Reviewerly/exHarmony huggingface dataset:
DNFS: A Digraph Neural Network with the First-Order and the Second-Order Similarity

Graph Neural Networks (GNNs) have made remarkable achievements on various graph tasks such as node classification, link prediction, and graph clustering. However, most GNNs focus on undirected graphs and limited effort has been made to handle directed graphs (digraphs) due to their uniqueness and complexity. In this article, we focus on directed graphs and propose DNFS, a Digraph Neural network model with the First-order and the Second-order similarities, which can preserve unique directional information and explore complex higher-order information in the digraphs. DNFS differentiates the in-degree and out-degree similarity matrices to carefully represent directional information of the digraphs, and it considers both the first-order and the second-order similarities to capture subtle structural information. Extensive experiments on public benchmark datasets demonstrate the effectiveness of our model compared to state-of-the-art baselines.
GNN-MRC: Machine Reading Comprehension Based on GNN Augmentation

Inspired by recent advances in retrieval-augmented methods in NLP, in this work, we introduce GNN-MRC (Machine Reading Comprehension) framework, which extends vanilla BERT-MRC by allowing it to retrieve similar contexts in the training set. We construct the directed heterogeneous graph by taking the tokens in the input context (question + passage) and the retrieved similar contexts as nodes and the connections between nodes as edges. Graph neural networks (GNNs) are built on the graph to aggregate information from neighbor contexts to augment the distribution of answer labels. This paradigm improves the model’s ability to deal with the long-tailed distribution problem and few-shot learning. We conduct comprehensive experiments to show the effectiveness of the GNN-MRC, which consistently outperforms its vanilla counterparts: we improved results on a variety of widely used MRC benchmarks SQuAD 1.1 and SQuAD2.0, and can achieve comparable results to the vanilla MRC model with 20% less amount of training data.
Co-RGCN: A Bi-path GCN-Based Co-Regression Model for Multi-intent Detection and Slot Filling

Since people’s utterance usually has multiple intents, joint models of multi-intent detection and slot filling have high research value in realistic scenarios. However, the existing models lack the syntactic dependencies and semantic contextual relations of the utterance to guide the slot filling and multi-intent detection, which cannot effectively resolve word order variations and semantic dependencies in a complex utterance. Meanwhile, these models lack the fine-grained slot information to guide the multi-intent detection, fail to utilize the semantic relevance of the rich intent and slot information well. In this paper, we propose a Bi-path GCN-based Co-Regression model for Multi-intent Detection and Slot Filling (Co-RGCN), which contains two modules: (1) Bi-path GCN module, which contains SynGCN and SemGCN, guiding slot filling and multi-intent detection by extracting syntactic and semantic contextual information of the utterance; (2) Co-Regression module, which achieves bi-directional joint modeling through the co-regression operations on multi-intent information and slot information. Experiments on two datasets show that our model performs more effectively than competing models and achieves SOTA performance.
Transformer Based Prototype Learning for Weakly-Supervised Histopathology Tissue Semantic Segmentation

Weakly-supervised semantic segmentation for computational pathology has the great potential to alleviate the time-consuming and labor-intensive burden of manual pixel-level annotations. Existing methods relying on class activation map (CAM) to localize target objects suffer from two problems. First, most CAM-based models adopt convolutional neural networks, which cannot model the long-range dependencies of dispersed tissues. Second, CAM tends to focus on the most discriminative region of the object, resulting in incomplete segmentation results. In this paper, we propose a novel Transformer based weakly-supervised model for pixel-level tissue segmentation. The proposed model is able to capture global tissue feature relations by the self-attention mechanism in Transformer. For the issue of incomplete segmentation in CAM, we propose a patch-token prototype self-supervised learning approach to obtain more complete localization maps. Additionally, we introduce a self-refinement mechanism to dampen the falsely activated regions in the initial localization map. Extensive experiments on two histopathology datasets demonstrate that our proposed model achieves the state-of-the-art performance compared with other weakly-supervised methods.
K-DLM: A Domain-Adaptive Language Model Pre-Training Framework with Knowledge Graph

Despite the excellent performance of pre-trained language models, such as BERT, on various natural language processing tasks, they struggle with tasks that require domain-specific knowledge. Integrating information from knowledge graphs through pre-training tasks is a common approach. However, existing models tend to focus on entity information at the word level and fail to capture the rich information in knowledge graphs. To address this issue, we propose a domain-adaptive language model pre-training framework with a knowledge graph (K-DLM). K-DLM can learn both word and lexical-semantic level entity information and relationships from the knowledge graph. It predicts entity categories and sememes for masked phrases, replaces entities in sentences according to the knowledge graph, and learns relationship information via contrastive learning. The evaluation on open-domain and domain-specific tasks demonstrates that K-DLM outperforms previous models, particularly in domain-specific contexts. Our findings highlight K-DLM as an excellent pre-training framework for knowledge-driven problems that leverage domain knowledge graphs.
BIG-FG: A Bi-directional Interaction Graph Framework with Filter Gate Mechanism for Chinese Spoken Language Understanding

