Machine unlearning offers a practical technical means for fulfilling users' requests to remove personally identifiable information (PII) under ''right to be forgotten'' regulations such as GDPR and COPPA. Traditionally, unlearning is performed with the removal of entire data samples (sample unlearning) or whole features across the dataset (feature unlearning). However, when the removal request targets only certain parts of the PII, such as specific objects within a sample, these traditional unlearning approaches fall short of meeting such finer-grained unlearning requirements. To address this gap, we propose a scene graph-based object unlearning framework. This framework utilizes scene graphs, rich in semantic representation, transparently translate unlearning requests into actionable steps. The result, is the preservation of the overall semantic integrity of the generated image, bar the unlearned object. Furthermore, we develop three distinct approaches for object unlearning, grounded in the mainstream unlearning techniques of fine-tuning and model redaction. For validation, we evaluate the unlearned object's fidelity in outputs under the tasks of image reconstruction and image synthesis. Our proposed framework demonstrates improved object unlearning outcomes, with the preservation of unrequested samples in contrast to sample and feature learning methods. This work addresses critical privacy issues by increasing the granularity of targeted machine unlearning through forgetting specific object-level details without sacrificing the utility of the whole data sample or dataset feature.

Improving the performance of data systems for join operations has long been a critical challenge. Recently, substantial attention has been focused on optimizing multi-way join performance, particularly in reducing the overhead caused by generating intermediate tuples that do not contribute to the final result. In this paper, we propose a novel algorithm called SieveJoin, which extends the established Bloomjoin approach to support multi-way joins. SieveJoin sets a new benchmark for the efficiency of join query execution. A key innovation of SieveJoin is its ability to propagate Bloom filters along the join path, allowing the system to terminate early and avoid producing superfluous intermediate results. The primary design objective of SieveJoin is to efficiently estimate join results using Bloom filters, while maintaining minimal memory overhead. We analyze the bottlenecks associated with deferred multi-way joins and detail how Bloom filters are utilized to suppress the creation of redundant intermediate tuples. To assess the effectiveness of SieveJoin, we conduct a comprehensive experimental evaluation using the TPC-H benchmark, citation datasets, and a synthetic dataset. Our results compare SieveJoin with a state-of-the-art column-store database and a worst-case optimal join algorithm, highlighting its advantages in both response time and memory usage.

Inductive knowledge graph completion (KGC) aims to represent unseen entities and complete triplets in emerging knowledge graphs (KGs), while the existing studies ignore that unseen elements combined with seen ones constitute a holistic new relational graph, where emerging KGs have inescapable impacts backtracking to original ones. Therefore, it is not only necessary to predict triplets in emerging KGs, but also with particular significance to further improve the completeness of original ones, considering the semantic and topological variations in the holistic new graph. To fill in this gap, we formulate a newIT(Inductive-Transductive) KGC task to transductively complete triplets inside original KGs after entities in the emerging scenario are represented and fine-tuned in an inductive manner. In order to handle this task, a novel model entitled StaR (Self-adaptive Retroaction-awareRepresentation) is proposed consisting of the following two modules: 1) a self-adaptive semantic encoding network is designed to adaptively adjust embeddings of seen entities to their surrounding semantic mutations; 2) a relation-aware transformer layer is developed to represent both seen and unseen entities in a unified representation space and generalize evolving reasoning paradigms to the whole graph. Our experimental results demonstrate that, compared with state-of-the-art methods, StaR is not only competitive in inductive KGC for unseen entities, but also ulteriorly improves the completeness of original parts inside the holistic new relational graph in ourITKGC task.

Temporal knowledge graphs (TKGs) effectively capture the dynamic evolution of events over time, emerging as a critical driving force in the advancement of artificial intelligence. In recent years, temporal knowledge graph reasoning (TKGR) has garnered significant attention for its ability to address the intrinsic incompleteness of TKGs. Among various TKGR methods, reinforcement learning (RL)-based multi-hop reasoning stands out due to the decision-making capabilities and interpretability. However, existing multi-hop reasoning methods are predominantly designed for the transductive setting where test entities are observed during training, and they exhibit limited performance in the fully-inductive setting where training and test entities are entirely disjoint. Moreover, the sparse links of newly emerged unseen entities in TKGs hinder multi-hop reasoning methods from utilizing sufficient actions to construct multihop relational paths, ultimately impairing reasoning accuracy. To address these challenges, we propose ARLIE (Adaptive Reinforcement Learning with Inductive Embeddings), a novel method capable of conducting multi-hop reasoning in both fully-inductive and transductive settings over TKGs. Specifically, ARLIE consists of the following two key components. (1) A context-based inductive representation method generates finegrained embeddings for unseen entities by exploiting queryrelated contextual information. (2) After obtaining temporal evolution and semantic dependencies of unseen entities, an actionaugmented adaptive RL framework leverages diverse actions to infer missing elements step-by-step over TKGs. Finally, experimental results show that ARLIE surpasses state-of-the-art TKGR methods across both fully-inductive and transductive settings.

