OpenRaiser/Intern-Atlas huggingface dataset:
DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps
Authors: Miao Fan, Jizhou Huang, Haifeng Wang
Venue: CIKM
Year: 2022

With the increased popularity of mobile devices, Web mapping services have become an indispensable tool in our daily lives. To provide user-satisfied services, such as location searches, the point of interest (POI) database is the fundamental infrastructure, as it archives multimodal information on billions of geographic locations closely related to people's lives, such as a shop or a bank. Therefore, verifying the correctness of a large-scale POI database is vital. To achieve this goal, many industrial companies adopt volunteered geographic information (VGI) platforms that enable thousands of crowdworkers and expert mappers to verify POIs seamlessly; but to do so, they have to spend millions of dollars every year. To save the tremendous labor costs, we devised DuMapper, an automatic system for large-scale POI verification with the multimodal street-view data at Baidu Maps. This paper presents not only DuMapper I, which imitates the process of POI verification conducted by expert mappers, but also proposes DuMapper II, a highly efficient framework to accelerate POI verification by means of deep multimodal embedding and approximate nearest neighbor (ANN) search. DuMapper II takes the signboard image and the coordinates of a real-world place as input to generate a low-dimensional vector, which can be leveraged by ANN algorithms to conduct a more accurate search through billions of archived POIs in the database for verification within milliseconds. Compared with DuMapper I, experimental results demonstrate that DuMapper II can significantly increase the throughput of POI verification by 50 times. DuMapper has already been deployed in production since June 2018, which dramatically improves the productivity and efficiency of POI verification at Baidu Maps. As of December 31, 2021, it has enacted over 405 million iterations of POI verification within a 3.5-year period, representing an approximate workload of 800 high-performance expert mappers.
Semi-Supervised Learning with Data Augmentation for Tabular Data
Authors: Jun-Peng Fang, Caizhi Tang, Qing Cui, Feng Zhu, Longfei Li, Jun Zhou, Wei Zhu
Venue: CIKM
Year: 2022

Data augmentation-based semi-supervised learning (SSL) methods have made great progress in computer vision and natural language processing areas. One of the most important factors is that the semantic structure invariance of these data allows the augmentation procedure (e.g., rotating images or masking words) to thoroughly utilize the enormous amount of unlabeled data. However, the tabular data does not possess an obvious invariant structure, and therefore similar data augmentation methods do not apply to it. To fill this gap, we present a simple yet efficient data augmentation method particular designed for tabular data and apply it to the SSL algorithm: SDAT (Semi-supervised learning with Data Augmentation for Tabular data). We adopt a multi-task learning framework that consists of two components: the data augmentation procedure and the consistency training procedure. The data augmentation procedure which perturbs in latent space employs a variational auto-encoder (VAE) to generate the reconstructed samples as augmented samples. The consistency training procedure constrains the predictions to be invariant between the augmented samples and the corresponding original samples. By sharing a representation network (encoder), we jointly train the two components to improve effectiveness and efficiency. Extensive experimental studies validate the effectiveness of the proposed method on the tabular datasets.
Few-Shot Relational Triple Extraction with Perspective Transfer Network
Authors: Junbo Fei, Weixin Zeng, Xiang Zhao, Xuanyi Li, Weidong Xiao
Venue: CIKM
Year: 2022

Few-shot Relational Triple Extraction (RTE) aims at detecting emerging relation types along with their entity pairs from unstructured text with the support of a few labeled samples. Prior arts use conditional random field or nearest-neighbor matching strategy to extract entities and use prototypical networks for extracting relations from sentences. Nevertheless, they fail to utilize the triple-level information to verify the plausibility of extracted relational triples, and ignore the proper transfer among the perspectives of entity, relation and triple. To fill in these gaps, in this work, we put forward a novel perspective transfer network (PTN) to address few-shot RTE. Specifically, PTN starts from the relation perspective by checking the existence of a given relation. Then, it transfers to the entity perspective to locate entity spans with relation-specific support sets. Next, it transfers to the triple perspective to validate the plausibility of extracted relational triples. Finally, it transfers back to the relation perspective to check the next relation, and repeats the aforementioned procedure. By transferring among the perspectives of relation, entity, and triple, PTN not only validates the extracted elements at both local and global levels, but also effectively handles more realistic and difficult few-shot RTE scenarios such as multiple triple extraction and nonexistence of triples. Extensive experimental results on existing dataset and new datasets demonstrate that our approach can significantly improve performance over the state-of-the-arts.
Aries: Accurate Metric-based Representation Learning for Fast Top-k Trajectory Similarity Query
Authors: Chunhui Feng, Zhicheng Pan, Junhua Fang, Jiajie Xu, Pengpeng Zhao, Lei Zhao
Venue: CIKM
Year: 2022