Spoken language understanding (SLU) primarily entailing slot filling and intent detection has been studied for many years with achieving significant results. However, in Chinese SLU tasks, Some models fail to take word-level information into account, and there is insufficient interaction between slot information and intent information. To address the aforementioned issues, we propose a novel bi-directional interaction graph framework with filter gate mechanism (BIG-FG) for Chinese spoken language understanding, which can make a fine-grained interaction directly with slot information and intent information, while also effectively fusing character-word semantic information. The model consists of two core modules: (1) bi-directional interaction graph (BIG), which is based on a multi-layer graph attention network with the bi-directional connections between intent information, slot information, and adjacent slot information, fully considering the correlation between slot filling and intent detection; (2) filter gate (FG), which enhances fusion performance by solving the problem of semantic ambiguity brought by direct fusion of character-word semantic information. Experiments on two datasets demonstrate that our model outperforms the best benchmark model by 0.39% and 2.65% in the Overall(Acc) evaluation metric, respectively, and accomplishes the state-of-the-arts performance.
GatedGCN with GraphSage to Solve Traveling Salesman Problem

Graph neural networks have shown good performance in many domains, as well as in combinatorial optimization. This paper proposes a new graph neural network framework to deal with the classical combinatorial optimization problem, the traveling salesman problem (TSP). The proposed framework is composed of GraphSage and GatedGCN jointly, named GGCN_GSG, where the output of GraphSage is the input of GatedGCN. With each TSP graph being used as data input, each node and its neighbors in the graph are embedded into the d-dimensional feature vector through GraphSage, and GatedGCN adds the distance information of the edge into the update function, and controls whether the TSP node enters the update function through the gated mechanism. Experimental results show that our proposed framework can get closer to the optimal solution than comparable graph neural network frameworks and other learning-based methods, achieving an optimal solution of 3.83 at 20 nodes and an optimal ratio of 30 nodes 2x increase.
Event Association Analysis Using Graph Rules

Event association analysis is used to mine potential association relationships between different events in event data sets, and is widely used in areas such as commodity trading and social media networks. Graph rules can be defined with different forms and expressive capabilities according to relationships in various domains. For the event association domain, we define an event association rule based on statistical knowledge to examine positive and negative associations between events, and the generated patterns fuse other semantic information such as time and location to realize the inference on event data. We innovatively propose a matching method for matching candidate sets, which can further refine the rule matching results to each specific event node so that the results can be logically interpreted. Meanwhile, based on the existing rule matching algorithms, we propose an incremental computation method that can quickly process the incremental part of the event data, effectively saving computational resources and time. We demonstrate the accuracy and efficiency of the algorithm using real-life datasets.
Graph Convolutional Network Semantic Enhancement Hashing for Self-supervised Cross-Modal Retrieval

Cross-modal hashing has gained widespread attention for its computational efficiency and reduced storage costs while achieving great success in cross-modal retrieval. However, the data information of different modalities is asymmetric, which means that we usually think of images as being much more informative than text. Currently, most previous methods have no obvious difference in feature extraction and hash function learning modules, and they do not address the semantic gap between different modalities. We propose Graph Convolutional Network Semantic Enhancement Hashing (GCSEH) approach. Specifically, We aim to let information-rich modalities further support information-poor modalities to bridge the semantic gap between different modalities. In addition, in order to more accurately capture the semantic affinity between modalities, in order to be able to discover high-level semantic information, we choose to use a self-supervised semantic network. Extensive experiments have proven that our GCSEH method can achieve excellent performance.
Efficient Question Answering Based on Language Models and Knowledge Graphs

Knowledge graph question answering (Q &A) aims to answer questions through a knowledge base (KB). When using a knowledge base as a data source for multihop Q &A, knowledge graph Q &A needs to obtain relevant entities, their relationships and the correct answer, but often the correct answer cannot be obtained through the reasoning path because of absent relationships. Currently, using pre-trained language models (PLM) and knowledge graphs (KG) has a good effect on complex problems. However, challenging problems remain; the relationships between problems and candidate entities need to be better represented, and joint reasoning must be performed in the relationship graph based on problems and entities. To solve these problems, we expand the relational graph by adding tail entities to the list of preselected entities through reverse relations and then add the processed problems and entities to the problem subgraph. To perform inference on a relational graph, we design an attention-based neural network module. To calculate the loss of the model’s inference process nodes, we use a modified Euclidean distance function as the loss function. To evaluate our model, we conducted experiments on the WebQSP and CWQ datasets, and the model obtained state-of-the-art results in both the KB-full and KB-half settings.
Low-resolution object detection via a lightweight super-resolution network