Trajectory anomaly detection, as a fundamental operation for moving object pattern discovery, plays an irreplaceable and critical role in spatio-temporal location-based services. Conducting online detection based on the current positions and their contextual semantics can significantly enhance the value of trajectory data. However, existing approaches suffer from two fundamental limitations: 1) treat trajectories as indivisible sequences or apply rigid segmentation strategies, and 2) use of a single detection model that struggles to adapt to concept drift caused by evolving trajectory distributions. Such limitations make it impossible to detect abnormal trajectories in a timely and semantically comprehensive manner. To fill this gap, we propose Stamp, a novel framework for Semantic-aware sub-Trajectory Anomaly detection with a diffusion Multi-model Pool. In particular, Stamp comprises three key innovations: 1. It employs a semantic-driven dynamic segmentation mechanism that identifies natural breakpoints in trajectories based on changes in road semantics, rather than fixed rules. 2. It enhances trajectory representation by embedding road network semantic vectors, capturing both spatial geometry and functional urban characteristics. 3. It employs a pool of diffusion models that dynamically evolves through reliability assessment, similarity measurement, and strategic merging operations, ensuring adaptability to concept drift while leveraging the superior generative capabilities of diffusion models over traditional autoencoders. Experimental results demonstrate that Stamp improves detection efficiency by 35%, AUPR by 5.6%, and F1-score by 2.7% on two large-scale real-world urban trajectory datasets when compared to state-of-the-art methods, demonstrating its effectiveness for real-time anomaly detection in complex urban environments.

Zero-shot Anomaly Detection (ZSAD) is an emerging task in industrial settings. It aims to detect anomalies in a target dataset without training samples, which is crucial for sample scarcity and data privacy. Existing methods largely rely on CLIP, leveraging its internal knowledge to detect anomalies. However, due to its pre-training on natural image-text pairs, CLIP suffers from domain shift, favoring global semantics over fine-grained defect detection in industrial images. Furthermore, most existing methods employ fixed text prompt to guide the model, which is difficult to describe diverse and unseen anomalies, leading to poor accuracy. To address these limitations, we propose a Detail-guided Dual-modal Enhancement Model (DDE-CLIP) for the ZSAD task. Firstly, we designed the Detail Feature Reinforcement Module (DFRM) to capture local representations of minute defects. Its specialized design effectively enhances the model's perception of fine-grained anomalies and enables the pre-trained CLIP model to better adapt to the unique visual characteristics of industrial images. Subsequently, we introduced the Visual-guided Text Refinement Module (VTRM), which can dynamically optimize text prompts based on the input image's visual content (particularly the detail features captured by DFRM). This ensures the accurate reflection of text prompts on specific semantics of various defects, thereby significantly enhancing the alignment between vision and text for unseen anomalies. Overall, our DDE-CLIP uses detail features to enhance both image and text modalities, effectively addressing the challenges of ZSAD. Extensive experiments on 7 real-world industrial product datasets demonstrate that DDE-CLIP exhibits superior detection and localization capabilities compared to other methods. The code is available at https://github.com/zhushengxinyue/DDE-CLIP.

Cardinality estimation has long been crucial for cost-based database optimizers in identifying optimal query execution plans, attracting significant attention over the past decades. While recent advancements have significantly improved the accuracy of multi-table join query estimations, these methods introduce challenges such as higher space overhead, increased latency, and greater complexity, especially when integrated with the binary join framework. In this paper, we introduce a novel cardinality estimation method named TKHist, which addresses these challenges by relaxing the uniformity assumption in histograms. TKHist captures bin-wise non-uniformity information, enabling accurate cardinality estimation for join queries without filter predicates. Furthermore, we explore the attribute independent assumption, which can lead to significant over-estimation rather than under-estimation in multi-table join queries. To address this issue, we propose the dominating join path correlation discovery algorithm to highlight and manage correlations between join keys and filter predicates. Our extensive experiments on popular benchmarks demonstrate that TKHist reduces error variance by 2-3 orders of magnitude compared to SOTA methods, while maintaining comparable or lower memory usage.

Data isolation poses a significant challenge to efficient big data query processing, as data providers are often reluctant to share their raw data due to security concerns. Current federated query systems address this issue by employing Secure Multi-Party Computation (SMC) and Differential Privacy (DP) to facilitate secure and collaborative computation. However, these privacy-preserving methods rely on cryptographic protocols, which introduce substantial computational overhead, slowing query processing by up to 1,000 times compared to plaintext queries. While sampling methods have been explored to enhance federated query systems, they frequently fail to strike a balance between accuracy and speed. To address the limitations above, we propose a secure federated approximate query system based on a deep classifier (SAQDC). This system utilizes deep learning techniques to accelerate query processing while integrating SMC and Differential Privacy to achieve an optimal balance between privacy and efficiency by allowing each data provider to train classifiers using Multi-Layer Perceptron (MLP) and Deep Set architectures, which predict query relative errors across different modules.Based on the prediction errors generated by the classifier, queries are assigned to the most appropriate approximate query model and the differential privacy parameters are adjusted to enhance query accuracy. This approach enhances query speed, preserves accuracy, and effectively mitigates malicious differential privacy attacks. We demonstrate SAQDC's superior performance through extensive experiments on three large-scale datasets.

Trajectory similarity computation is a fundamental functionality in trajectory data mining, with wide-ranging applications in location-based services. Position embedding, which transforms GPS points into embedding vectors, plays a critical role in learning-based trajectory similarity models. The quality of these embeddings significantly impacts the performance of the models on downstream tasks. Existing methods fail to satisfy all good properties, i.e., global, continuous, unique, and dynamic, thereby limiting the development of trajectory similarity computation in both local and global scenarios. Inspired by linear counting systems, such as the decimal system, we first propose the łambda-base circular system to embed positions on the circle, then introduce the multi-base global embedding method GPE to encode global positions into vectors. Experiments conducted on five real-world datasets with nine baseline methods demonstrate that the GPE achieves state-of-the-art performance across four key evaluations in downstream tasks.