With the prevalence of location-based services (LBS), trajectories are being generated rapidly. As is widely used in LBS, top-k trajectory similarity query serves as a key operation, deeply empowering applications such as travel route recommendation and carpooling. Given the rise of deep learning, trajectory representation has been well-proven to speed up this operator. However, existing representation-based computing modes remain two major problems understudied: the low quality of trajectory representation and insufficient support for various trajectory similarity metrics, which make them difficult to apply in practice. Therefore, we propose an Accurate metric-based representation learning approach for fast top-k trajectory similarity query, named Aries. Specifically, Aries has two sophisticated modules: (1) An novel trajectory embedding strategy enhanced by the bidirectional LSTM encoder and spatial attention mechanism, which can extract more precise and comprehensive knowledge. (2) A deep metric learning network aggregating multiple measures for better top-k query. Extensive experiments conducted on real trajectory dataset show that Aries achieves both impressive accuracy and lower training time compared with state-of-the-art solutions. In particular, it achieves 5x-10x speedup and 10%-20% accuracy improvement over Euclidean, Hausdorff, DTW, and EDR measures. Besides, our method can maintain stable performance when handling various scenarios, without repeated training in order to adapt to diverse similarity metrics.
Adaptive Graph Spatial-Temporal Transformer Network for Traffic Forecasting
Authors: Aosong Feng, Leandros Tassiulas
Venue: CIKM
Year: 2022

Traffic forecasting can be highly challenging due to complex spatial-temporal correlations and non-linear traffic patterns. Existing works mostly model such spatial-temporal dependencies by considering spatial correlations and temporal correlations separately, or within a sliding temporal window, and fail to model the direct spatial-temporal correlations. Inspired by the recent success of transformers in the graph domain, in this paper, we propose to directly model the cross-spatial-temporal correlations on the adaptive spatial-temporal graph using local multi-head self-attentions. We then propose a novel Adaptive Graph Spatial-Temporal Transformer Network (ASTTN), which stacks multiple spatial-temporal attention layers to apply self-attention on the input graph, followed by linear layers for predictions. Experimental results on public traffic network datasets, METR-LA PEMS-BAY, PeMSD4, and PeMSD7, demonstrate the superior performance of our model.
Subspace Co-clustering with Two-Way Graph Convolution
Authors: Chakib Fettal, Lazhar Labiod, Mohamed Nadif
Venue: CIKM
Year: 2022

Subspace clustering aims to cluster high dimensional data lying in a union of low-dimensional subspaces. It has shown good results on the task of image clustering but text clustering, using document-term matrices, proved more impervious to advances based on this approach. We hypothesize that this is because, compared to image data, text data is generally higher dimensional and sparser. This renders subspace clustering impractical in such a context. Here, we leverage subspace clustering for text by addressing these issues. We first extend the concept of subspace clustering to co-clustering, which has been extensively used on document-term matrices due to the resulting interplay between the document and term representations. We then address the sparsity problem through a two-way graph convolution, which promotes the grouping effect that has been credited for the effectiveness of some subspace clustering models. The proposed formulation results in an algorithm that is efficient both in terms of computational and spatial complexity. We show the competitiveness of our model w.r.t the state-of-the-art on document-term attributed graph datasets in terms of performance and efficiency.
Simulating Complex Problems Inside a Database
Authors: Giancarlo Fissore, Nikolaos Vasiloglou
Venue: CIKM
Year: 2022

The standard way to store and interact with the large amount of data that are central to the functioning of any modern business is through the use of a relational Knowledge Graph Management System (KGMS). In this paper we show how the relational model can be successfully exploited to model complex analytic scenarios while enjoying the same characteristics of clarity and flexibility as when modeling the data themselves. Using the Rel language, we simulate the daily schedule of an airline company as an agentbased system, and we will show how modeling this system through a set of relationships and logical rules will let us focus directly on the inherent complexity of our model, taking away most of the incidental effort in actually implementing our simulation.
MalNet: A Large-Scale Image Database of Malicious Software
Authors: Scott Freitas, Rahul Duggal, Duen Horng Chau
Venue: CIKM
Year: 2022

Computer vision is playing an increasingly important role in automated malware detection with the rise of the image-based binary representation. These binary images are fast to generate, require no feature engineering, and are resilient to popular obfuscation methods. Significant research has been conducted in this area, however, it has been restricted to small-scale or private datasets that only a few industry labs and research teams have access to. This lack of availability hinders examination of existing work, development of new research, and dissemination of ideas. We release MalNet-Image, the largest public cybersecurity image database, offering 24x more images and 70x more classes than existing databases (available at https://mal-net.org). MalNet-Image contains over 1.2 million malware images-across 47 types and 696 families---democratizing image-based malware capabilities by enabling researchers and practitioners to evaluate techniques that were previously reported in propriety settings. We report the first million-scale malware detection results on binary images. MalNet-Image unlocks new and unique opportunities to advance the frontiers of machine learning, enabling new research directions into vision-based cyber defenses, multi-class imbalanced classification, and interpretable security.
DP-HORUS: Differentially Private Hierarchical Count Histograms under Untrusted Server
Authors: Congcong Fu, Hui Li, Jian Lou, Jiangtao Cui
Venue: CIKM
Year: 2022

