OpenRaiser/Intern-Atlas huggingface dataset:
MODIMO: Workshop on Multi-Omics Data Integration for Modelling Biological Systems
Authors: Marco Beccuti, Vincenzo Bonnici, Rosalba Giugno
Venue: CIKM
Year: 2021

Multi-omics analysis aims at extracting previously uncovered biological knowledge by integrating information across multiple single-omic sources. Past approaches have focused on the simultaneous analysis of a small number of omic data sets. Current challenges face the problem of integrating multiple omic sources into a unified complex model, or of combining already available tools for two-by-two omics analyses and merging their outcomes. By doing so and leveraging integrated system-level knowledge, multi-omic approaches ought to enable the development of better qualitative and quantitative models for descriptive and predictive analyses. To move this area forward, new statistical and algorithmic frameworks are needed, for example for generalizing classical graph theory results to heterogeneous networks and applying them to diverse problems such as drug repurposing or understanding the immune response to infections. Thus, in short, this workshop aims at investigating novel methodologies for providing crucial insights into multi-omics data management, integration, and analysis in order to enable biological discoveries.
Influence Maximization With Co-Existing Seeds
Authors: Ruben Becker, Gianlorenzo D&apos, Angelo, Hugo Gilbert
Venue: CIKM
Year: 2021

In the classical influence maximization problem we aim to select a set of nodes, called seeds, to start an efficient information diffusion process. More precisely, the goal is to select seeds such that the expected number of nodes reached by the diffusion process is maximized. In this work we study a variant of this problem where an unknown (up to a probability distribution) set of nodes, referred to as co-existing seeds, joins in starting the diffusion process even if not selected. This setting allows to model that, in certain situations, some nodes are willing to act as "voluntary seeds'' even if not chosen by the campaign organizer. This may for example be due to the positive nature of the information campaign (e.g., public health awareness programs, HIV prevention, financial aid programs), or due to external social driving effects (e.g., nodes are friends of selected seeds in real life or in other social media). In this setting, we study two types of optimization problems. While the first one aims to maximize the expected number of reached nodes, the second one endeavors to maximize the expected increment in the number of reached nodes in comparison to a non-intervention strategy. The problems (particularly the second one) are motivated by cooperative game theory. For various probability distributions on co-existing seeds, we obtain several algorithms with approximation guarantees as well as hardness and hardness of approximation results. We conclude with experiments that demonstrate the usefulness of our approach when co-existing seeds exist.
MUFin'21: First International Workshop on Modelling Uncertainty in the Financial World
Authors: Srikanta Bedathur, Tanmoy Bhowmik, Nitendra Rajput, Karamjit Singh, Maneet Singh
Venue: CIKM
Year: 2021

Of many things, Covid-19 has provided a stark proof that uncertainty is real, and it is here to stay. Perhaps nothing is more sensitive to uncertainty than the Financial World. To couple with it, while Artificial Intelligence techniques are used to predict the future state of events, their performance is significantly impacted by disruptions not captured in the past. Unforeseen scenarios such as economy changes, variations in the customer behaviour, pandemics, recessions, and fraudulent transactions often result in unexpected behaviour of financial models, thus associating a level of uncertainty with them. It is thus imperative for the research community to explore, identify, analyze, and address such uncertainties in order to develop robust models applicable in real-world scenarios. To this effect, the International Workshop on Modelling Uncertainty in the Financial World 2021 (MUFin21) aims to bring academics and industry experts together to discuss on this important, timely and yet- unsolved area of modelling uncertainties in the financial world.
Beamer: An End-to-End Deep Learning Framework for Unifying Data Cleaning in DNN Model Training and Inference
Authors: Nifei Bi, Xiansen Chen, Chen Xu, Aoying Zhou
Venue: CIKM
Year: 2021

