OpenRaiser/Intern-Atlas huggingface dataset:
Named Entity-based Question-Answering Pair Generator
Authors: Aritra Kumar Lahiri, Qinmin Vivian Hu
Venue: CIKM
Year: 2022

In this paper, we demonstrate an approach for question-answering pair generation primarily based on named entities using TV series data. Our generator provides a task based pipeline abstraction, which can be interpreted by a simple method where a context paragraph is passed as an input argument to the pipeline and the output is generated based on the task selected. We currently implemented three tasks for the pipeline which includes the following - i) qg - single question generation, ii) multi-qa-qg for multiple QA pairs generation and iii) e2e-qg for end to end QA pair generation.
Semorph: A Morphology Semantic Enhanced Pre-trained Model for Chinese Spam Text Detection
Authors: Kaiting Lai, Yinong Long, Bowen Wu, Ying Li, Baoxun Wang
Venue: CIKM
Year: 2022

Chinese spam text detection is essential for social media since these texts affect the user experience of Chinese speakers and pollute the community. The underlying text classification method is employed to explore the unique combinations of characters that represent clues of spam information from annotated or further augmented data. However, based on the diversity of Chinese characters in glyphs, the spammers frequently wrap the spam content in another visually close text to fool the model but make sure people understand. This paper proposes to adopt the essence of human cognition of these adversarial texts into spam text detection models, by designing a pre-trained model to learn the morphology semantics of Chinese characters and represent their contextual meanings from scratch. The model pre-trains on self-supervised Chinese corpus and fine-tunes on spam-annotated community texts. Besides, cooperating with the pre-trained model that can capture the morphological features of Chinese, a new data perturbation method is introduced to guide the optimization towards the direction of recognizing the actual meaning of a text after spammers tamper with partial characters by visually close ones. The experimental results have shown that our proposed methodology can notably improve the performance of spam text detection as well as maintain robustness against adversarial samples.
Loyalty-based Task Assignment in Spatial Crowdsourcing
Authors: Tinghao Lai, Yan Zhao, Weizhu Qian, Kai Zheng
Venue: CIKM
Year: 2022

With the fast-paced development of mobile networks and the widespread usage of mobile devices, Spatial Crowdsourcing (SC) has drawn increasing attention in recent years. SC has the potential for collecting information for a broad range of applications such as on-demand local delivery and on-demand transportation. One of the critical issues in SC is task assignment that allocates location-based tasks (e.g., delivering food and packages) to appropriate moving workers (i.e., intelligent device carriers). In this paper, we study a loyalty-based task assignment problem, which aims to maximize the overall rewards of workers while considering worker loyalty. We propose a two-phase framework to solve the problem, including a worker loyalty prediction and a task assignment phase. In the first phase, we use a model based on an efficient time series prediction method called Prophet and an Entropy Weighting method to extract workers' short-term and long-term loyalty and then predict workers' current loyalty scores. In the task assignment phase, we design a Kuhn-Munkras-based algorithm that achieves the optimal task assignment and an efficient Degree-Reduction-based algorithm with minority first scheme. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.
Demonstrating SubStrat: A Subset-Based Strategy for Faster AutoML on Large Datasets
Authors: Teddy Lazebnik, Amit Somech
Venue: CIKM
Year: 2022

Automated machine learning (AutoML) frameworks are gaining popularity among data scientists as they dramatically reduce the manual work devoted to the construction of ML pipelines while obtaining similar and sometimes even better results than manually-built models. Such frameworks intelligently search among millions of possible ML pipeline configurations to finally retrieve an optimal pipeline in terms of predictive accuracy. However, when the training dataset is large, the construction and evaluation of a single ML pipeline take longer, which makes the overall AutoML running times increasingly high. To this end, in this work we demonstrate SubStrat, an AutoML optimization strategy that tackles the dataset size rather than the configurations search space. SubStrat wraps existing AutoML tools, and instead of executing them directly on the large dataset, it uses a genetic-based algorithm to find a small yet representative data subset that preserves characteristics of the original one. SubStrat then employs the AutoML tool on the generated subset, resulting in an intermediate ML pipeline, which is later refined by executing a restricted, much shorter, AutoML process on the large dataset. We demonstrate SubStrat on both AutoSklearn, TPOT, and H2O, three popular AutoML frameworks, using several real-life datasets.
Legal Charge Prediction via Bilinear Attention Network
Authors: Yuquan Le, Yuming Zhao, Meng Chen, Zhe Quan, Xiaodong He, Kenli Li
Venue: CIKM
Year: 2022

