the fact that the optimality metric depends on the joint motion, but the task specification is in the end-effector space. Moreover, mapping between a pair of end-effector positions to the shortest arc-length joint trajectory is not analytic; instead, it entails solving a complex trajectory optimization problem. Existing works ignore this complex mapping and use the Euclidean distance in the end-effector space to compute the sequence. In this paper, we overcome the reliance on the Euclidean distance heuristic by introducing a novel data-driven technique to estimate the optimal arc-length cost in joint space (a.k.a the value function) between two given end-effector positions. We parametrize the value function as a Neural Network and motivate a niche choice for its architecture, inspired by the works on metric learning. The learned value function is then used as an edge cost in a capacitated vehicle routing problem (CVRP) set-up to compute the optimal visitation sequence. Finally, we optimize over the input space of the learnt value function network to propose a novel Inverse Kinematics (IK) algorithm that produces substantially shorter joint arc-length trajectories than existing approaches while executing the computed optimal sequence. We show that our sequence planner, in combination with our proposed IK, offers a substantial improvement in joint arc-length over existing state-of-the-art while maintaining scalability to a large number of objects.
Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices

Federated learning (FL) has evolved as a prominent method for edge devices to cooperatively create a unified prediction model while securing their sensitive training data local to the device.Despite the existence of numerous research frameworks for simulating FL algorithms, they do not facilitate comprehensive deployment for automatic speech recognition tasks on heterogeneous edge devices.This is where Ed-Fed, a comprehensive and generic FL framework, comes in as a foundation for future practical FL system research.We also propose a novel resource-aware client selection algorithm to optimise the waiting time in the FL settings.We show that our approach can handle the straggler devices and dynamically set the training time for the selected devices in a round.Our evaluation has shown that the proposed approach significantly optimises waiting time in FL compared to conventional random client selection methods.
Langevin Algorithms for Markovian Neural Networks and Deep Stochastic Control

Stochastic Gradient Descent Langevin Dynamics (SGLD) algorithms, which add noise to the classic gradient descent, are known to improve the training of neural networks in some cases where the neural network is very deep. In this paper we study the possibilities of training acceleration for the numerical resolution of stochastic control problems through gradient descent, where the control is parametrized by a neural network. If the control is applied at many discretization times then solving the stochastic control problem reduces to minimizing the loss of a very deep neural network. We numerically show that Langevin and Layer-Langevin algorithms improve the training on various stochastic control problems like hedging and resource management, and for different choices of gradient descent methods.
Learning Rich Detailed Features and Object Relations for Object Detection

This paper proposes a feature fusion method with multi-scale detail features and an object relation inference method to better assist the detectors in completing detection tasks. First, we fully use and extract the multi-scale detail features contained in the shallow feature map of the backbone, and integrate them into the feature map of the corresponding scale responsible for detection, to realize the effective transfer and supplement of detail features. Secondly, we fully use the location relationship between object categories for inference and judgment so that the model can more fully use the potential relationship between objects to assist detection instead of using the features of the object itself. Finally, we conducted extensive experiments using different detectors and backbones as the improvement baseline to verify the effectiveness of our proposed method. The results show that our improved model significantly improved the prediction accuracy compared with the baseline model.
GAE-ISUMM: Unsupervised Graph-based Summarization for Indian Languages

Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus and efficient pre-trained language models and tools. However, English summarization models for low-resource Indian languages are often limited by rich morphological variation, syntax, and semantic differences. In this paper, we propose GAE-ISUMM, an unsupervised Indic summarization model that extracts summaries from text documents. In particular, our proposed model, GAE-ISUMM uses Graph Autoencoder (GAE) to learn text representations and a document summary jointly. We also provide a manually-annotated Telugu summarization dataset TELSUM, to experiment with our model GAE-ISUMM. Further, we benchmark with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISUMM. Our experiments of GAE-ISUMM on seven Indian languages make the following observations: (i) it is competitive or better than state-of-the-art results on all datasets, (ii) it reports benchmark results on TELSUM, and (iii) the inclusion of positional and cluster information in the proposed model improved the performance of summaries. We open-source our dataset and code <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/scsmuhio/Summarization.
Personalized Outfit Compatibility Prediction Using Outfit Graph Network