Deep learning has made extraordinary progress in the last few years, focusing on improving the accuracy and speed of standard deep learning benchmarks. Nevertheless, datasets in production environments are often messy, which makes data cleaning crucial for DNN model training and inference. Existing solutions that combine big data processing systems and deep learning systems to accomplish the data cleaning, DNN model training and inference are internally tied to one of Spark or Flink. However, Spark and Flink usually show different performance under batch and stream processing workloads. In order to employ Spark in batch training and Flink in streaming inference, existing solutions incur the burden of maintaining two data cleaning programs. In this demonstration, we showcase Beamer: an end-to-end deep learning framework for unifying the data cleaning program when employing Spark in training and Flink in inference, respectively.
Boosting Few-shot Abstractive Summarization with Auxiliary Tasks
Authors: Qiwei Bi, Haoyuan Li, Hanfang Yang
Venue: CIKM
Year: 2021

For summarization in niche domains, data is not enough to fine-tune the large pre-trained model. In order to alleviate the few-shot problem, we design several auxiliary tasks to assist the main task---abstractive summarization. In this paper, we employ BART as the base sequence-to-sequence model and incorporate the main and auxiliary tasks under the multi-task framework. We transform all the tasks in the format of machine reading comprehension [19]. Moreover, we utilize the task-specific adapter to effectively share knowledge across tasks and the adaptive weight mechanism to adjust the contribution of auxiliary tasks to the main task. Experiments show the effectiveness of our method for few-shot datasets. We also propose to firstly pre-train the model on unlabeled datasets, and the methods proposed in this paper can further improve the model performance.
Contrastive Curriculum Learning for Sequential User Behavior Modeling via Data Augmentation
Authors: Shuqing Bian, Wayne Xin Zhao, Kun Zhou, Jing Cai, Yancheng He, Cunxiang Yin, Ji-Rong Wen
Venue: CIKM
Year: 2021

Within online platforms, it is critical to capture the semantics of sequential user behaviors for accurately modeling user interests. However, dynamic characteristics and sparse behaviors make it difficult to train effective user representations for sequential user behavior modeling. Inspired by the recent progress in contrastive learning, we propose a novel Contrastive Curriculum Learning framework for producing effective representations for modeling sequential user behaviors. We make important technical contributions in two aspects, namely data quality and sample ordering. Firstly, we design a model-based data generator by generating high-quality samples confirming to users' attribute information. Given a target user, it can leverage the fused attribute semantics for generating more close-to-real sequences. Secondly, we propose a curriculum learning strategy to conduct contrastive learning via an easy-to-difficult learning process. The core component is a learnable difficulty evaluator, which can score augmented sequences, and schedule them in curriculums. Extensive results on both public and industry datasets demonstrate the effectiveness of our approach on downstream tasks.
Certification and Trade-off of Multiple Fairness Criteria in Graph-based Spam Detection
Authors: Kai Burkholder, Kenny Kwock, Yuesheng Xu, Jiaxin Liu, Chao Chen, Sihong Xie
Venue: CIKM
Year: 2021

Spamming reviews are prevalent in review systems to manipulate seller reputation and mislead customers. patterns to achieve state-of-the-art detection accuracy. The detection can influence a large number of real-world entities and it is ethical to treat different groups of entities as equally as possible. However, due to skewed distributions of the graphs, GNN can fail to meet diverse fairness criteria designed for different parties. We formulate linear systems of the input features and the adjacency matrix of the review graphs for the certification of multiple fairness criteria. When the criteria are competing, we relax the certification and design a multi-objective optimization (MOO) algorithm to explore multiple efficient trade-offs, so that no objective can be improved without harming another objective. We prove that the algorithm converges to a Pareto efficient solution using duality and the implicit function theorem. Since there can be exponentially many trade-offs of the criteria, we propose a data-driven stochastic search algorithm to approximate Pareto fronts consisting of multiple efficient trade-offs. Experimentally, we show that the algorithms converge to solutions that dominate baselines based on fairness regularization and adversarial training.
Uncertainty-Aware Self-Training for Semi-Supervised Event Temporal Relation Extraction
Authors: Pengfei Cao, Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao, Wei Bi
Venue: CIKM
Year: 2021