With the increasing prevalence of Web-based platforms handling vast amounts of user data, machine unlearning has emerged as a crucial mechanism to uphold users' right to be forgotten, enabling individuals to request the removal of their specified data from trained models. However, the auditing of machine unlearning processes remains significantly underexplored. Although some existing methods offer unlearning auditing by leveraging backdoors, these backdoor-based approaches are inefficient and impractical, as they necessitate involvement in the initial model training process to embed the backdoors. In this paper, we propose a TAilored Posterior diffErence (TAPE) method to provide unlearning auditing independently of original model training. We observe that the process of machine unlearning inherently introduces changes in the model, which contains information related to the erased data. TAPE leverages unlearning model differences to assess how much information has been removed through the unlearning operation. Firstly, TAPE mimics the unlearned posterior differences by quickly building unlearned shadow models based on first-order influence estimation. Secondly, we train a Reconstructor model to extract and evaluate the private information of the unlearned posterior differences to audit unlearning. Existing privacy reconstructing methods based on posterior differences are only feasible for model updates of a single sample. To enable the reconstruction effective for multi-sample unlearning requests, we propose two strategies, unlearned data perturbation and unlearned influence-based division, to augment the posterior difference. Extensive experimental results indicate the significant superiority of TAPE over the state-of-the-art unlearning verification methods, at least 4.5x efficiency speedup and supporting the auditing for broader unlearning scenarios.

Multi-Modal Knowledge Graphs (MMKGs), comprising relational triples and related multi-modal data (e.g., text and images), usually suffer from the problems of low coverage and incompleteness. To mitigate this, existing studies introduce a fundamental MMKG fusion task, i.e., Multi-Modal Entity Alignment (MMEA) that identifies equivalent entities across multiple MMKGs. Despite MMEA's significant advancements, effectively integrating MMKGs remains challenging, mainly stemming from two core limitations: 1) entity ambiguity, where real-world entities across different MMKGs may possess multiple corresponding counterparts or alternative identities; and 2) severe noise within multi-modal data. To tackle these limitations, a new task MMER (Multi-Modal Entity Resolution), which expands the scope of MMEA to encompass entity ambiguity, is introduced. To tackle this task effectively, we develop a novel model ADMH-ER (Adaptive Denoising Multi-modal Hybrid for Entity Resolution) that incorporates several crucial modules: 1) multi-modal knowledge encoders, which are crafted to obtain entity representations based on multi-modal data sources; 2) an adaptive denoising multi-modal hybrid module that is designed to tackle challenges including noise interference, multi-modal heterogeneity, and semantic irrelevance across modalities; and 3) a hierarchical multi-objective learning strategy, which is proposed to ensure diverse convergence capabilities among different learning objectives. Experimental results demonstrate that ADMH-ER outperforms state-of-the-art methods.

With the widespread use of GPS devices and the advancement of location-based services, a vast amount of trajectory data has been collected and mined for various applications. Trajectory clustering, which categorizes trajectories into distinct groups, is the fundamental functionality of trajectory data mining. The challenge is how to cluster on a mass of trajectory data efficiently and universally with satisfying results. The raw trajectory clustering algorithms are universal, but trapped in the dilemma between efficiency and desirable results. Other approaches, such as density-based, road network-based, and deep learning-based algorithms, encounter issues like high time complexity, loss of trajectory integrity, reliance on road networks, and data quality during training. To tackle these challenges, we first propose the efficient KMCT (k-Means Clustering of Trajectories) algorithm based on a semantic interpolation transformation to cluster raw trajectories and achieve satisfying results. Additionally, we introduce the DA-KMCT (Density Accelerated k-Means Clustering of Trajectories) algorithm to further boost the clustering process based on trajectory densities and an optimized centroid selecting strategy. Moreover, we present a novel clustering evaluation method called IOD, which efficiently estimates clustering results on large-scale datasets with linear time complexity. Experimental results on real-world datasets demonstrate that KMCT and DA-KMCT outperform five related methods in terms of clustering quality and time efficiency, and the proposed IOD evaluation shows a strong correlation with the Silhouette Coefficient, offering a reliable and efficient alternative for evaluating clustering results.

The automatic generation of radiological imaging reports aims to produce accurate and coherent clinical descriptions based on X-ray images. This facilitates clinicians in completing the arduous task of report writing and advances clinical automation. The primary challenge in radiological imaging report generation lies in accurately capturing and describing abnormal regions in the images under data bias conditions, resulting in the generation of lengthy texts containing image details. Existing methods mostly rely on prior knowledge such as medical knowledge graphs, corpora, and image databases to assist models in generating more precise textual descriptions. However, these methods still struggle to identify rare anomalies in the images. To address this issue, we propose a two-stage training model, named CLR2G, based on cross-modal contrastive learning. This model delegates the task of capturing anomalies, particularly those challenging for the generative model trained with cross-entropy loss under data bias conditions, to a specialized abnormality capture component. Specifically, we employ a semantic matching loss function to train additional abnormal image and text encoders through cross-modal contrastive learning, facilitating the capture of 13 common anomalies. We utilize the anomalous image features, text features and their confidence probabilities as a posteriori knowledge to help the model generate accurate image reports. Experimental results demonstrate the state-of-the-art performance of our method on two widely used public datasets, IU-Xray and MIMIC-CXR.