The legal charge prediction task aims to judge appropriate charges according to the given fact description in cases. Most existing methods formulate it as a multi-class text classification problem and have achieved tremendous progress. However, the performance on low-frequency charges is still unsatisfactory. Previous studies indicate leveraging the charge label information can facilitate this task, but the approaches to utilizing the label information are not fully explored. In this paper, inspired by the vision-language information fusion techniques in the multi-modal field, we propose a novel model (denoted as LeapBank) by fusing the representations of text and labels to enhance the legal charge prediction task. Specifically, we devise a representation fusion block based on the bilinear attention network to interact the labels and text tokens seamlessly. Extensive experiments are conducted on three real-world datasets to compare our proposed method with state-of-the-art models. Experimental results show that LeapBank obtains up to 8.5% Macro-F1 improvements on the low-frequency charges, demonstrating our model's superiority and competitiveness.
Efficient Data Augmentation Policy for Electrocardiograms
Authors: ByeongTak Lee, Yong-Yeon Jo, Seon-Yu Lim, Youngjae Song, Joon-Myoung Kwon
Venue: CIKM
Year: 2022

We present the taxonomy of data augmentation for electrocardiogram (ECG) after reviewing various ECG augmentation methods. On the basis of the taxonomy, we demonstrate the effect of augmentation methods on the ECG classification via extensive experiments. Initially, we examine the performance trend as the magnitude of distortion increases and identify the optimal distortion magnitude. Secondly, we investigate the synergistic combinations of the transformations and identify the pairs of transformations with the greatest positive effect. Finally, based on our experimental findings, we propose an efficient augmentation policy and demonstrate that it outperforms previous augmentation policies.
Maximum Norm Minimization: A Single-Policy Multi-Objective Reinforcement Learning to Expansion of the Pareto Front
Authors: Seonjae Lee, Myoung-Hoon Lee, Jun Moon
Venue: CIKM
Year: 2022

In this paper, we propose Maximum Norm Minimization (MNM), a single-policy Multi-Objective Reinforcement Learning (MORL) algorithm to solve the multi-objective RL problem. The main objective of our MNM is to provide the Pareto optimal points constituting the Pareto front in the multi-objective space. First, MNM measures distances among the Pareto optimal points in the current Pareto front and then normalizes the distances based on maximum and minimum reward values for each objective in the multi-objective space. Second, MNM identifies the maximum norm, i.e., the maximum value of the normalized Pareto optimal distances. Then MNM seeks to find a new Pareto optimal point, which corresponds to the middle of the two Pareto optimal points constituting the maximum norm. By iterating these two processes, MNM is able to expand and densify the Pareto front with increasing summation of the Pareto front volumes and decreasing mean-squared distance of the Pareto optimal points. To validate the performance of MNM, we provide the experimental results of five complex robotic multi-objective environments. In particular, we compare the performance of MNM with those of other state-of-the-art methods in terms of the summation of volumes and the mean-squared distance of the Pareto optimal points.
Accelerating CNN via Dynamic Pattern-based Pruning Network
Authors: Gwanghan Lee, Saebyeol Shin, Simon S. Woo
Venue: CIKM
Year: 2022