Extracting event temporal relations is an important task for natural language understanding. Many works have been proposed for supervised event temporal relation extraction, which typically requires a large amount of human-annotated data for model training. However, the data annotation for this task is very time-consuming and challenging. To this end, we study the problem of semi-supervised event temporal relation extraction. Self-training as a widely used semi-supervised learning method can be utilized for this problem. However, it suffers from the noisy pseudo-labeling problem. In this paper, we propose the use of uncertainty-aware self-training framework (UAST) to quantify the model uncertainty for coping with pseudo-labeling errors. Specifically, UAST utilizes (1) Uncertainty Estimation module to compute the model uncertainty for pseudo-labeling unlabeled data; (2) Sample Selection with Exploration module to select informative samples based on uncertainty estimates; and (3) Uncertainty-Aware Learning module to explicitly incorporate the model uncertainty into the self-training process. Experimental results indicate that our approach significantly outperforms previous state-of-the-art methods.
ScarceGAN: Discriminative Classification Framework for Rare Class Identification for Longitudinal Data with Weak Prior
Authors: Surajit Chakrabarty, Rukma Talwadker, Tridib Mukherjee
Venue: CIKM
Year: 2021

This paper introduces ScarceGAN which focuses on identification of extremely rare or scarce samples from multi-dimensional longitudinal telemetry data with small and weak label prior. We specifically address: (i) severe scarcity in positive class, stemming from both underlying organic skew in the data, as well as extremely limited labels; (ii) multi-class nature of the negative samples, with uneven density distributions and partially overlapping feature distributions; and (iii) massively unlabelled data leading to tiny and weak prior on both positive and negative classes, and possibility of unseen or unknown behavior in the unlabelled set, especially in the negative class. Although related to PU learning problems, we contend that knowledge (or lack of it) on the negative class can be leveraged to learn the compliment of it (i.e., the positive class) better in a semi-supervised manner. To this effect, ScarceGAN re-formulates semi-supervised GAN by accommodating weakly labelled multi- class negative samples and the available positive samples. It relaxes the supervised discriminator's constraint on exact differentiation be- tween negative samples by introducing a 'leeway' term for samples with noisy prior. We propose modifications to the cost objectives of discriminator, in supervised and unsupervised path as well as that of the generator. For identifying risky players in skill gaming, this formulation in whole gives us a recall of over 85% (~60% jump over vanilla semi-supervised GAN) on our scarce class with very minimal verbosity in the unknown space. Further ScarceGAN out- performs the recall benchmarks established by recent GAN based specialized models for the positive imbalanced class identification and establishes a new benchmark in identifying one of rare attack classes (0.09%) in the intrusion dataset from the KDDCUP99 challenge. We establish ScarceGAN to be one of new competitive benchmark frameworks in the rare class identification for longitudinal telemetry data.
Spectral Graph Attention Network with Fast Eigen-approximation
Authors: Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Somayeh Sojoudi, Junzhou Huang, Wenwu Zhu
Venue: CIKM
Year: 2021

Variants of Graph Neural Networks (GNNs) for representation learning have been proposed recently and achieved fruitful results in various fields. Among them, Graph Attention Network (GAT) first employs a self-attention strategy to learn attention weights for each edge in the spatial domain. However, learning the attentions over edges can only focus on the local information of graphs and greatly increases the computational costs. In this paper, we first introduce the attention mechanism in the spectral domain of graphs and present Spectral Graph Attention Network (SpGAT) that learns representations for different frequency components regarding weighted filters and graph wavelets bases. In this way, SpGAT can better capture global patterns of graphs in an efficient manner with much fewer learned parameters than that of GAT. Further, to reduce the computational cost of SpGAT brought by the eigen-decomposition, we propose a fast approximation variant SpGAT-Cheby. We thoroughly evaluate the performance of SpGAT and SpGAT-Cheby in semi-supervised node classification tasks and verify the effectiveness of the learned attentions in the spectral domain.
Geometric Heuristics for Transfer Learning in Decision Trees
Authors: Siddhesh Chaubal, Mateusz Rzepecki, Patrick K. Nicholson, Guangyuan Piao, Alessandra Sala
Venue: CIKM
Year: 2021