The object detection algorithm has developed rapidly in recent years and achieved excellent results on various benchmark datasets. However, these datasets are usually composed of high-quality [high resolution (HR), high signal-to-noise ratio, etc.] images. In many scenarios, we need to detect objects in low-resolution (LR) images. But the detector trained in HR images performs poorly in LR images. The image super-resolution (SR) algorithm is an important image processing technology that has been proven to improve the performance of various visual tasks. Based on this consideration, we combine image SR technology with the object detection task to design an object detection algorithm for detecting LR images. Specifically, we propose a lightweight SR algorithm that achieves a good balance between parameters and performance. We perform SR reconstruction on the LR image in advance and detect the reconstructed image instead of directly detecting the LR image. Both quantitative and qualitative experimental results show that our LR object detection algorithm significantly improves the detection performance of LR images.
Finding a home in or through mobile phones: Access and usage patterns among homeless women in shelter-homes of India

With the number of mobile phone subscribers increasing across all sections of society, this paper aims to understand if homeless women in shelter-homes of India have access to mobile phones and, if so, what are their usage patterns. Facilitated by issues ranging from domestic violence to lack of employment to forced migration and even to human trafficking and rape, women residing in the shelter-homes of India have a myriad of stories to tell. Not all of them have been on streets forever and many have the basic literacy to understand how to use a mobile phone. For the purposes of the study, the researchers engaged with women above 18 years of age up to the age of 45 in a series of open-ended interviews to understand their access to mobile phones and contextualize their homelessness within an affordances-based framework for mobile phone access. This paper takes into account the dual barriers of gender and the lack of a home to study the digital divide experienced by homeless women in India, expanding on how both these factors shape their access and usage, eventually bridging the digital divide, and whether mobile phones are required/desired by the women themselves. The study found that the women staying in shelter-homes see a window of hope in their use of mobile phones in the form of an independent financial future or an independent marital life, but the biased perception of using a mobile phone among women hinders women’s access to and use of these devices, which ultimately results in the loss of any kind of opportunity before it has even been explored, perhaps leading to the loss of a chance at a bright future.
SS-Net: 3D Spatial-Spectral Network for Cerebrovascular Segmentation in TOF-MRA

The extraction of cerebrovascular structure plays a pivotal role in the diagnosis and analysis of various cerebrovascular diseases. However, cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) volumes remains a challenging task due to the complex topology, slender contour, and noisy background. This paper proposes a 3D SS-Net that combines the spatial and spectral domain features to accurately segment the cerebral vasculature. The SS-Net is based on an end-to-end autoencoder architecture, which incorporates both spatial and a spectral encoders. The spectral encoder branch applies 3D fast Fourier convolution (FFC) to extract global features and frequency domain features in the shallow layers of the network. Furthermore, we introduce cerebrovascular edge supervised information, which enables the network to model the high-frequency variations and distribution patterns of cerebrovascular edges more effectively. Experimental results show that the SS-Net delivers outstanding performance, achieving the DSC of 71.14% on a publicly available dataset and outperforming other 3D deep-learning-based approaches. Code: github.com/y8421036/SS-Net.
A Partitioned Detection Architecture for Oriented Objects

Rotated object detection aims to identify and locate objects in images with oriented angles. In this case, the detection scenario varies a lot in multi-dimensions. Besides, multiple oriented objects always exist within a single image, which increase the complexity for angle prediction. Although representing the angle by an extra regression branch can enhance precision in rotated box detector, it’s more plausible to optimize the prediction process in a global view including feature-extraction, detection field partition and angle regression branch. In this paper, we introduced a partitioned detection architecture to identify objects which are at different scales, corresponding to different feature levels of the network. Inspired by Vision Transformer, a series of novel attention layers are embedded seamlessly for extracting necessary features from different scales and regions. We also conducted a short-term diffusion process to produce Gaussian noise in original image considering generalizability. As is shown, our detectors show a significant improvement of mAP and balance between performance and accuracy in two challenging aerial detection task, DOTA and HRSC2016.
Contrastive Learning and the Emergence of Attributes Associations