Hierarchical count histograms is the task of publishing count statistics at different granularity as per hierarchy defined on a dimension table in a data warehouse, which has wide applications in On-line Analytical Processing (OLAP) scenarios. In this paper, we systematically investigate this task subjected to the rigorous privacy-preserving constraint under the untrusted server setting. Our study first reveals that the straightforward baseline approach of the local differential privacy fails to achieve a satisfactory privacy and utility tradeoff. We are thus motivated to propose DP-HORUS, a novel crypto-assisted Differentially Private framework for Hierarchical cOunt histogRams under Untrusted Server. DP-HORUS consists of a series of novel designs, including 1) Encrypted Hierarchical Tree (EHT) structure, which maintains the concept hierarchy in the input data; 2) Random Matrix (RM), which reduces communication and computational cost; 3) To further boosted the utility, we propose DP-HORUS+ encompassing two additional modules of Histograms Structure (HS) and Hierarchical Consistency (HC), which are respectively introduced to reduce the noise caused by data sparsity and to ensure the hierarchy consistency. We provide both theoretical analysis and extensive empirical study on both real-world and synthetic datasets, which demonstrates the superior utility of the proposed methods over the state-of-the-art solutions while ensuring strict privacy guarantee.
C-Cast: A Real-Time Forecasting Model for a Controlled Sequence
Authors: Ren Fujiwara
Venue: CIKM
Year: 2022

Predictive control is an advanced control method that is used successfully in industrial control applications. One of the most fundamental demands for predictive control is the accurate forecasting of a ''controlled sequence" using exogenous sequences which consist of multiple attributes (manipulated variables and operation signals). Given a controlled sequence and exogenous sequences, how can we effectively forecast the future behavior of a controlled sequence? In this paper, we present C-Cast, an efficient and effective method for forecasting a time-evolving controlled sequence with exogenous sequences. Our proposed method has the following properties: (a) Adaptive: it captures important time-evolving patterns and operation shift in a time-evolving controlled sequence (b) Effective: it performs accurate forecasting. (c) Practical: it enables real-time controlled sequence forecasting fast enough to satisfy the limitation required for predictive control. Extensive experiments on a real dataset demonstrate that C-Cast consistently outperforms the best existing state-of-the-art methods as regards accuracy, and the execution speed is sufficiently fast.
Implicit User-Generated Content in the Service of Public Health
Authors: Evgeniy Gabrilovich
Venue: CIKM
Year: 2022

Every day millions of people use online products and services to satisfy their information needs. While doing so, they produce large volumes of user-generated content (UGC). In this talk, we will distinguish between "explicit" UGC, which is intended to be made public (such as product ratings or reviews), and "implicit" UGC, which can be responsibly anonymized and aggregated in a privacy-preserving way to improve public health. We will analyze implicit UGC as a positive consumption externality, and will discuss its beneficial uses across a range of public health applications. The bulk of this talk will focus on methods for aggregating and analyzing the data to provide timely signals that help guide public health interventions and assess their efficacy. We will discuss applications such as estimating disease incidence, outbreak prediction, mitigating pandemic spread, and improving public health messaging.
End-to-end Multi-task Learning Framework for Spatio-Temporal Grounding in Video Corpus
Authors: Yingqi Gao, Zhiling Luo, Shiqian Chen, Wei Zhou
Venue: CIKM
Year: 2022

In this paper, we consider a novel task, Video Corpus Spatio-Temporal Grounding (VCSTG) for material selection and spatio-temporal adaption in intelligent video editing. Given a text query depicting an object and a corpus of untrimmed and unsegmented videos, VCSTG aims to localize a sequence of spatio-temporal object tubes from the video corpus. Existing methods tackle the VCSTG task in a multi-stage approach, which encodes the query and video representation independently for each task, leading to local optimum. In this paper, we propose a novel one-stage multi-task learning based framework named MTSTG for the VCSTG task. MTSTG learns unified query and video representation for video retrieval, temporal grounding and spatial grounding tasks. Video-level, frame-level and object-level contrastive learning are introduced to measure the mutual information between query and video at different granularity. Comprehensive experiments demonstrate our newly proposed framework outperforms the state-of-the-art multi-stage methods on VidSTG dataset.
Towards Practical Large Scale Non-Linear Semi-Supervised Learning with Balancing Constraints
Authors: Zhengqing Gao, Huimin Wu, Martin Takác, Bin Gu
Venue: CIKM
Year: 2022