Motivated by a network fault detection problem, we study how recall can be boosted in a decision tree classifier, without sacrificing too much precision. This problem is relevant and novel in the context of transfer learning(TL), in which few target domain training samples are available. We define a geometric optimization problem for boosting the recall of a decision tree classifier, and show it is NP-hard. To solve it efficiently, we propose several near-linear time heuristics, and experimentally validate these heuristics in the context of TL. Our evaluation includes 7 public datasets, as well as 6 network fault datasets, and we compare our heuristics with several existing TL algorithms, as well as exact mixed integer linear programming(MILP) solutions to our optimization problem. We find that our heuristics boost recall in a manner similar to optimal MILP solutions, yet require several orders of magnitude less compute time. In many cases the F1 score of our approach is competitive, and often better, than other TL algorithms. Moreover, our approach can be used as a building block to apply transfer learning to more powerful ensemble methods, such as random forests.
Scalable Contrast Pattern Mining over Data Streams
Authors: Elaheh Alipour Chavary, Sarah M. Erfani, Christopher Leckie
Venue: CIKM
Year: 2021

Incremental contrast pattern mining (CPM) is an important task in various fields such as network traffic analysis, medical diagnosis, and customer behavior analysis. Due to increases in the speed and dimension of data streams, a major challenge for CPM is to deal with the huge number of generated candidate patterns. While there are some works on incremental CPM, their approaches are not scalable in dense and high dimensional data streams, and the problem of CPM over an evolving dataset is an open challenge. In this work we focus on extracting the most specific set of contrast patterns (CPs) to discover significant changes between two data streams. We devise a novel algorithm to extract CPs using previously mined patterns instead of generating all patterns in each window from scratch. Our experimental results on a wide variety of datasets demonstrate the advantages of our approach over the state of the art in terms of efficiency.
Adversarial Reprogramming of Pretrained Neural Networks for Fraud Detection
Authors: Lingwei Chen, Yujie Fan, Yanfang Ye
Venue: CIKM
Year: 2021

Machine learning models have been widely used for fraud detection, while developing and maintaining these models often suffers from significant limitations in terms of training data scarcity and constrained resources. To address these issues, in this paper, we leverage machine learning vulnerability to adversarial attacks, and design a novel model AdvRFD that Adversarially Reprograms an ImageNet classification neural network for Fraud Detection task. AdvRFD first embeds transaction features into a host image to construct new ImageNet data, and then learns a universal perturbation to be added to all inputs, such that the outputs of the pretrained model can be accordingly mapped to the final detection decisions for all transactions. Extensive experiments on two transaction datasets made over Ethereum and credit cards have demonstrated that AdvRFD is effective to detect fraud using limited data and resources.
Constructing Noise Free Economic Policy Uncertainty Index
Authors: Chung-Chi Chen, Hen-Hsen Huang, Yu-Lieh Huang, Hsin-Hsi Chen
Venue: CIKM
Year: 2021

The economic policy uncertainty (EPU) index is one of the important text-based indexes in finance and economics fields. The EPU indexes of more than 26 countries have been constructed to reflect the policy uncertainty on country-level economic environments and serve as an important economic leading indicator. The EPU indexes are calculated based on the number of news articles with some manually-selected keywords related to economic, uncertainty, and policy. We find that the keyword-based EPU indexes contain noise, which will influence their explainability and predictability. In our experimental dataset, over 40% of news articles with the selected keywords are not related to the EPU. Instead of using keywords only, our proposed models take contextual information into account and get good performance on identifying the articles unrelated to EPU. The noise free EPU index performs better than the keyword-based EPU index in both explainability and predictability.
Distilling Numeral Information for Volatility Forecasting
Authors: Chung-Chi Chen, Hen-Hsen Huang, Yu-Lieh Huang, Hsin-Hsi Chen
Venue: CIKM
Year: 2021