Recently, dynamic pruning methods have been actively researched, as they have shown very effective and remarkable performance in reducing computation complexity of deep neural networks. Nevertheless, most dynamic pruning methods fail to achieve actual acceleration due to the extra overheads caused by indexing and weight-copying to implement the dynamic sparse patterns for every input sample. To address this issue, we propose Dynamic Pattern-based Pruning Network (DPPNet), which preserves the advantages of both static and dynamic networks. First, our method statically prunes the weight kernel into various sparse patterns. Then, the dynamic convolution kernel is generated via aggregating input-dependent attention weights and static kernels. Unlike previous dynamic pruning methods, our novel method dynamically fuses static kernel patterns, enhancing the kernel's representational power without additional overhead. Moreover, our dynamic sparse pattern enables an efficient process using BLAS libraries, accomplishing actual acceleration. We demonstrate the effectiveness of the proposed DPPNet on CIFAR and ImageNet, outperforming the state-of-the-art methods achieving better accuracy with lower computational cost. For example, on ImageNet classification, ResNet34 utilizing DPP module achieves state-of-the-art performance with 65.6% FLOPs reduction and the inference speed increased by 35.9% without loss in accuracy. Code is available at https://github.com/lee-gwang/DPPNet.
A Multi-grained Dataset for News Event Triggered Knowledge Update
Authors: Yu-Ting Lee, Ying-Jhe Tang, Yu-Chung Cheng, Pai-Lin Chen, Tsai-Yen Li, Hen-Hsen Huang
Venue: CIKM
Year: 2022

Keeping knowledge facts up-to-date is labored and costly as the world rapidly changes and new information emerges every second. In this work, we introduce a novel task, news event triggered knowledge update. Given an existing article about a topic with a news event about the topic, the aim of our task is to generate an updated article according to the information from the news event. We create a multi-grained dataset for the investigation of our task. The articles from Wikipedia are collected and aligned with news events at multiple language units, including the citation text, the first paragraph, and the full content of the news article. Baseline models are also explored at three levels of knowledge update, including the first paragraph, the summary, and the full content of the knowledge facts.
Mining of Real-world Hypergraphs: Patterns, Tools, and Generators
Authors: Geon Lee, Jaemin Yoo, Kijung Shin
Venue: CIKM
Year: 2022

Group interactions are prevalent in various complex systems (e.g., collaborations of researchers and group discussions on online Q&A sites), and they are commonly modeled as hypergraphs. Hyperedges, which compose a hypergraph, are non-empty subsets of any number of nodes, and thus each hyperedge naturally represents a group interaction among entities. The higher-order nature of hypergraphs brings about unique structural properties that have not been considered in ordinary pairwise graphs. In this tutorial, we offer a comprehensive overview of a new research topic called hypergraph mining. We first present recently revealed structural properties of real-world hypergraphs, including (a) static and dynamic patterns, (b) global and local patterns, and (c) connectivity and overlapping patterns. Together with the patterns, we describe advanced data mining tools used for their discovery. Lastly, we introduce simple yet realistic hypergraph generative models that provide an explanation of the structural properties.
Parallel Skyline Processing Using Space Pruning on GPU
Authors: Chuanwen Li, Yu Gu, Jianzhong Qi, Ge Yu
Venue: CIKM
Year: 2022

Skyline computation is an essential database operation that has many applications in multi-criteria decision making scenarios such as recommender systems. Existing algorithms have focused on checking point domination, which lack efficiency over large datasets. We propose a grid-based structure that enables grid cell domination checks. We show that only a small constant number of cells need to be checked which is independent from the number of data points. Our structure also enables parallel processing. We thus obtain a highly efficient parallel skyline algorithm named SkyCell, taking advantage of the parallelization power of graphics processing units. Experimental results confirm the effectiveness and efficiency of SkyCell -- it outperforms state-of-the-art algorithms consistently and by up to over two orders of magnitude in the computation time.
AdaDebunk: An Efficient and Reliable Deep State Space Model for Adaptive Fake News Early Detection
Authors: Ke Li, Bin Guo, Siyuan Ren, Zhiwen Yu
Venue: CIKM
Year: 2022