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines.

Extracting entities and relations from text is a significant task of information extraction. Existing extraction models often straightforwardly produce their confident prediction results without any reconsideration or double-checking, resulting in avoidable mistakes and sub-optimal performance. In this paper, we propose a novel coarse-to-fine extraction framework, which first extracts high-potential relations as well as entities via knowledge distillation, and then rechecks the predictions via handcrafted natural language inference (NLI) task in a fine-grained manner. Specifically, based on the knowledge distillation mechanism, we train multiple teacher models iteratively through an adaptive loss function for making one teacher concentrate more on the data that others are incompetent for. Then, these complementary teacher models are utilized to provide valuable soft-label information for training a considerate student model, enabling it to generate reliable preliminary predictions. Further, these generated potential relations and entities are formulated as hypotheses, together with the original sentences as premises, serving as the input for an NLI model. Considering the linguistic diversity of relational expression, we automatically generate various semantic templates for hypotheses through an$\mathcal{N}$-gram mining strategy. Moreover, due to the existence of multi-fact sentences, a relation-guided Gaussian attention is designed to reduce the gap between the single-relation hypothesis and the multi-relation premise. To implement efficient training, we also develop several ways to generate high-quality negative samples, which help the NLI model learn to identify errors. Experimental results show that the proposed method is effective and outperforms other strong baselines on public benchmarks.

Document-level Relation Extraction (RE) is a promising task aiming at identifying relations of multiple entity pairs in a document. Compared with the sentence-level counterpart, it has raised two significant challenges: a) In most cases, a relational fact can be adequately expressed via a small subset of sentences from the document, namely evidence. But the traditional method cannot model such strong semantic correlations between evidence sentences that collaborate to describe a specific relation; b) The data of this task is extremely long-tail in terms of too many NA instances and imbalanced relational types. Such data can mislead the tail prediction bias to the head categories in the RE model. In this paper, we present a novelEvidence reasoning andCurriculum learning method forDocRE(DRE-EC) to address these challenges. Particularly, we first formulate evidence extraction as a sequential decision problem through a crafted reinforcement learning mechanism with an efficient path searching strategy to reduce the action space. Providing the evidence for each entity pair as a customized-filtered document in advance helps infer the relations better. To address the long-tail issue, we further develop a hybrid curriculum learning method at the NA-level (NC) and relation-level (RC) with our customized difficulty measure score. In NC, the NA samples are scheduled in an easy-to-hard scheme and gradually added, resulting in the data distribution from ideal and balanced to real and unbalanced. In RC, the scheme is switched into hard-to-easy to enhance the hard and tail samples. In addition, we propose a new Equalization adaptive Focal Loss(EFLoss) that can adjust to the changing data distribution and focus more on the tail categories. We conduct various experiments on two document-level RE benchmarks and achieve a remarkable improvement over previous competitive baselines. Furthermore, we provide detailed analyses of the advantages and effectiveness of our method.

Map matching is a problem to align recorded location data to a digital map. It has been well studied to map GPS data collected from vehicles to paths in a road network. The problem of Cellular Trajectory Map-Matching (CTMM) is a new problem that deals with trajectories of cellular-based positioning data. It has a wide range of applications, for example, for telecommunication companies to understand and predict traffic information based on telecom tokens obtained from vehicles. CTMM is a significantly more challenging task that faces much lower data precision and higher positioning errors. While Hidden Markov Model (HMM) based methods can achieve satisfactory results for GPS-based map matching, we show that they cannot be directly applied to the CTMM problem. In this paper, we aim at reducing the impact of positioning errors by incorporating knowledge obtained by neural networks into learned probabilities. A multi-relational graph learning method is developed to generate meaningful embedding, with multi-relational useful information fully preserved in a shared space. An attentive neural network is then designed as the learner for observation probability, incorporating the knowledge of the dynamic correlation between roads and cell towers under varying trajectory contexts. A transition probability learner is used to capture implicit deep features for enhanced transition probability modeling. Finally, the learned observation and transition probabilities are seamlessly integrated into HMM to guide more accurate path-finding. Extensive experiments on two large-scale cellular datasets reveal that our approach achieves high accuracy and robustness on CTMM.

Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the given documents in another language. CLS has attracted wide research attention due to its practical significance in the multi-lingual world. Though great contributions have been made, existing CLS works typically focus on short documents, such as news and guides. Different from these short texts, long documents such as academic articles usually discuss complicated subjects and consist of thousands of words, making them non-trivial to process and summarize. To promote CLS research on long documents, we construct Perseus, the first long-document CLS dataset which collects about 94K Chinese scientific documents paired with English summaries. The average length of documents in Perseus is more than 2000 tokens. As a preliminary study on long-document CLS, we build and evaluate various CLS baselines, including pipeline and end-to-end methods. Experimental results on Perseus show the superiority of the end-to-end baseline, which performs the best among all methods. Furthermore, to provide a deeper understanding, we manually analyze the model outputs and discuss specific challenges faced by current approaches. We hope that our work could benchmark long-document CLS and benefit future studies.