The volatility of stock price reflects the risk of stock and influences the risk of investor's portfolio. It is also a crucial part of pricing derivative securities. Researchers have paid their attention to predict the stock volatility with different kinds of textual data. However, most of them focus on using word information only. Few touch on capturing the numeral information in textual data, providing fine-grained clues for financial document understanding. In this paper, we present a novel dataset, ECNum, for understanding the numerals in the transcript of earnings conference calls. We propose a simple but efficient method, Numeral-Aware Model (NAM), for enhancing the capacity of numeral understanding of neural network models. We employ the distilled information in the stock volatility forecasting task and achieve the best performance compared to the previous works in short-term scenarios.
Robust Road Network Representation Learning: When Traffic Patterns Meet Traveling Semantics
Authors: Yile Chen, Xiucheng Li, Gao Cong, Zhifeng Bao, Cheng Long, Yiding Liu, Arun Kumar Chandran, Richard Ellison
Venue: CIKM
Year: 2021

In this work, we propose a robust road network representation learning framework called Toast, which comes to be a cornerstone to boost the performance of numerous demanding transport planning tasks. Specifically, we first propose a traffic context aware skip-gram module to incorporate auxiliary tasks of predicting the traffic context of a target road segment. Furthermore, we propose a trajectory-enhanced Transformer module that utilizes trajectory data to extract traveling semantics on road networks. Apart from obtaining effective road segment representations, this module also enables us to obtain the route representations. With these two modules, we can learn representations which can capture multi-faceted characteristics of road networks to be applied in both road segment based applications and trajectory based applications. Last, we design a benchmark containing four typical transport planning tasks to evaluate the usefulness of Toast and comprehensive experiments verify that Toast consistently outperforms the state-of-the-art baselines across all tasks.
MGNETS: Multi-Graph Neural Networks for Table Search
Authors: Zhiyu Chen, Mohamed Trabelsi, Jeff Heflin, Dawei Yin, Brian D. Davison
Venue: CIKM
Year: 2021

Table search aims to retrieve a list of tables given a user's query. Previous methods only consider the textual information of tables and the structural information is rarely used. In this paper, we propose to model the complex relations in the table corpus as one or more graphs and then utilize graph neural networks to learn representations of queries and tables. We show that the text-based table retrieval methods can be further improved by graph-based predictions which fuse multiple field-level information.
HetMAML: Task-Heterogeneous Model-Agnostic Meta-Learning for Few-Shot Learning Across Modalities
Authors: Jiayi Chen, Aidong Zhang
Venue: CIKM
Year: 2021

Most of existing gradient-based meta-learning approaches to few-shot learning assume that all tasks have the same input feature space. However, in the real world scenarios, there are many cases that the input structures of tasks can be different, that is, different tasks may vary in the number of input modalities or data types. Existing meta-learners cannot handle the heterogeneous task distribution (HTD) as there is not only global meta-knowledge shared across tasks but also type-specific knowledge that distinguishes each type of tasks. To deal with task heterogeneity and promote fast within-task adaptions for each type of tasks, in this paper, we propose HetMAML, a task-heterogeneous model-agnostic meta-learning framework, which can capture both the type-specific and globally shared knowledge and can achieve the balance between knowledge customization and generalization. Specifically, we design a multi-channel backbone module that encodes the input of each type of tasks into the same length sequence of modality-specific embeddings. Then, we propose a task-aware iterative feature aggregation network which can automatically take into account the context of task-specific input structures and adaptively project the heterogeneous input spaces to the same lower-dimensional embedding space of concepts. Our experiments on six task-heterogeneous datasets demonstrate that HetMAML successfully leverages type-specific and globally shared meta-parameters for heterogeneous tasks and achieves fast within-task adaptions for each type of tasks.