Next Basket Recommendation (NBR) predicts unordered item sets for users' next purchases, crucial for grocery shopping and online retail scenarios. However, existing NBR methods face two critical challenges: (1) They neglect the threshold-meeting phenomenon, such as common add-to-cart behaviors where users intentionally include seemingly irrelevant items to meet promotional or free shipping price thresholds, which is a widespread phenomenon in reality but has not been a dedicated research focus in the recommendation domain; (2) Even though many works study repetitive and exploratory recommendations, they still lack a theoretically analyzable mechanism to directionally decouple these patterns. This may limit the model's performance. To address these issues, we propose DiffNBR, the first diffusion-model-based framework for NBR. DiffNBR employs two denoising diffusion probabilistic models (DDPMs) to jointly learn users' purchasing behaviors in in spatial and temporal dimensions, modeling the latent compositional strategies the dynamic evolution behind phenomena like threshold-meeting. Moreover, we integrate the information bottleneck theory to enforce directional decoupling between repetition and exploration by explicitly regulating information flows. Specifically, our framework constrains the model's generative representation to focus on learning the exploratory patterns, while the habitual repurchase representation is responsible for the repetitive patterns. Extensive experiments on four real-world datasets show that DiffNBR outperforms the state-of-the-art (SOTA) methods.

Recent research indicates that adding residual connections in Graph Neural Networks (GNNs) would amplify susceptibility to anomalous nodes, consequently undermining the robustness of deep GNNs in practical settings. However, existing verification methods encounter challenges with the increasing number of parameters and computational overhead in deep GNNs. In this paper, we derive the general form of the residual connections and apply the dual backpropagation network to deep GNNs. Considering the heightened computational errors arising from the increased number of layers in deep GNNs, we propose a new method for calculating intermediate activation bounds of GNNs based on linear approximation. Experimental results show that new method can effectively enhance the verification accuracy. Notably, the maximum perturbation value of nodes correctly classified shows an average improvement of 119.5%. To showcase the the efficacy and scalability of our method, we verify robustness of deep GNNs on six different graph datasets, and our method can effectively verify the robustness of deep GNNs even with 32 layers of residual connections, i.e. verify over 87.29% of nodes in the Citeseer dataset. Furthermore, we analyse the influence of the graph structural properties on the robustness of the model.

Weakly Supervised Entity Alignment (EA) is the task of identifying equivalent entities across diverse knowledge graphs (KGs) using only a limited number of seed alignments. Despite substantial advances in aggregation-based weakly supervised EA, the underlying mechanisms in this setting remain unexplored. In this article, we present a propagation perspective to analyze weakly supervised EA and explain the existing aggregation-based EA models. Our theoretical analysis reveals that these models essentially seek propagation operators for pairwise entity similarities. We further prove that, despite the structural heterogeneity across different KGs, the potentially aligned entities within aggregation-based EA models exhibit isomorphic subgraphs, a fundamental yet underexplored premise of EA. Leveraging this insight, we introduce a potential isomorphism propagation operator to enhance the propagation of neighborhood information across KGs. We develop a general EA framework, PipEA, incorporating this operator to improve the accuracy of every type of aggregation-based model without altering the learning process. Extensive experiments substantiate our theoretical findings and demonstrate PipEA’s significant performance gains over state-of-the-art weakly supervised EA methods. Our work advances the field and enhances our comprehension of aggregation-based weakly supervised EA.

Recently, reconstruction-based deep models have gained widespread usage in unsupervised anomaly detection. However, they may overlook some anomalies owing to the over-generalization of neural networks. Several studies have incorporated memory networks to mitigate this problem. Nonetheless, some of them lack an explicit memory updating process, while others rely on data-driven updating methods that are sensitive to initial values and unsuitable for end-to-end training. Additionally, the traditional criterion for detection computed in the high-dimensional input space may collapse as the spike in the deviation score is averaged across numerous dimensions. To address these challenges, we propose MCAKE, a M emory-augmented C ontrastive A utoencoder with K NN-Based E xtraction. It is designed to highlight the deviation score for anomalies by reconstructing input using fixed normal prototypes recorded in the memory. We explicitly encourage the memory to be autonomously learned and effectively allocated through contrastive learning with multiple positive and multiple negative samples. Furthermore, we introduce a bivariate detection criterion that calculates anomaly scores considering both input and latent space to tackle the collapse. Extensive experiments on 50 datasets across various categories demonstrate the superiority of our approach, with a 2% relative improvement over the previous state-of-the-art models.