In this paper, we study the problem of on-demand food delivery in a new setting where two groups of workers -- riders and taxi drivers (drivers for short) -- cooperate with each other for better service. The riders are responsible for the first and the last mile, and the drivers are in charge of the cross-community transportation. We show this problem is generally NP-hard by a reduction from the well-known 3-dimensional matching (3DM). To tackle with this problem, we first reduce it to the maximum independent set problem and use a simple greedy strategy to design an approximate algorithm which has a polynomial time. Considering the exponents in the polynomial are not very small, we then transform the 3DM into two rounds of 2-dimensional matching and propose a fast algorithm to solve it. Though 3DM problem is NP-hard, we find the cooperation between riders and drivers form a special tripartite graph, based on which we construct a flow network and employ the min-cost max-flow algorithm to efficiently compute the exact solution. We conduct extensive experiments to show the efficiency and the effectiveness of our proposed algorithms.

Document-level Relation Extraction (RE) is a promising task aiming at identifying relations of multiple entity pairs in a document. However, in most cases, a relational fact can be expressed enough via a small subset of sentences from the document, namely evidence sentence. Moreover, there often exist strong semantic correlations between evidence sentences that collaborate together to describe a specific relation. To address these challenges, we propose a novel evidence-aware model for document-level RE. Particularly, we formulate evidence sentence selection as a sequential decision problem through a crafted reinforcement learning mechanism. Considering the explosive search space of our agent, an efficient path searching strategy is executed on the converted document graph to heuristically obtain hopeful sentences and feed them to reinforcement learning. Finally, each entity pair owns a customized-filtered document for further inferring the relation between them. We conduct various experiments on two document-level RE benchmarks and achieve a remarkable improvement over previous competitive baselines, verifying the effectiveness of our method.

Trajectory prediction is a fundamental problem for a wide spectrum of location-based applications. Existing methods can achieve inspiring results in predicting personal frequent routes conditioned on massive historical data. However, trajectory estimation may involve cold-start routes or users due to the data sparsity problem, which severely limits the performance of spatial trajectory prediction. Although meta-learning models can alleviate the cold-start problem, they simply utilize the same initialization for all tasks and thus cannot fit each user well due to users' varying travel preferences. To this end, we propose an adaptive meta-optimized model called MetaPTP for personalized spatial trajectory prediction. Specifically, it adopts a soft-clustering based method to guide the network initialization in a finer granularity, so that shared knowledge can be better transferred across users with similar travel preferences. Besides, towards model fine-tuning, an effective trajectory sampling method is introduced to generate meaningful support set, which simultaneously considers user preference and spatial trace similarities to provide task-related information for model adaptation. In addition, we design a weight generator to adaptively assign reasonable weights to trajectories in support set to avoid sub-optimal results which will occur when fine-tuning the initial network with the same weight for trajectories with different user preferences and spatial distributions. Finally, extensive experiments on two real-world datasets demonstrate the superiority of our model.

Sports game summarization aims at generating sports news from live commentaries. However, existing datasets are all constructed through automated collection and cleaning processes, resulting in a lot of noise. Besides, current works neglect the knowledge gap between live commentaries and sports news, which limits the performance of sports game summarization. In this paper, we introduce K-SportsSum, a new dataset with two characteristics: (1) K-SportsSum collects a large amount of data from massive games. It has 7,854 commentary-news pairs. To improve the quality, K-SportsSum employs a manual cleaning process; (2) Different from existing datasets, to narrow the knowledge gap, K-SportsSum further provides a large-scale knowledge corpus that contains the information of 523 sports teams and 14,724 sports players. Additionally, we also introduce a knowledge-enhanced summarizer that utilizes both live commentaries and the knowledge to generate sports news. Extensive experiments on K-SportsSum and SportsSum datasets show that our model achieves new state-of-the-art performances. Qualitative analysis and human study further verify that our model generates more informative sports news.

Massive trajectory data stem from the prevalence of equipment-supporting GPS and wireless communication technology. Especially, activity trajectory from Location-based Social Network (LBSN) endows traditional trajectory data with additional user semantic activities, e.g., visiting work/home/entertainment places. Measuring the similarity between activity trajectories is to compare their proximity in multiple dimensions such as time, location, and semantics. In this way, we can mine implicit user preference and apply it to route planning, POI recommendation or any other online tasks. The key challenge of comparing activity trajectories (i.e., computing their similarity) lies in two aspects. One is the uneven sampling rate in both time and space. The other is the discrepancy of individual activities. Previous effort alleviates the issue of uneven sampling rate via trajectory complements, which is limited to spatial-temporal information. In this paper, we propose to learn a representation for one activity trajectory by jointly considering the spatio-temporal characteristics and the activity semantics. The similarity of two trajectories is computed by weighting individual trajectory points and contextual features with multi-level attention mechanisms. In specific, we propose a point-level and feature-level attention mechanism to adaptively select critical elements and contextual factors for learning trajectory representation. Our proposed approach, called At2vec, demonstrates better performance than existing baselines in extensive experimental evaluation on real trajectory databases.

Knowledge graph (KG) embedding aims to encode both entities and relations into a continuous vector space. Most existing methods require that all entities should be observed during training while ignoring the evolving nature of KG. Major recent efforts on this issue embed new entities by aggregating neighborhood information from existing entities and relations with Graph Neural Network (GNN). However, these methods rely on the neighbors seen during training and suffer from the embedding of new entities with insufficient triplets or triplets with the unseen-to-unseen form. To relieve this problem, we propose a two-stage learning model referred as Hyper-Relation Feature Learning Network (HRFN) for effective out-of-knowledge-base embedding. For the first stage, HRFN learns pre-representations for emerging entities using hyper-relation features meta-learned from the training set. A novel feature aggregating network that involves an entity-centered Graph Convolutional Network (GCN) and a relation-centered GCN is proposed to aggregate information from both new entities themselves and their neighbors. For stage two, a transductive learning network is employed to learn finer-grained embeddings based on above-mentioned pre-representations of new entities. Experimental results on the link prediction task demonstrate the superiority of our model. Further analysis is also done to validate the effectiveness and efficiency of pre-representing emerging entities with the hyper-relation feature.

