Reviewerly/exHarmony huggingface dataset:
Path Integration Enhanced Graph Attention Network

Graph attention networks are a deep learning method for processing graph data. By learning the relationships between neighbouring nodes in the graph, GATs have been widely used in many fields. However, the graph attention network has the problem of information lag in the process of information aggregation, which degrades the performance of the graph attention network. Referring to the ideas of Feynman path integral theory in physics, we proposed a new graph attention method called PaInGAT to solve the above issue by introducing a new neighbor information aggregation mechanism. Specifically, we improve the neighbour node aggregation mechanism of traditional graph attention networks by calculating the path integral from the source node to the target node to obtain the attention factor, and update the information of multi-order neighbours to the central node directly by the attention factor of the current state at each layer. Through experimental demonstration combining different downstream tasks, our method achieves excellent results on several datasets, demonstrating its effectiveness and advancement.
Self-supervised Multi-view Clustering Framework with Graph Filtering and Contrast Fusion

With the increasing prevalence of multi-view data in practical applications, multi-view clustering has become popular due to its ability to integrate complementary information from multiple views to enhance clustering accuracy and robustness. However, existing multi-view clustering methods easily suffer from the following limitations: 1) Most existing methods assume that each sample can be well represented in the original data space, but real-world data inevitably contains redundancy and noise; 2) Existing comparative methods have sampling bias when selecting negative samples; 3) The pseudo-labels generated by model iteration are underused. To address these challenges, this paper proposes a self-supervised multi-view clustering framework with graph filtering and contrast fusion named SMCGC. Specifically, SMCGC first eliminates redundancies and noise in the original data through graph filtering and then uses contrast fusion to enhance the discriminative ability of the samples. This approach avoids the potential challenges related to negative sample selection and does not require the construction of positive and negative samples. For pseudo-labels, the framework utilizes self-supervised mechanisms to guide model operation and optimize cluster representation. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed SMCGC against the existing state-of-the-art methods.
Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations

With the extensive accumulation of conversational data on the Internet, emotion recognition in conversations (ERC) has received increasing attention. Previous efforts of this task mainly focus on leveraging contextual and speaker-specific features, or integrating heterogeneous external commonsense knowledge. Among them, some heavily rely on future contexts, which, however, are not always available in real-life scenarios. This fact inspires us to generate pseudo future contexts to improve ERC. Specifically, for an utterance, we generate its future context with pre-trained language models, potentially containing extra beneficial knowledge in a conversational form homogeneous with the historical ones. These characteristics make pseudo future contexts easily fused with historical contexts and historical speaker-specific contexts, yielding a conceptually simple framework systematically integrating multi-contexts. Experimental results on four ERC datasets demonstrate our method’s superiority. Further in-depth analyses reveal that pseudo future contexts can rival real ones to some extent, especially in relatively context-independent conversations.
Knowledge Graph Embedding with Relation Rotation and Entity Adjustment by Quaternions

Recent knowledge graph embedding models have shown promising results on link prediction, by employing operations on quaternion space to capture correlations between entities. However, they only used three quaternion embeddings for rotation calculation that fails to capture the interaction between entities and relations. The single relation quaternion to rotate the head entity also makes the connection between the head and tail entities weak. To address the problem, this paper proposes a novel knowledge graph embedding model denoted as QuatPE, to utilize paired relations to simultaneously rotate the head and tail entities in a triple. We employ the adjustment vectors to adjust the position of the same entity in a quaternion space when facing different triples. With paired relations, QuatPE can strengthen the connection between the head and tail entities which enhances the representation capabilities. By using the adjustment vectors, QuatPE also helps to better handle complex relation patterns, such as 1-to-N, N-to-1, and N-to-N. Experimental results show that QuatPE can achieve significant performance on well-known datasets for link prediction. Researchers can reproduce our results by following the source codes at https://github.com/galaxysunwen/QuatPE-master .
Efficient Graph Collaborative Filtering with Multi-layer Output-Enhanced Contrastive Learning