Automatically detecting fake news as early as possible becomes increasingly necessary. Conventional approaches of fake news early detection (FNED) verify news' veracity with a predefined and indiscriminate detection position, which depends on domain experience and leads to unstable performance. More advanced methods address this problem with a proposed concept of adaptive detection position (ADP), i.e. the position where the veracity of the news record can be concluded. Yet these methods either lack theoretical reliability or weaken complex dependencies among multi-aspect clues, thus failing to provide practical and reasonable detection. This work focuses on the adaptive FNED problem and proposes a novel efficient and reliable deep state space model, namely AdaDebunk, which models the complex probabilistic dependencies. Specifically, a Bayes' theorem-based dynamic inference algorithm is designed to infer the ADPs and veracity, supporting the accumulation of multi-aspect clues. Besides, a training mechanism with hybrid loss is also designed to solve the over-/under-fitting problems, which further trades off the performance and generalization ability. Experiments on two real-world fake news datasets are conducted to evaluate the effectiveness and superiority of AdaDebunk. Compared with the state-of-the-art baselines, AdaDebunk achieves a 10% increase in F1 performance. Meanwhile, a case study is provided to demonstrate the reliability of AdaDebunk as well as our research motivation.
Demonstration of LogicLib: An Expressive Multi-Language Interface over Scalable Datalog System
Authors: Mingda Li, Jin Wang, Guorui Xiao, Youfu Li, Carlo Zaniolo
Venue: CIKM
Year: 2022

With the ever-increasing volume of data, there is an urgent need to provide expressive and efficient tools to support Big Data analytics. The declarative logical language Datalog has proven very effective at expressing concisely graph, machine learning, and knowledge discovery applications via recursive queries. In this demonstration, we develop Logic Library (LLib), a library of recursive algorithms written in Datalog that can be executed in BigDatalog, a Datalog engine on top of Apache Spark developed by us. LLib encapsulates complex logic-based algorithms into high-level APIs, which simplify the development and provide a unified interface akin to the one of Spark MLlib. As LLib is fully compatible with DataFrame, it enables the integrated utilization of its built-in applications and new Datalog queries with existing Spark functions, such as those provided by \mlib and Spark SQL. With a variety of examples, we will (i) show how to write programs with LLib to express a variety of applications; (ii) illustrate its user experience in Apache Spark ecosystem; and (iii) present a user-friendly interface to interact with the LLib framework and monitor the query results.
Heterogeneous Graph Attention Network for Drug-Target Interaction Prediction
Authors: Mei Li, Xiangrui Cai, Linyu Li, Sihan Xu, Hua Ji
Venue: CIKM
Year: 2022

Identification of drug-target interactions (DTIs) is crucial for drug discovery and drug repositioning. Existing graph neural network (GNN) based methods only aggregate information from directly connected nodes restricted in a drug-related or a target-related network, and are incapable of capturing long-range dependencies in the biological heterogeneous graph. In this paper, we propose the heterogeneous graph attention network (HGAN) to capture the complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. HGAN enhances heterogeneous graph structure learning from both the intra-layer perspective and the inter-layer perspective. Concretely, we develop an enhanced graph attention diffusion layer (EGADL), which efficiently builds connections between node pairs that may not be directly connected, enabling information passing from important nodes multiple hops away. By stacking multiple EGADLs, we further enlarge the receptive field from the inter-layer perspective. HGAN advances 15 state-of-the-art methods on two heterogeneous biological datasets, achieving the results near to 1 in terms of AUC and AUPR. We also find that enlarging receptive fields from the inter-layer perspective (stacking layers) is more effective than that from the intra-layer perspective (attention diffusion) for HGAN to achieve promising DTI prediction performances. The code is available at https://github.com/Zora-LM/HGAN-DTI.
A Hierarchical User Behavior Modeling Framework for Cross-Domain Click-Through Rate Prediction
Authors: Hai Li, Xin Dong, Lei Cheng, Linjian Mo
Venue: CIKM
Year: 2022

Click-through rate (CTR) prediction is a long-standing problem in advertising systems. Existing single-domain CTR prediction methods suffer from the data sparsity problem since few users can click advertisements on many items. Recently, cross-domain CTR prediction leverages the relatively richer information from a source domain to improve the performance on a target domain with sparser information, but it cannot explicitly capture users' diverse interests in different domains. In this paper, we propose a novel hierarchical user behavior modeling framework for cross-domain CTR prediction, named HBMNet. HBMNet contains two main components: an element-wise behavior transfer(EWBT) layer and a user representation layer. EWBT layer transfers the information collected from one domain by element-level masks to dynamically highlight the informative elements in another domain. The user representation layer performs behavior-level attention between these behavior representations and the ranking item representation. Extensive experimental results on two cross-domain datasets show that the proposed HBMNet outperforms SOTA models.
Heterogeneous Hypergraph Neural Network for Friend Recommendation with Human Mobility
Authors: Yongkang Li, Zipei Fan, Jixiao Zhang, Dengheng Shi, Tianqi Xu, Du Yin, Jinliang Deng, Xuan Song
Venue: CIKM
Year: 2022