Recommendation systems improve users' online shopping experience by recommending relevant items from a large pool of items in different categories. Fashion recommendation systems apart from recommending individual fashion items also recommend fashion outfits. In this work, we consider the problem of the outfit compatibility prediction task, an integral part of the fashion outfit recommendation system. A compatibility prediction module determines whether all the items in an outfit are visually compatible with each other and match the user's preferences. Existing approaches can be grouped based on the representation scheme: (i) pair-wise and (ii) set or sequence. Pair-wise representation does not consider the outfit as a whole, and the sequence representation approaches are sensitive to the ordering of the items. Further, these methods do not explicitly capture the visual relationship between the items. We propose a novel method for the personalized outfit-compatible prediction task. The proposed method represents the outfit as a graph and uses a dot-attention graph neural network to capture the visual relationship between items. The graph read-out layer generates the final outfit embedding. A novel approach is proposed to model the user's preference for different styles. The final outfit compatibility score is generated by computing the similarity between outfit embedding and user embedding. Experimental results and ablation study on the Polyvore-U dataset, highlight the effectiveness of the proposed method.
pGBF: Personalized Gradient Boosting Forest

Due to the regulations to protect user data privacy and concerns about trade secrets, industrial organizations do not share user data with others. Federated learning makes it possible for multiple organizations to train a global model without revealing their data. Since, in real life, data distributions differ between organizations, it is necessary to personalize the model to have better performance for the data of a single participant. In this paper, we present the first personalized federated learning method for Gradient Boosting Decision Trees (GBDT) focusing on classification tasks, i.e., Personalized Gradient Boosting Forest (pGBF). Our method extends the existing federated learning method, Gradient Boosting Forest (GBF). Our experi-ments on three public datasets show that pGBF has better or similar performance to the existing methods, GBDT and GBF, in non-IID settings. Specifically, we find that our method has higher performance than GBDT when the data of the personalization target participant is small enough for GBDT model performance to be low. Moreover, pGBF has better performance than GBF when the data distributions among the participants are non-IID.
A Fine-grained Chinese Short Text Classification Method Based on Capsule Networks

Abstract Chinese short-text classification is an essential primary research direction in natural language processing. The main problem facing the current Chinese short text classification task is how to effectively extract critical semantic feature information and determine the text category within a short text length. Aiming at the problem of feature information extraction of Chinese short texts, this paper introduces a novel sparse attention mechanism. It proposes a Chinese short text classification model RGSC combined with capsule networks. After the model generates word vectors through the RoBERTa pre-training model, the Bi-GRU model initially extracts text feature information. The profound text feature extraction module SC, composed of sparse attention mechanism and capsule networks, is used to further extract critical semantic feature information. The results of ablation experiments on the TouTiao Chinese short text dataset and performance comparison experiments with various models show that the RGSC model can effectively extract the essential text feature information while effectively reducing the irrelevant noise information contained in the text features, and obtain fine-grained text feature information.
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness

Graph-based semi-supervised node classification has been shown to become a state-of-the-art approach in many applications with high research value and significance. Most existing methods are only based on the original intrinsic or artificially established graph structure which may not accurately reflect the “true” correlation among data and are not optimal for semi-supervised node classification in the downstream graph neural networks. Besides, while existing graph-based methods mostly utilize the explicit graph structure, some implicit information, for example, the density information, can also provide latent information that can be further exploited. To address these limitations, this paper proposes the Dual Hypergraph Neural Network (DualHGNN), a new dual connection model integrating both hypergraph structure learning and hypergraph representation learning simultaneously in a unified architecture. The DualHGNN first leverages a multi-view hypergraph learning network to explore the optimal hypergraph structure from multiple views, constrained by a consistency loss proposed to improve its generalization. Then, DualHGNN employs a density-aware hypergraph attention network to explore the high-order semantic correlation among data points based on the density-aware attention mechanism. Extensive experiments are conducted in various benchmark datasets, and the results demonstrate the effectiveness of the proposed approach.
Neural Linguistic Steganography with Controllable Security

Information hiding is an art and science with a long history and is widely used in covert communication. There are many ways to hide secret data in image, audio, and video. However, relatively few systems can hide information in text. Generative text steganography is a promising topic in natural language text infor-mation hiding. Previous generative text steganography methods use a fixed candidate pool generation rule, and they cannot effec-tively control the security of the generated text. The perceptual-imperceptibility and statistical-imperceptibility conflict effect also causes the poor quality of the steganographic text generated by previous generative text steganography methods. Moreover, pre-vious generative text steganography approaches barely discuss the robustness of steganographic text. This paper proposes a security controllable text steganography method that can generate natural-looking steganographic text with a statistical distribution that matches the natural language distribution. The proposed method combines the metrics of per-ceptual-imperceptibility and statistical-imperceptibility to calcu-late the combined distortion. It selects the tokens with the smallest combined distortion to construct a candidate pool at each time step. Moreover, the maximum combined distortion threshold is set when embedding secret messages to ensure controllable security. We conducted several experiments to evaluate the proposed model from the perspectives of embedding rate, perceptual-impercepti-bility, statistical-imperceptibility, and anti-attack ability. The ex-perimental results show that the proposed method can generate smooth and readable steganographic sentences with good re-sistance to steganalysis and high robustness.
Reservoir Computing for Symbol Detection of Optical Wireless Scattering Communications