Multi-Modal Entity Alignment (MMEA) is a pivotal task in Multi-Modal Knowledge Graphs (MMKGs), seeking to identify identical entities by leveraging associated modal attributes. However, real-world MMKGs confront the challenges of semantic inconsistency arising from diverse and incomplete data sources. This inconsistency is predominantly caused by the absence of specific modal attributes, manifesting in two distinct forms: disparities in attribute counts or the absence of certain modalities. Current methods address these issues through attribute interpolation, but their reliance on predefined distributions introduces modality noise, compromising original semantic information. Furthermore, the absence of a generalizable theoretical principle hampers progress towards achieving semantic consistency. In this work, we propose a generalizable theoretical principle by examining semantic consistency from the perspective of Dirichlet energy. Our research reveals that, in the presence of semantic inconsistency, models tend to overfit to modality noise, leading to over-smoothing and performance oscillations or declines, particularly in scenarios with a high rate of missing modality. To overcome these challenges, we propose DESAlign, a robust method addressing the over-smoothing caused by semantic inconsistency and interpolating missing semantics using existing modalities. Specifically, we devise a training strategy for multi-modal knowledge graph learning based on our proposed principle. Then, we introduce a propagation strategy that utilizes existing features to provide interpolation solutions for missing semantic features. DESAlign outperforms existing approaches across 60 benchmark splits, encompassing both monolingual and bilingual scenarios, achieving state-of-the-art performance. Experiments on splits with high missing modal attributes demonstrate its effectiveness, providing a robust MMEA solution to semantic inconsistency in real-world MMKGs.

Anomaly detection in multivariate time series (MTS) is crucial for various applications in data mining and industry. Current industrial methods typically approach anomaly detection as an unsupervised learning task, aiming to identify deviations by estimating the normal distribution in noisy, label-free datasets. These methods increasingly incorporate interdependencies between channels through graph structures to enhance accuracy. However, the role of interdependencies is more critical than previously understood, as shifts in interdependencies between MTS channels from normal to anomalous data are significant. This observation suggests that anomalies could be detected by changes in these interdependency graph series. To capitalize on this insight, we introduce MADGA (MTS Anomaly Detection via Graph Alignment), which redefines anomaly detection as a graph alignment (GA) problem that explicitly utilizes interdependencies for anomaly detection. MADGA dynamically transforms subsequences into graphs to capture the evolving interdependencies, and Graph alignment is performed between these graphs, optimizing an alignment plan that minimizes cost, effectively minimizing the distance for normal data and maximizing it for anomalous data. Uniquely, our GA approach involves explicit alignment of both nodes and edges, employing Wasserstein distance for nodes and Gromov-Wasserstein distance for edges. To our knowledge, this is the first application of GA to MTS anomaly detection that explicitly leverages interdependency for this purpose. Extensive experiments on diverse real-world datasets validate the effectiveness of MADGA, demonstrating its capability to detect anomalies and differentiate interdependencies, consistently achieving state-of-the-art across various scenarios.

In this paper, we study the unsupervised plain graph alignment problem, which aims to find node correspondences across two graphs without any side information. The majority of previous works addressed UPGA based on structural information, which will inevitably lead to subgraph isomorphism issues. That is, unaligned nodes could take similar local structural information. To mitigate this issue, we present the Multi-order Matched Neighborhood Consistent (MMNC) which tries to match nodes by aligning the learned node embeddings with only a small number of pseudo alignment seeds. In particular, we extend matched neighborhood consistency (MNC) to vector space and further develop embedding-based MNC (EMNC). By minimizing the EMNC-based loss function, we can utilize the limited pseudo alignment seeds to approximate the orthogonal transformation matrix between two groups of node embeddings with high efficiency and accuracy. Through extensive experiments on public benchmarks, we show that the proposed methods achieve a good balance between alignment accuracy and speed over multiple datasets compared with existing methods.