Recently, Contrastive Learning (CL) is becoming a mainstream approach to reduce the influence of data sparsity in recommendation system. However, existing methods do not fully explore the relationship between the outputs of different Graph Neural Network (GNN) layers and fail to fully utilize the capacity of combining GNN and CL for better recommendation. Within this paper, we introduce a novel approach based on CL, called efficient Graph collaborative filtering with multi-layer output-enhanced Contrastive Learning (GmoCL). It maximizes the benefits derived from the information propagation property of GNN with multi-layer aggregation to obtain better node representations. Specifically, the construction of CL tasks involves considerations from both intra-layer and inter-layer perspectives. The goal of intra-layer CL task is to exploit the semantic similarities of different users (or items) on a certain GNN layer. The inter-layer CL task aims to make the outputs of different GNN layers of the same user (or item) more similar. Additionally, we propose the strategy of negative sampling in the inter-layer CL task to learn the better node representations. The efficacy of the suggested approach is validated through comprehensive experiments conducted on five publicly available datasets.
An Elastic Scalable Grouping for Stateful Operators in Stream Computing Systems

In distributed stream computing systems, dynamic data skew and cluster heterogeneity can lead to major load imbalance among multiple instances of stateful operators. Existing stream grouping schemes mainly focus on data load balancing for stateful operators, but they are not considered to be sufficiently elastic scalable, which directly affects the latency and throughput. We propose an elastic scalable grouping (called Es-Stream) for stateful operators. This paper discusses the following aspects: (1) Investigating the dynamic grouping of real-time data stream, proposing a general data stream graph model and a data stream grouping model, as well as formalizing the problem of load balancing optimization and data stream grouping. (2) Utilizing key splitting to solve the bottleneck problem caused by high-frequency keys in the data streams, and lightweight weight adjustment strategy to dynamically change the data tuple allocation probability of the instance according to the network cost, data stream rate and processing rate. (3) Implementing Es-Stream in Apache Storm platform and evaluating the system using metrics such as latency, throughput and load imbalance. Experimental results showed that Es-Stream reduces latency by up to 72%, increases throughput by up to 44% and reduces load imbalance by up to 75%, compared with existing state-of-the-art grouping schemes.
Calibrating Popularity Bias Based on Quality for Recommendation Fairness

Recommendation fairness has become a significant topic recently. One obstacle to it is popularity bias. Previous works reduce the bias by eliminating popularity difference entirely. However, items with higher quality should be assigned with higher popularity. Completely excluding popularity will cripple recommender on recommendation accuracy and unfairly treat items with high quality. For this reason, we argue that the popularity of items should not be avoided at all costs but instead calibrated based on their quality, which is a depiction of items’ natural properties. To this end, we propose the Quality Recommendation (QR) framework. Specifically, we separate item embedding into quality embedding and non-popularity ID embedding. The former efficiently encodes quality related features and the latter eliminates stereotype of specific items’ popularity. To evaluate model vulnerability to popularity-quality discrepancy, we propose a novel evaluation method. Its core idea is simulate this discrepancy at the training stage. Experiments on datasets show the effective fairness and recommendation performance of our proposed methods.
SVIM: A Skeleton-Based View-Invariant Method for Online Gesture Recognition

Online gesture recognition is a challenging task in practical application scenarios since the gesture is not always directly in front of the camera. In order to solve the challenges caused by multiple viewpoints of skeleton data, in this paper, we proposed a novel view-invariant method for online skeleton gesture recognition. The whole skeleton sequence data as a point set in our method and a PCA-based view-invariant data preprocessing algorithm is proposed and applied in this paper. We can transform similar skeleton data to relatively stable viewpoints by applying the PCA algorithm according to the similarity of distribution features of the point set, which can ensures the viewpoint stability of our gesture recognition model. We conduct extensive experiments on the NTU RGB+D and Northwestern-UCLA benchmark datasets which contain multiple viewpoints and the results have demonstrated the effectiveness of the method proposed in this paper.
UKGAT: Uncertain Knowledge Graph Embedding Enriched KGAT for Recommendation

Heterogeneous Information Networks (HINs) with complex structures and rich semantics, which show great flexibility in modeling the heterogeneous data of HINs, are widely used in recommendation systems. Effective embedding of HINs not only represents the complex and heterogeneous auxiliary data in the graph, facilitating efficient extraction and utilization of information by the recommender system, but also has the advantage of retaining rich semantics, which provides more accurate, varied, and interpretable recommendation results. An uncertain knowledge graph embedding method based on the expanded meta-path semantics of KGAT was proposed, which not only inherits the higher-order connectivity among nodes in KGAT in an end-to-end approach with recursive delivery of embedded information (either users, items, or attributes) from neighboring nodes but also enables the relationships in HINs to be uncertain by expanding the meta-path semantics of HINs to better capture the underlying semantic relationships among entities and then incorporate the structured knowledge they contain into machine learning. Finally, the effectiveness of the UKGAT was demonstrated through experiments on two real-world datasets, which outperformed state-of-the-art models in capturing semantic relationships among entities, it could be better utilized in many knowledge-driven applications.
Wavelet-SVDD: Anomaly Detection and Segmentation with Frequency Domain Attention