Non-line-of-sight (NLOS) optical wireless communication (OWC) based on atmospheric scattering attracts more and more attention due to their mitigation of pointing, acquisition, and tracking requirement. However, the channel relies on scattering radiation and thus causes significant dispersion and inter-symbol interference (ISI), which limits high data rate communications. Reservoir computing (RC) is a computational framework derived from recurrent neural networks (RNNs). It is suitable for sequential data processing with low training cost. In contrast to multilayer perceptron (MLP)-based symbolwise detectors that treat ISI as noise, a RC performs sequence detection that takes ISI into account. In this paper, we propose to employ the RC approach to detect transmitted symbols of NLOS OWC systems without knowing channel state information. Our simulations show that the RC can perform symbol detection in various channel conditions and it brings lower bit error rate results than the MLP-based detection.
Causal Mean Field Multi-Agent Reinforcement Learning

Scalability remains a challenge in multi-agent reinforcement learning and is currently under active research. A framework named mean-field reinforcement learning (MFRL) could alleviate the scalability problem by employing the Mean Field Theory to turn a many-agent problem into a two-agent problem. However, this framework lacks the ability to identify essential interactions under nonstationary environments. Causality contains relatively invariant mechanisms behind interactions, though environments are nonstationary. Therefore, we propose an algorithm called causal mean-field Q-learning (CMFQ) to address the scalability problem. CMFQ is ever more robust toward the change of the number of agents though inheriting the compressed representation of MFRL's action-state space. Firstly, we model the causality behind the decision-making process of MFRL into a structural causal model (SCM). Then the essential degree of each interaction is quantified via intervening on the SCM. Furthermore, we design the causality-aware compact representation for behavioral information of agents as the weighted sum of all behavioral information according to their causal effects. We test CMFQ in a mixed cooperative-competitive game and a cooperative game. The result shows that our method has excellent scalability performance in both training in environments containing a large number of agents and testing in environments containing much more agents.
Towards infrared human pose estimation via Transformer

Due to the limited color information and low hierarchy present in infrared images, traditional CNN-based pose estimation networks designed for visible light often exhibit weak performance when applied to infrared images for human pose estimation. In order to overcome the inherent shortcomings of infrared images and improve the accuracy of human pose estimation in this domain, we propose a novel model called FEPose. Our model incorporates the Transformer Encoder architecture to establish correlation dependencies in the infrared image space, enhancing the network's ability to sense spatial distance and mitigating the impact of low hierarchy on detection accuracy. Additionally, we introduce a specially-designed FELayer layer for infrared images, which enhances the network's response to human grayscale values while reducing the impact of background interference factors. To evaluate the effectiveness of our model, we conduct experiments on a self-built IR multi-person pose estimation dataset comprising 7621 training instances and 1082 test instances. Our most complex model achieves a PCKm of 77.5, while the simplified model achieves 75.1 PCKm.
Adaptive Topological Graph Learning for Generalized Multi-View Clustering

Graph-based multi-view clustering methods construct affinity graphs to depict the potential cluster structure in given data and further partition them into respective groups without the supervision of labels. However, affinity graphs constructed by most of the existing methods lack the ability to precisely reflect the cluster structure of the original data, and fail to recover similarity information when there are missing instances involved. Additionally, current methods mainly employ pairwise relationships between instances to build the affinity graphs but ignore topological relationships, which causes insufficient use of underlying information and results in inferior results. To overcome these shortcomings, in this paper, we propose a novel graph learning method, which is enabled to solve a more general multi-view clustering (MVC) problem, i.e., MVC with probable missing instances. Specifically, a rank-constraint affinity learning method, which is capable to deal with both fully observed and partially observed data, is put forward to preserve similarity between existing instances and infer the similarity related to the missing instances. Moreover, a topological constraint is introduced on the learned affinity matrix, so that more comprehensive information, rather than limited pairwise relationships only, is embraced in each entry of the affinity matrix. Importantly, this is the first work using topological structure to conduct both complete and incomplete multi-view clustering in one unified learning framework. Extensive experiments under both complete and incomplete situations validate the effectiveness of our proposed method when compared to other state-of-the-art multi-view clustering algorithms. Our code has been released at https://github.com/WenjueHE/ATGL_GMVC.