The current success of entity alignment (EA) is still mainly based on large-scale labeled anchor links. However, the refined annotation of anchor links still consumes a lot of manpower and material resources. As a result, an increasing number of works based on active learning, few-shot learning, or other deep network learning techniques have been developed to address the performance bottleneck caused by a lack of labeled data. These works focus either on the strategy of choosing more informative labeled data or on the strategy of model training, while it remains opaque why existing popular EA models (e.g., GNN-based models) fail the EA task with limited labeled data. To overcome this issue, this paper analyzes the problem of weakly supervised EA from the perspective of model design and proposes a novel weakly supervised learning framework, Position Enhanced Entity Alignment (PEEA). Besides absorbing structural and relational information, PEEA aims to increase the connections between far-away entities and labeled ones by incorporating positional information into the representation learning with a Position Attention Layer (PAL). To fully utilize the limited anchor links, we further introduce a novel position encoding method that considers both anchor links and relational information from a global view. The proposed position encoding will be fed into PEEA as additional entity features. Extensive experiments on public datasets demonstrate the effectiveness of PEEA.

Web-based AR technology has broadened human-computer interaction scenes from traditional mechanical devices and flat screens to the real world, resulting in unconstrained environmental challenges such as complex backgrounds, extreme illumination, depth range differences, and hand-object interaction. The previous hand detection and 3D hand pose estimation methods are usually based on single modality such as RGB or depth data, which are not available in some scenarios in unconstrained environments due to the differences between the two modalities. To address this problem, we propose a multimodal fusion approach, named Scene-Adapt Fusion (SA-Fusion), which can fully utilize the complementarity of RGB and depth modalities in web-based HCI tasks. SA-Fusion can be applied in existing hand detection and 3D hand pose estimation frameworks to boost their performance, and can be further integrated into the prototyping AR system to construct a web-based interactive AR application for unconstrained environments. To evaluate the proposed multimodal fusion method, we conduct two user studies on CUG Hand and DexYCB dataset, to demonstrate its effectiveness in terms of accurately detecting hand and estimating 3D hand pose in unconstrained environments and hand-object interaction.

Artistic portrait drawing (APDrawing) generation has seen progress in recent years. However, due to the naturally high scarcity and artistry, it is difficult to collect large‐scale labeled and paired data and generally divide drawing styles into several specific recognized categories. Existing works suffer from the limited labeled data and naive manual division of drawing styles according to the corresponding artists. They cannot adapt to the actual situations, for example, a single artist might have multiple drawing styles and APDrawings from different artists might share similar styles. In this paper, we propose to use unlabeled and unpaired data and perform the task in an unsupervised manner. Without manual division of drawing styles, we take each portrait drawing as a unique style and introduce self‐supervised feature learning to learn free styles for unlabeled portrait drawings. Besides, we devise a style bank and a decoupled cycle structure to take over two main considerations in the task: generation quality and style control. Extensive experiments show that our model is more adaptable to different style inputs than state‐of‐the‐art methods.

Graph alignment, also known as network alignment, has many applications in data mining tasks. It aims to find the node correspondence across disjoint graphs. With recent representation learning advancements, embedding-based graph alignment has become a hot topic. Existing embedding-based methods focus either on structural proximity across graphs or on the positional proximity within a single graph. However, only considering the structural similarity will make the position relation of nodes not clear enough, which makes it easy to misalign the nodes close in distance, while only considering the position proximity of a single graph will make the node embeddings from different graphs in different subspaces. To mitigate this issue, we propose a novel model CEGA forCross-graphEmbedding-basedGraphAlignment, which can generate node embeddings to reflect structural proximity and positional proximity simultaneously. Meanwhile, we make the proximity trainable thus it can be learned to best suit the alignment task at hand automatically. We show that CEGA outperforms existing graph alignment methods in accuracy under unsupervised scenarios through extensive experiments on public benchmarks.