Anomaly detection is a formidable challenge that entails the formulation of a model capable of detecting anomalous patterns in datasets, even when anomalous data points are absent. Traditional algorithms focused on learning knowledge regarding the typical features that arise in images, such as texture, shape, and color, to distinguish between normal and anomalous examples. However, there is untapped potential in frequency domain features for differentiating anomalous patterns, and current methodologies have not exhaustively exploited this avenue. In this work, we present an extension of the deep learning version of support vector data description (SVDD), a prevalent algorithm used for anomaly detection, through the introduction of Wavelet transformation and frequency domain attentions in the feature learning network. This extension allows for the consideration of frequency domain patterns in defect detection, and improves detection performance significantly. We performed extensive experiments on the MVTecAD dataset, and the results revealed that our approach attained advanced performance in both anomaly detection and segmentation localization, thereby confirming the efficacy of our proposed innovative designs.
VAASI: Crafting valid and abnormal adversarial samples for anomaly detection systems in industrial scenarios

In the realm of industrial anomaly detection, machine and deep learning models face a critical vulnerability to adversarial attacks. In this context, existing attack methodologies primarily target continuous features, often in the context of images, making them unsuitable for the categorical or discrete features prevalent in industrial systems. To fortify the cybersecurity of industrial environments, this paper introduces a groundbreaking adversarial attack approach tailored to the unique demands of these settings. Our novel technique enables the creation of targeted adversarial samples that are valid within the framework of supervised cyberattack detection models in industrial scenarios, preserving the consistency of discrete values and correcting cases where an adversarial sample transitions into a normal one. Our approach leverages the SHAP interpretability method to identify the most salient features for each sample. Subsequently, the Projected Gradient Descent technique is employed to perturb continuous features, ensuring adversarial sample generation. To handle categorical features for a specific adversarial sample, our method scrutinizes the closest sample within the normal training dataset and replicates its categorical feature values. Additionally, Decision Trees trained within a Random Forest are utilized to ensure that the resulting adversarial samples maintain the essential abnormal behavior required for detection. The validation of our proposal was conducted using the WADI dataset obtained from a water distribution plant, providing a realistic industrial context. During validation, we assessed the mean error and the total number of adversarial samples generated by our approach, comparing it with the original Projected Gradient Descent method and the Carlini & Wagner attack across various parameter configurations. Remarkably, our proposal consistently achieved the best trade-off between mean error and the number of generated adversarial samples, showcasing its superiority in safeguarding industrial systems.
PVO-based reversible data hiding using bit plane segmentation and pixel expansion

The existing pixel value ordering (PVO)-based reversible data hiding (RDH) approaches have been investigated; however, the functions of these techniques are weak when embedded in rough image regions. Therefore, a novel PVO-RDH method is proposed herein. This method performs ‘bit-plane segmentation low bit-plane weighted-pixel expansion’ on an image, which improves the original PVO-RDH method’s embedding performance in rough image regions. Pixel expansion increases the number of image chunks, thereby improving the maximum embedding capability of the carrier image. After preprocessing, the expanded picture is chunked using the original PVO-RDH strategy, and the maximum value in the chunk is modified via prediction. In contrast to the results of previous studies, the current experimental findings indicate that the proposed strategy improves the average PSNR by at least 2.97 dB when the embedding data are 10000 bit. The experimental findings show a significant improvement in the embedding volume and PSNR of the proposed algorithm.
Two phase representation discriminant projection for face recognition