Knowledge graph (KG) reasoning is a significant method for KG completion. To enhance the explainability of KG reasoning, some studies adopt reinforcement learning (RL) to complete the multi-hop reasoning. However, RL-based reasoning methods are severely limited by few-shot relations (only contain few triplets). To tackle the problem, recent studies introduce meta-learning into RL-based methods to improve reasoning performance. However, the generalization abilities of their models are limited due to the problem of low reasoning accuracies over hard relations (e.g., language and title). To overcome this problem, we propose a novel model called THML (Two-level Hardness-aware Meta-reinforcement Learning). Specifically, the model contains the following two components: (1) A hardness-aware meta-reinforcement learning method is proposed to predict the missing element by training hardness-aware batches. (2) A two-level hardness-aware sampling is proposed to effectively generate new hardness-aware batches from relation level and relation-cluster level. The generalization ability of our model is significantly improved by repeating the process of these two components in an alternate way. The experimental results demonstrate that THML notably outperforms the state-of-the-art approaches in few-shot scenarios.

Crowdsensing Data Trading (CDT), through which a platform can aggregate some data collected by a group of mobile users with sensing devices (a.k.a., data sellers) and sell the corresponding statistics to data consumers, has been recognized as a promising paradigm for large-scale data trading in recent years. It is critical to select sellers with high sensing qualities and maximize all trading participants' profits simultaneously. However, most existing CDT systems either assume that sellers' sensing qualities are known in advance or cannot realize concurrent profit maximization. In this paper, we propose a data trading mechanism based on Combinatorial Multi-Armed Bandit and three-stage Hierarchical Stackelberg game, called CMAB-HS, to tackle the problem of quality unknown seller selection and incentive strategy design. Our objective is to select a group of sellers to maximize the total sensing quality within time budget, and determine the optimal incentive strategy for each participant to maximize individual profit simultaneously. We theoretically prove that CMAB-HS achieves Stackelberg Equilibrium and a tight bound on regret. Additionally, we demonstrate its significant performances through extensive simulations on real data traces.

Travel time estimation has been recognized as an important research topic that can find broad applications. Existing approaches aim to explore mobility patterns via trajectory embedding for travel time estimation. Though state-of-the-art methods utilize estimated traffic condition (by explicit features such as average traffic speed) for auxiliary supervision of travel time estimation, they fail to model their mutual influence and result in inaccuracy accordingly. To this end, in this article, we propose an improved traffic-aware model, called TAML, which adopts a multi-task learning network to integrate a travel time estimator and a traffic estimator in a shared space and improves the accuracy of estimation by enhanced representation of traffic condition, such that more meaningful implicit features are fully captured. In TAML, multi-task learning is further applied for travel time estimation in multi-granularities (including road segment, sub-path, and entire path). The multiple loss functions are combined by considering the homoscedastic uncertainty of each task. Extensive experiments on two real trajectory datasets demonstrate the effectiveness of our proposed methods.

Data incompleteness is a common data quality problem in databases. Recent work proposes to retrieve missing string values from the World Wide Web for higher imputation recall, but on the other hand, takes the risk of introducing web noises into the imputation results. So far there lacks an effective way to control the quality of web-based data imputation, given the complexity of the quality model and lacking of enough ground truth data. In this article, an EM-based quality model is first built for web-based data imputation which investigates three key factors jointly, i.e., precision of web sources, correlation among web sources, and precision and recall of the employed extractors. However, the accuracy of the EM-based quality model could be harmed when the EM (Expectation Maximization) assumption that “the majority agree on the truth” does not hold in some cases. To solve this problem, we introduce crowd intervention to help improve the quality model. While a straightforward but expensive way is to let the crowd to identify all these undesirable cases and provide the right imputation values for these blanks, a most crowd-economic way is to select a small set of blanks for crowd-based imputation, whose results could help to adjust the EM-based quality model towards a better one. To achieve this, an adaptive blank selection strategy is proposed to select a sequence of blanks for crowd-based imputation. Also, we work on finding a proper time to stop further crowd intervention for the balance of crowd efficiency and quality improvement. Our experiments performed on three real world and one simulated data collections prove that the proposed quality model can effectively help improve the quality of the web-based imputation results by more than 15 percent, while our crowd cost saving strategy saves more than 75 percent crowd cost.

Abstract Online group buying is a burgeoning business model of Internet shopping, in which people with the same merchandise interests form a group and co-purchase goods with favorable prices. The buyer who launches the co-purchase is called the initiator, and other buyers are called the co-purchasers. Although recommending co-purchasers for a target buyer (co-purchase initiator) on the group buying is an interesting problem, existing studies have paid few attention to this topic. Different from the collaborator recommendation that only considers users with high similarity to the target user, co-purchaser recommendation takes both users with high and weak similarity into account, and the recommendation results can achieve high recall and diversity. However, the task turns out to be a challenging problem since it is hard to make a precise recommendation for buyers with weak similarity. To address the problem, we propose the following two methods. In the first one, we directly impose a penalty to the weak similar co-purchasers in the embedding space. To further improve the recommendation performance, in the second one, we smoothly increase the co-occurrence probability of the weak similar co-purchasers by truncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has high recommendation performance. In addition, considering that co-purchase may last longer, the total recommendation result can be generated in multiple stages and adjust the current recommendation list based on the feedback from the recommendation of previous stages. It is a trick for all co-purchaser recommendation methods to make the total result better.