Multimedia retrieval models need the ability to extract useful information from large-scale data for clients. As an important part of multimedia retrieval, image classification model directly affects the efficiency and effect of multimedia retrieval. We need a lot of data to train a image classification model applied to multimedia retrieval task. However, with the protection of data privacy, the data used to train the model often needs to be kept on the client side. Federated learning is proposed to use data from all clients to train one model while protecting privacy. When federated learning is applied, the distribution of data across different clients varies greatly. Disregarding this problem yields a final model with unstable performance. To enable federated learning to work dependably in the real world with complex data environments, we propose FedNKD, which utilizes knowledge distillation and random noise. The superior knowledge of each client is distilled into a central server to mitigate the instablity caused by Non-IID data. Importantly, a synthetic dataset is created by some random noise through back propagation of neural networks. The synthetic dataset will contain the abstract features of the real data. Then we will use this synthetic dataset to realize the knowledge distillation while protecting users' privacy. In our experimental scenarios, FedNKD outperforms existing representative algorithms by about 1.5% in accuracy.

Response generation is a fundamental function in conversational systems, where controllability of the response is a key problem. In this paper, we consider how to control the response by lexical constraints, namely lexically constrained response generation. The stochastic search-based methods have achieved promising performance in satisfying lexical constraints. The idea of these methods is modifying a sentence through the actions of insertion, deletion and replacement guided by an optimization algorithm. The core of our method is modifying the response by incorporating the lexical constraints and preserving the message-related parts. For this purpose, we propose the novel Parallel Decoders to guide response modification. The first decoder generates responses according to the given message and constraints. The second decoder calculates the relevance between the input and the response. Based on Parallel Decoders, during the modification, we could sample the positions in the response for editing according to the relevance score. Experiments show the proposed framework achieves better performance than the state-of-the-art generation models in terms of constraint relevance, sentence fluency, response diversity and human evaluation.

Logs play an important part in analyzing system behavior and diagnosing system failures. As the basic step of log analysis, log parsing converts raw log messages into structured log templates. However, existing log parsing approaches are not adaptive and versatile enough to ensure their high accuracy on all types of datasets. In particular, it is required to design regular expressions or fine-tune the hyper-parameters manually for the best performance. In this paper, we propose Prefix-Graph, an online versatile log parsing approach. Prefix-Graph is a probabilistic graph structure extended from prefix tree. It iteratively merges together two branches which have high similarity in probability distribution, and represents log templates as the combination of cut-edges in root-to-leaf paths of the graph. Since no domain knowledge is used and all the parameters are fixed, Prefix-Graph can be easily applied to different log datasets without any additional manual work. We evaluate our approach on 10 real-world datasets and 117GB log messages obtained from Huawei. The experimental results demonstrate that Prefix-Graph achieves the highest average accuracy of 0.975 and the smallest standard deviation of 0.037. Our approach is superior to baseline methods in terms of adaptability and versatility.

Given a long text, the summarization system aims to obtain a shorter highlight while keeping important information on the original text. For customer service, the summaries of most dialogues between an agent and a user focus on several fixed key points, such as user's question, user's purpose, the agent's solution, and so on. Traditional extractive methods are difficult to extract all predefined key points exactly. Furthermore, there is a lack of large-scale and high-quality extractive summarization datasets containing key points. In order to solve the above challenges, we propose a Distant Supervision based Machine Reading Comprehension model for extractive Summarization (DSMRC-S). DSMRC-S transforms the summarization task into the machine reading comprehension problem, to fetch key points from the original text exactly according to the predefined questions. In addition, a distant supervision method is proposed to alleviate the lack of eligible extractive summarization datasets. We conduct experiments on a large-scale summarization dataset collected in customer service scenarios, and the results show that the proposed DSMRC-S outperforms the strong baseline methods by 4 points on ROUGE-L.