This paper proposes a novel approach called two-phase representation discriminant projection (TPRDP) for facial recognition. It uses two-phase representation learning to obtain a robust representation for each training sample. In the first phase, the method identifies a set of closely related samples to the training sample based on representation-based distances. In the second phase, TPRDP uses these samples to linearly encode the training sample, effectively enhancing its representation capability. After that, TPRDP constructs two graphs to capture relationships between samples. By leveraging these graphs, TPRDP seeks an optimized transformation matrix that enhances both within-class compactness and between-class separability. Through the projection of samples into a subspace using this matrix, TPRDP effectively improves the discrimination between different classes while preserving compactness within each class. Comprehensive experiments on various face datasets, including AR, CMU PIE, and ExtYaleB, confirm the effectiveness of our method.
Semi-supervised medical image segmentation via feature similarity and reliable-region enhancement

Semantic segmentation is a crucial task in the field of computer vision, and medical image segmentation, as its downstream task, has made significant breakthroughs in recent years. However, the issue of requiring a large number of annotations in medical image segmentation has remained a major challenge. Semi-supervised semantic segmentation has provided a powerful approach to address the annotation problem. Nevertheless, existing semi-supervised semantic segmentation methods in medical images have drawbacks, such as insufficient exploitation of unlabeled data information and inefficient utilization of all pseudo-label information. We introduces a novel segmentation model, the Feature Similarity and Reliable-region Enhancement Network (FSRENet), to overcome these limitations. Firstly, this paper proposes a Feature Similarity Module (FSM), which combines the dense feature prediction ability of true labels for unlabeled images with segmentation features as additional constraints, utilizing the similarity relationship between dense features to constrain segmentation features, and thus fully exploiting the dense feature information of unlabeled data. Additionally, the Reliable-region Enhancement Module (REM) designs a high-confidence network structure by fusing two networks that can learn from each other, forming a triple-network structure. The high-confidence network generates reliable pseudo-labels that further constrain the predictions of the two networks, achieving the goal of enhancing the weight of reliable regions, reducing the noise interference of pseudo-labels, and efficiently utilizing all pseudo-label information. Experimental results on the ACDC and LA datasets demonstrate that the FSRENet model proposed in this paper excels in the task of semi-supervised semantic segmentation of medical images and outperforms the majority of existing methods. Our code is available at: https://github.com/gdghds0/FSRENet-master.
A deep learning knowledge graph neural network for recommender systems

Knowledge graphs are becoming the new state-of-the-art for recommender systems. This paper is based on knowledge graphs to alleviate the problem of data sparsity. Various methods have been recently deployed to solve this problem which largely attempts to study user-item representation and then recommend items to users based on these representations. Although these methods are effective, they lack explainability for recommendations and do not mine side information. In this paper, we propose the use of knowledge graphs which includes additional information about users and items in addition to the use of a user/item interaction matrix. The vital element of our model is neighbourhood aggregation for collaborative filtering. Every user and item are associated with an ID embedding, which is circulated on the interaction graph for users, items, and their attributes. We obtain the final embeddings by combining the embeddings learned at various hidden layers with a biased sum. Our model is easier to train and achieves better performance compared to graph neural network-based collaborative filtering (GCF) and other state-of-the-art recommender methods. We provide evidence for our argument by analytically comparing the knowledge graph convolution network (KGCN) with GCF and eight other state-of-the-art methods, using similar experimental settings and the same datasets.
From Temporal-Evolving to Spatial-Fixing: A Keypoints-Based Learning Paradigm for Visual Robotic Manipulation

The current learning pipelines for robotics manipulation infer movement primitives sequentially along the temporal-evolving axis, which can result in an accumulation of prediction errors and subsequently cause the visual observations to fall out of the training distribution. This paper proposes a novel hierarchical behavior cloning approach which tries to dissociate standard behaviour cloning (BC) pipeline to two stages. The intuition of this approach is to eliminate accumu-lation errors using a fixed spatial representation. At first stage, a high-level planner will be employed to translate the initial observation of the scene into task-specific spatial waypoints. Then, a low-level robotic path planner takes over the task of guiding the robot by executing a set of pre-defined elementary movements or actions known as primitives, with the goal of reaching the previously predicted waypoints. Our hierarchical keypoints-based paradigm aims to simplify existing temporal-evolving approach to a more simple way: directly spatialize the whole sequential primitives as a set of 8D waypoints only from the very first observation. Plentiful experiments demon-strate that our paradigm can achieve comparable results with Reinforcement Learning (RL) and outperforms existing offline BC approaches, with only a single-shot inference from the initial observation. Code and models are available at: https://github.com/KevinRiou22/spatial-fixing-il