In response to an object presentation, supervised learning schemes generally respond with a parsimonious label. Upon a similar presentation we humans respond again with a label, but are flooded, in addition, by a myriad of associations. A significant portion of these consist of the presented object attributes (e.g. “comprises one long quasi vertical segment”). Contrastive learning is a self supervised learning scheme based on the application of identity preserving transformations on the object input representations. It is intuitively assumed in this work that the same applied transformations preserve, in addition to the identity of the presented object, also the identity of its semantically meaningful attributes. The natural outcome of this intuition is that the output representations of such a contrastive learning scheme contain valuable information not only for the classification of the presented object, but also for the decision regarding the presence or absence of any attribute of interest. Simulation results which demonstrate this idea and the feasibility of this hypothesis are presented.
Addressing Delays in Reinforcement Learning via Delayed Adversarial Imitation Learning

Observation and action delays occur commonly in many real-world tasks which violate Markov property and consequently degrade the performance of Reinforcement Learning methods. So far, there have been several efforts on delays in RL. Model-based methods train forward models to predict unknown current information while model-free approaches focus on state-augmentation to define new Markov Decision Processes. However, previous works suffer from difficult model fine-tuning and the curse of dimensionality that prevent them from solving delays. Motivated by the advantage of imitation learning, a novel idea is introduced that a delayed policy can be trained by imitating undelayed expert demonstrations. Based on the idea, we propose an algorithm named Delayed Adversarial Imitation Learning (DAIL). In DAIL, a few undelayed expert demonstrations are utilized to generate a surrogate delayed expert and a delayed policy is trained by imitating the surrogate expert using adversarial imitation learning. Moreover, a theoretical analysis of DAIL is presented to validate the rationality of DAIL and guide the practical design of the approach. Finally, experiments on continuous control tasks demonstrate that DAIL achieves much higher performance than previous approaches in solving delays in RL, where DAIL can converge to high performance with an excellent sample efficiency, even for substantial delays, while previous works cannot due to the divergence problems.
An Evaluation of Self-supervised Learning for Portfolio Diversification

Recently self-supervised learning (SSL) has achieved impressive performance in computer vision (CV) and natural language processing (NLP) tasks, and some early attempts are made in the area of finance. In this paper, we apply SSL to extract features from financial time series data, and use those features to measure the similarities between assets in the market. As similarity measurement is the key to portfolio diversification, we consider two portfolio optimisation problems: index tracking (IT) and minimum variance portfolio (MVP), with the additional diversification terms linked to different similarity measurements, which are sourced from different SSL algorithms. Both IT and MVP are both convex optimisation problems with deterministic solutions, therefore the performance difference is traced back to SSL algorithms, rather than other factors. Extensive experiments are conducted with eight SSL algorithms, and the analysis of the results of the experiments demonstrates the advantages of SSL over non-SSL alternatives.
Enhancing Text2SQL Generation with Syntactic Information and Multi-task Learning

The Text2SQL task aims to convert natural language (NL) questions into SQL queries. Most rule-based traditional syntactic parsers are often ignoring the syntactic information in the question and unseen database schemas, which lead to lower generalization ability. To breakthrough these limitations, we propose a novel model Syn-RGAT that leverages syntactic information of questions. Specifically, our model jointly encodes database schemas and questions by mapping them into a graph, which is then passed through a relation-aware graph attention network (RGAT). For the questions, map it as a syntax information graph and use syntactic augmentation module to learn all the nodes representations of the graph. Then the outputs of RGAT and syntactic augmentation module are integrated. Additionally, to assist the model distinguish between different database schemas, we introduce a graph pruning task and form a multi-task framework which shares the encoder of Text2SQL task. We use a heuristic learning method to combine graph pruning tasks with Text2SQL. In experimental part, Syn-RGAT outperforms all baseline models on the Spider dataset, and we further improve the performance more than 8% with BERT.
A Personalized Federated Multi-task Learning Scheme for Encrypted Traffic Classification

Traffic classification is a crucial technology for ensuring Quality of Service (QoS) in network services and network security management. Deep learning has shown great promise in this area, particularly for classifying encrypted traffic. However, the significant number of samples required for training models presents a challenge. In this paper, we propose a multi-task learning and Federated Learning approach for training multi-task models for encrypted traffic classification in a privacy-protected, multi-enterprise setting. Our proposed two-stage federated multi-task learning scheme, pFedDAMT, aims to address data heterogeneity by first obtaining a global multi-task model that performs well for all tasks and then personalizing and fine-tuning the model with each enterprise’s dataset to generate personalized models. Our experiments demonstrate that pFedDAMT improves prediction accuracy by an average of 1.58% compared to other schemes.