Semi-Supervised Support Vector Machine (S3VM) is one of the most popular methods for semi-supervised learning, which can make full use of plentiful, easily accessible unlabeled data. Balancing constraint is normally enforced in S3VM (denoted as BCS3VM) to avoid the harmful solution which assigns all or most of the unlabeled examples to one same label. Traditionally, non-linear BCS3VM is solved by sequential minimal optimization algorithm. Recently, a novel incremental learning algorithm (IL-BCS3VM) was proposed to scale up BCS3VM further. However, IL-BCS3VM needs to calculate the inverse of the linear system related to the support matrix, making the algorithm not scalable enough. To make BCS3VM be more practical in large-scale problems, in this paper, we propose a new scalable BCS3VM with accelerated triply stochastic gradients (denoted as TSG-BCS3VM). Specifically, to make the balancing constraint handle different proportions of positive and negative samples among labeled and unlabeled data, we propose a soft balancing constraint for S3VM. To make the algorithm scalable, we generate triply stochastic gradients by sampling labeled and unlabeled samples as well as the random features to update the solutions, where Quasi-Monte Carlo (QMC) sampling is utilized on random features to accelerate TSG-BCS3VM further. Our theoretical analysis shows that the convergence rate is O(1/√T) for both diminishing and constant learning rates where T is the number of iterations, which is much better than previous results thanks to the QMC method. Empirical results on a variety of benchmark datasets show that our algorithm not only has a good generalization performance but also enjoys better scalability than existing BCS3VM algorithms.
Consistent, Balanced, and Overlapping Label Trees for Extreme Multi-label Learning
Authors: Zhiqi Ge, Yuanyuan Guan, Ximing Li, Bo Fu
Venue: CIKM
Year: 2022

The emerging eXtreme Multi-label Learning (XML) aims to induce multi-label predictive models from big datasets with extremely large numbers of instances, features, and especially labels. To meet the great efficiency challenge of XML, one flexible solution is the methodology of label tree, which, as its name suggests, is technically defined as a tree hierarchy of label subsets, partitioning the original large-scale XML problem into a number of small-scale sub-problems (i.e., denoted by leaf nodes) and then reducing the complexity to logarithmic time. Notably, the expected label trees should accurately find the right leaf nodes for future instances (i.e., effectiveness) and generate balanced leaf nodes (i.e., efficiency). To achieve this, we propose a novel generic method of label tree, namely Consistent, Balanced, and Overlapping Label Tree (CBOLT). To enhance the precision, we employ the weighted clustering to partition non-leaf nodes and allow overlapping label subsets, enabling to alleviate the inconsistent path and disjoint label subset issues. To improve the efficiency, we propose a new concept of a balanced problem scale and implement it with a balanced regularization for non-leaf nodes partition. We conduct extensive experiments on several benchmark XML datasets. Empirical results demonstrate that CBOLT is superior to the existing methods of label trees, and it can be applied to existing XML methods and achieve competitive performance with strong baselines.
Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels
Authors: Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke A. Rundensteiner
Venue: CIKM
Year: 2022

Recurrent Classifier Chains (RCCs) are a leading approach for multi-label classification as they directly model the interdependencies between classes. Unfortunately, existing RCCs assume that every training instance is completely labeled with all its ground truth classes. In practice often only a subset of an instance's labels are annotated, while the annotations for other classes aremissing. RCCs fail in this missing label scenario, predicting many false negatives and potentially missing important classes. In this work, we propose Robust-RCC, the first strategy for tackling this open problem of RCCs failing formulti-label missing-label data. Robust-RCC is a new type of deep recurrent classifier chain empowered to model inter-class relationships essential for predicting thecomplete label set most likely to match the ground truth. The key to Robust-RCC is the design of the Multi Incomplete Label Risk (MILR) function, which we prove to be equal in expectation to the true risk of the ground truth full label set despite being computed from incompletely labeled data. Our experimental study demonstrates that Robust-RCC consistently beats six state-of-of-the-art methods by as much as 30% in predicting the true labels.
RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer
Authors: Mogan Gim, Donghee Choi, Kana Maruyama, Jihun Choi, Hajung Kim, Donghyeon Park, Jaewoo Kang
Venue: CIKM
Year: 2022

We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. To perform this task, we developed RecipeMind, a food affinity score prediction model that quantifies the suitability of adding an ingredient to set of other ingredients. We constructed a large-scale dataset containing ingredient co-occurrence based scores to train and evaluate RecipeMind on food affinity score prediction. Deployed in recipe ideation, RecipeMind helps the user expand an initial set of ingredients by suggesting additional ingredients. Experiments and qualitative analysis show RecipeMind's potential in fulfilling its assistive role in cuisine domain.