In this paper, we study a new type of spatial crowdsourcing, namely competitive detour tasking, where workers can make detours from their original travel paths to perform multiple tasks, and each worker is allowed to compete for preferred tasks by strategically claiming his/her detour costs. The objective is to make suitable task assignment by maximizing the social welfare of crowdsourcing systems and protecting workers' private sensitive information. We first model the task assignment problem as a reverse auction process. We formalize the winning bid selection of reverse auction as an n-to-one weighted bipartite graph matching problem with multiple 0-1 knapsack constraints. Since this problem is NP-hard, we design an approximation algorithm to select winning bids and determine corresponding payments. Based on this, a Secure Reverse Auction (SRA) protocol is proposed for this novel spatial crowdsourcing. We analyze the approximation performance of the proposed protocol and prove that it has some desired properties, including truthfulness, individual rationality, computational efficiency, and security. To the best of our knowledge, this is the first theoretically provable secure auction protocol for spatial crowdsourcing systems. In addition, we also conduct extensive simulations on a real trace to verify the performance of the proposed protocol.

An Attributed Dynamic Graph (ADG) contains multiple dynamic attributes associated with each edge. In ADG based applications, people usually can specify multiple constrains in the attributes to illustrate their requirements, such as the total cost, the total travel time and the stopover interval of a flight between two cities. This inspires a type of Multi-Constrained Temporal Path (MCTP) discovery in ADGs, which is a challenging NP-Complete problem. In order to deliver an efficient and effective temporal path discovery method to be used in real-time environment, we propose a Reinforcement Learning (RL) based, Monte Carlo Tree Search algorithm (RLMCTS). RL-MCTS uses a newly designed memory structure to address the challenges of Monte Carlo Tree Search (MCTS) in MCTP discovery. To the best of our knowledge, RL-MCTS is the first RL algorithm that supports path discovery in ADGs. The experimental results on ten real dynamic graphs demonstrate that our algorithm outperforms the state-of-the-art methods in terms of both efficiency and effectiveness.

Semantic drift is a common problem in iterative information extraction. Previous approaches for minimizing semantic drift may incur substantial loss in recall. We observe that most semantic drifts are introduced by a small number of questionable extractions in the earlier rounds of iterations. These extractions subsequently introduce a large number of questionable results, which lead to the semantic drift phenomenon. We call these questionable extractions Drifting Points (DPs). If erroneous extractions are the “symptoms” of semantic drift, then DPs are the “causes” of semantic drift. In this paper, we propose a method to minimize semantic drift by identifying the DPs and removing the effect introduced by the DPs. We use isA (concept-instance) extraction as an example to describe our approach in cleaning information extraction errors caused by semantic drift, but we perform experiments on different relation extraction processes on three large real data extraction collections. The experimental results show that our DP cleaning method enables us to clean around 90 percent incorrect instances or patterns with about 90 percent precision, which outperforms the previous approaches we compare with.

Graph Pattern Matching (GPM) has been used in lots of areas, like biology, medical science, and physics. With the advent of Online Social Networks (OSNs), recently, GPM has been playing a significant role in social network analysis, which has been widely used in, for example, finding experts, social community mining, and social position detection. Given a query which contains a pattern graph GQand a data graph GD, a GPM algorithm finds those subgraphs, GM, that match GQin GD. However, the existing GPM methods do not consider the multiple end-to-end constraints of the social contexts, like social relationships, social trust, and social positions on edges in GQ, which are commonly found in various applications, such as crowdsourcing travel, social network based ecommerce, and study group selection, etc. In this paper, we first conceptually extend Bounded Simulation to Multi-Constrained Simulation (MCS), and propose a novel NP-Complete Multi-Constrained Graph Pattern Matching (MC-GPM) problem. Then, to address the efficiency issue in large-scale MC-GPM, we propose a new concept called Strong Social Component (SSC), consisting of participants with strong social connections. We also propose an approach to identifying SSCs, and propose a novel index method and a graph compression method for SSC. Moreover, we devise a multithreading heuristic algorithm, called M-HAMC, to bidirectionally search the MC-GPM results in parallel without decompressing graphs. An extensive empirical study over five real-world large-scale social graphs has demonstrated the effectiveness and efficiency of our approach.

Information Extraction by Text Segmentation (IETS) aims at segmenting text inputs to extract implicit data values contained in them.The state-of-art IETS approaches mainly rely on machine learning techniques, either supervised or unsupervised.However, while the supervised approaches require a large labelled training data, the performance of the unsupervised ones could be unstable on different data sets.To overcome their weaknesses, this paper introduces CNN-IETS, a novel unsupervised probabilistic approach that takes the advantages of pre-existing data and a Convolution Neural Network (CNN)-based probabilistic classification model. While using the CNN model can ease the burden of selecting high-quality features in associating text segments with attributes of a given domain, the pre-existing data as a domain knowledge base can provide training data with a comprehensive list of features for building the CNN model.Given an input text, we do initial segmentation (according to the occurrences of these words in the knowledge base) to generate text segments for CNN classification with probabilities. Then, based on the probabilistic CNN classification results, we work on finding the most probable labelling way to the whole input text.As a complementary, a bidirectional sequencing model learned on-demand from test data is finally deployed to do further adjustment to some problematic labelled segments.Our experimental study conducted on several real data collections shows that CNN-IETS improves the extraction quality of state-of-art approaches by more than 10%.