Friend recommendation from human mobility is a vital real-world application of location-based social networks (LBSN). It is necessary to recognize patterns from human mobility to assist friend recommendation because previous works have shown complex relations between them. However, most of previous works either modelled social networks and user trajectories separately, or only used classical simple graph-based methods with an edge linking two nodes that cannot fully model the complex data structure of LBSN. Inspired by the fact that hyperedges can connect multiple nodes of different types, we model user trajectories and check-in records as hyperedges in a novel heterogeneous LBSN hypergraph to represent complex spatio-temporal information. And then, we design a type-specific attention mechanism for an end-to-end trainable heterogeneous hypergraph neural network (HHGNN) with supervised contrastive learning, which can learn hypergraph node embedding for the next friend recommendation task. At last, our model HHGNN outperforms the state-of-the-art methods on four real-world city datasets, while ablation studies also confirm the effectiveness of each model part.
Spatiotemporal-aware Session-based Recommendation with Graph Neural Networks
Authors: Yinfeng Li, Chen Gao, Xiaoyi Du, Huazhou Wei, Hengliang Luo, Depeng Jin, Yong Li
Venue: CIKM
Year: 2022

Session-based recommendation (SBR) aims to recommend items based on user behaviors in a session. For the online life service platforms, such as Meituan, both the user's location and the current time primarily cause the different patterns and intents in user behaviors. Hence, spatiotemporal context plays a significant role in the recommendation on those platforms, which motivates an important problem of spatiotemporal-aware session-based recommendation (STSBR). Since the spatiotemporal context is introduced, there are two critical challenges: 1) how to capture session-level relations of spatiotemporal context (inter-session view), and 2) how to model the complex user decision-making process at a specific location and time (intra-session view). To address them, we propose a novel solution named STAGE in this paper. Specifically, STAGE first constructs a global information graph to model the multi-level relations among all sessions, and a session decision graph to capture the complex user decision process for each session. STAGE then performs inter-session and intra-session embedding propagation on the constructed graphs with the proposed graph attentive convolution (GAC) to learn representations from the above two perspectives. Finally, the learned representations are combined with spatiotemporal-aware soft-attention for final recommendation. Extensive experiments on two datasets from Meituan demonstrate the superiority of STAGE over state-of-the-art methods. Further studies also verify that each component is effective.
Frequent Itemset Mining with Local Differential Privacy
Authors: Junhui Li, Wensheng Gan, Yijie Gui, Yongdong Wu, Philip S. Yu
Venue: CIKM
Year: 2022

With the development of the Internet, a large amount of transaction data (e.g., shopping records, web browsing history), which represents user data, has been generated. By collecting user transaction data and learning specific patterns and association rules from it, service providers can provide better services. However, because of the increasing privacy awareness and the formulation of laws on data protection, collecting data directly from users will raise privacy concerns. The concept of local differential privacy (LDP), which provides strict data privacy protection on the user side and allows effective statistical analysis on the server side, is able to protect user privacy and perform statistics on sensitive issues at the same time. This paper adopts padding-and-sampling-based frequent oracle (PSFO), combined with an interactive query-response method satisfying local differential privacy, to identify frequent itemsets in an efficient and accurate way. Therefore, this paper proposes FIML, an improved algorithm for finding frequent itemsets in the LDP setting of transaction data. The data collector generates frequent candidate sets based on the results of the previous stage and uses them for querying, and users randomize their responses in a reduced domain to achieve local differential privacy. Extensive experiments on real-world and synthetic datasets show that the FIML algorithm can find frequent itemsets more efficiently with the same privacy protection and computational cost.