Word embedding is a basic task in natural language processing area. Unlike English, Chinese subword units, such as characters, radicals, and components, contain rich semantic information which can be used to enhance word embeddings. However, existing methods neglect the semantic contribution of corresponding subword units to the word. In this work, we employ attention mechanism to capture the semantic structure of Chinese words and propose a novel framework, named Attention-based multi-Layer Word Embedding model(ALWE). We also design an asynchronous strategy for updating embedding and attention efficiently. Our model learns to share subword information between distinct words selectively and adaptively. Experimental results on the word similarity, word analogy, and text classification show that the proposed model outperforms all baselines, especially when words don't appear frequently. Qualitative analysis further demonstrates the superiority of ALWE.

The rapid development of artificial intelligence in multiple scenarios, including machine learning, image recognition, and autonomous driving, has led to an explosion of computation jobs. These jobs are often divided into parallel child tasks and executed in distributed clusters with limited computing resources, making parallel task scheduling one of the most important research topics nowadays. Most studies about parallel task scheduling focused on formulating special scenarios and service requirements as optimization problems. However, the complicated and dynamic parallel computing environments are hard to model, predict and control, making those previous methods unscalable and unable to reflect the real scenarios. In this paper, a Multi-task Deep reinforcement learning approach for scalable parallel Task Scheduling (MDTS) is firstly devised. Generally, Deep Reinforcement Learning (DRL) is a model-free optimization algorithm for long-term control by leveraging experience, but it suffers the curse of dimensionality for decision when coping with complex parallel computing environments and jobs with diverse properties. We extend the action selection in DRL to a multi-task decision, where the output branches of multitask learning are fine-matched to parallel scheduling tasks. Child tasks of a job are accordingly assigned to distributed nodes without any human knowledge while the resource competition among parallel tasks is leveraged through shared neural network layers. Extensive experiments show that the MDTS significantly reduces the job execution time compared with least-connection scheduling and particle swarm optimization algorithm by 15.3% and 39.8% respectively. Moreover, MDTS outperforms the raw DRL algorithm on job execution time, load imbalance value, and total cost by 42.8%, 47.5%, and 59.0%.

Obtaining insight into user mobility for next point-of-interest (POI) recommendations is a vital yet challenging task in location-based social networking. Information is needed not only to estimate user preferences but to leverage sequence relationships from user check-ins. Existing approaches to understanding user mobility gloss over the check-in sequence, making it difficult to capture the subtle POI-POI connections and distinguish relevant check-ins from the irrelevant. We created a geographically-temporally awareness hierarchical attention network (GT-HAN) to resolve those issues. GT-HAN contains an extended attention network that uses a theory of geographical influence to simultaneously uncover the overall sequence dependence and the subtle POI-POI relationships. We show that the mining of subtle POI-POI relationships significantly improves the quality of next POI recommendations. A context-specific co-attention network was designed to learn changing user preferences by adaptively selecting relevant check-in activities from check-in histories, which enabled GT-HAN to distinguish degrees of user preference for different check-ins. Tests using two large-scale datasets (obtained from Foursquare and Gowalla) demonstrated the superiority of GT-HAN over existing approaches and achieved excellent results.

This article describes the capability of online data storage which has been enhanced by the emergence of cloud datacenter development. Distributed Hash Table (DHT) based image retrieval system using locality sensitive hash (LSH) has provided an efficient way to set up distributed Content Based Image Retrieval (CBIR) frameworks. However, with the fixed LSH function adopted, LSH and other codebook-based distributed retrieval systems are facing the problem of flexibility, and also are difficult to satisfy the user's demand. In this article, LRFMIR is proposed to introduce semantic search into DHT based CBIR system. LRFMIR is established on a DHT based network, where a flexible result truncating strategy is employed to fuse provided results by using multiple features measurements. Experiments show that LRFMIR provides a higher accuracy and recall rate than single feature employed retrieval systems, and possesses good load balancing and query efficiency performance.