Schema Matching (SM) and Record Matching (RM) are two necessary steps in integrating multiple relational tables of different schemas, where SM unifies the schemas and RM detects records referring to the same real-world entity. The two processes have been thoroughly studied separately, but few attention has been paid to the interaction of SM and RM. In this work we find that, even alternating them in a simple manner, SM and RM can benefit from each other to reach a better integration performance (i.e., in terms of precision and recall). Therefore, combining SM and RM is a promising solution for improving data integration.

Schema Matching (SM) and Record Matching (RM) are two necessary steps in integrating multiple relational tables of different schemas, where SM unifies the schemas and RM detects records referring to the same real-world entity. The two processes have been thoroughly studied separately, but few attention has been paid to the interaction of SM and RM. In this work, we find that, even alternating them in a simple manner, SM and RM can benefit from each other to reach a better integration performance (i.e., in terms of precision and recall). Therefore, combining SM and RM is a promising solution for improving data integration. To this end, we define novel matching rules for SM and RM, respectively, that is, every SM decision is made based on intermediate RM results, and vice versa, such that SM and RM can be performed alternately. The quality of integration is guaranteed by a Matching Likelihood Estimation model and the control of semantic drift, which prevent the effect of mismatch magnification. To reduce the computational cost, we design an index structure based on q-grams and a greedy search algorithm that can reduce around 90 percent overhead of the interaction. Extensive experiments on three data collections show that the combination and interaction between SM and RM significantly outperforms previous works that conduct SM and RM separately.

Outsourcing database to clouds is a scalable and cost-effective way for large scale data storage, management, and query processing. Trajectory data contain rich spatio-temporal relationships and reveal many forms of individual sensitive information (e.g., home address, health condition), which necessitate them to be encrypted before being outsourced for privacy concerns. However, efficient query processing over encrypted trajectory data is a very challenging task. Though some achievements have been reported very recently for simple queries (e.g., SQL queries, kNN queries) on encrypted data, there is rather limited progress on secure evaluation of trajectory queries because they are more complex and need special treatment. In this paper, we focus on secure trajectory similarity computation that is the cornerstone of secure trajectory query processing. More specifically, we propose an efficient solution to securely compute the similarity between two encrypted trajectories, which reveals nothing about the trajectories, but the final result. We theoretically prove that our solution is secure against the semi-honest adversaries model as all the intermediate information in our protocols can be simulated in polynomial time. Finally we empirically study the efficiency of the proposed method, which demonstrates the feasibility of our solution.

Graph Pattern Matching (GPM) plays a significant role in social network analysis, which has been widely used in, for example, experts finding, social community mining and social position detection. Given a pattern graph GQand a data graph GD, a GPM algorithm finds those subgraphs, GM, that match GQin GD. However, the existing GPM methods do not consider the multiple constraints on edges in GQ, which are commonly exist in various applications such as, crowdsourcing travel, social network based e-commerce and study group selection, etc. In this paper, we first conceptually extend Bounded Simulation to Multi-Constrained Simulation (MCS), and propose a novel NP-Complete Multi-Constrained Graph Pattern Matching (MC-GPM) problem. Then, to address the efficiency issue in large-scale MC-GPM, we propose a new concept called Strong Social Component (SSC), consisting of participants with strong social connections. We also propose an approach to identify SSCs, and propose a novel index method and a graph compression method for SSC. Moreover, we devise a heuristic algorithm to identify MC-GPM results effectively and efficiently without decompressing graphs. An extensive empirical study on five real-world large-scale social graphs has demonstrated the effectiveness, efficiency and scalability of our approach.

Currently, the natural expectation of contracting a set of Web services by virtue of their semantics is becoming more and more feasible and popular. Meanwhile, it is generally accepted that a formalism with a well-defined model-theoretic semantics (i.e. some sort of logics) should be considered as the underpinning of Semantic Web Services [1]. In this paper, concrete domain and action theory are incorporated into a very expressive DL, called ALCQO. Notably, this extension can significantly augment the expressive power for modeling and reasoning about dynamic aspects of services contracting. At the same time, the original nature and advantages of classical DLs, particularly the ability to describe "static" aspects of Web services, are also preserved to the extent possible.

QoS-aware Web services composition has recently received much attention. While most work focused on service selection, we study QoS in another stage of the life cycle of composite services, namely, scheduling. An interesting problem is whether we can obtain better QoS via scheduling even when the component services have been fixed. In this paper, we propose an approach to find an optimal (near-optimal) schedule with the least cancellation cost, which can further improve the overall QoS of composite services. An approach to analyze the expected cancellation cost of a schedule of a composite service is proposed and QoS-Aware service scheduling is formalized as a Constraint Satisfaction Optimization Problem (CoSOP). Two algorithms - heuristic back tracking and genetic algorithm - are presented to find an optimal (near-optimal) schedule, and their performance is studied by simulations. Preliminary experimental results show that our approach is effective.

In this paper, we incorporate concrete domain and action theory into a very expressive Description Logic (DL), called ALCQO. Notably, this extension can significantly augment the expressive power for modeling and reasoning about dynamic aspects of services contracting. Meanwhile, the original nature and advantages of classical DLs are also preserved to the extent possible. Categories and Subject Descriptors: