OpenRaiser/Intern-Atlas huggingface dataset:
Cross-domain Prototype Learning from Contaminated Faces via Disentangling Latent Factors
Authors: Meng Pang, Binghui Wang, Shengbo Chen, Yiu-ming Cheung, Rong Zou, Wei Huang
Venue: CIKM
Year: 2022

This paper focuses on an emerging challenging problem called heterogeneous prototype learning (HPL) across face domains-It aims to learn the variation-free target domain prototype for a contaminated input image from the source domain and meanwhile preserve the personal identity. HPL involves two coupled subproblems, i.e., domain transfer and prototype learning. To address the two subproblems in a unified manner, we advocate disentangling the prototype and domain factors in their respected latent feature spaces, and replace the latent source domain features with the target domain ones to generate the heterogeneous prototype. To this end, we propose a disentangled heterogeneous prototype learning framework, dubbed DisHPL, which consists of one encoder-decoder generator and two discriminators. The generator and discriminators play adversarial games such that the generator learns to embed the contaminated image into a prototype feature space only capturing identity information and a domain-specific feature space, as well as generating a realistic-looking heterogeneous prototype. The two discriminators aim to predict personal identities and distinguish between real prototypes versus fake generated prototypes in the source/target domain. Experiments on various heterogeneous face datasets validate the effectiveness of DisHPL.
Improving Graph-based Document-Level Relation Extraction Model with Novel Graph Structure
Authors: Seongsik Park, Dongkeun Yoon, Harksoo Kim
Venue: CIKM
Year: 2022

Document-level relation extraction is a natural language processing task for extracting relations among entities in a document. Compared with sentence-level relation extraction, there are more challenges to document-level relation extraction. To acquire mutual information among entities in a document, recent studies have designed mention-level graphs or improved pretrained language models based on co-occurrence or coreference information. However, these methods cannot utilize the anaphoric information of pronouns, which play an important role in document-level relation extraction. In addition, there is a possibility of losing lexical information of the relations among entities directly expressed in a sentence. To address this issue, we propose two novel graph structures: an anaphoric graph and a local-context graph. The proposed method outperforms the existing graph-based relation extraction method when applying the document-level relation extraction dataset, DocRED.
Observability of SQL Hints in Oracle
Authors: Krishna Kantikiran Pasupuleti, Dinesh Das, Satyanarayana R. Valluri, Mohamed Zaït
Venue: CIKM
Year: 2022

Observability is a critical requirement of increasingly complex and cloud-first data management systems. In most commercial databases, this relies on telemetry like logs, traces, and metrics, which helps to identify, mitigate, and resolve issues expeditiously. SQL monitoring tools, for example, can show how a query is performing. One area that has received comparatively less attention is the observability of the query optimizer whose inner workings are often shrouded in mystery. Optimizer traces can illuminate the plan selection process for a query, but they are comprehensible only to human experts and are not easily machine-parsable to remediate sub-optimal plans. Hints are directives that guide the optimizer toward specific directions. While hints can be used manually, they are often used by automatic SQL plan management tools that can quickly identify and resolve regressions by selecting alternate plans. It is important to know when input hints are inapplicable so that the tools can try other strategies. For example, a manual hint may have syntax errors, or an index in an automatic hint may have been accidentally dropped. In this paper, we describe the design and implementation of Oracle's hint observability framework which provides a comprehensive usage report of all hints, manual or otherwise, used to compile a query. The report, which is available directly in the execution plan in a human-understandable and machine-readable format, can be used to automate any necessary corrective actions. This feature is available in Oracle Autonomous Database 19c.
High Availability Framework and Query Fault Tolerance for Hybrid Distributed Database Systems
Authors: Krishna Kantikiran Pasupuleti, Boris Klots, Vijayakrishnan Nagarajan, Ananthakiran Kandukuri, Nipun Agarwal
Venue: CIKM
Year: 2022

Modern commercial database systems are increasingly evolving into a hybrid distributed system model where a primary database host system enlists the services of a loosely coupled secondary system that acts as an accelerator. Often the secondary system is a distributed system that can perform specific tasks massively parallelized with results fed back to the host database. Similar models can also be seen in architectures that separate compute from storage. As the scale of the system grows, failures of nodes become common, and the architectural goal is to recover the system with minimal disruption to the workload as seen by the user. This paper introduces a new framework that allows a host database to efficiently manage the availability of a massive secondary distributed system and describes a mechanism to achieve query fault tolerance at the primary database by transparently re-executing query (sub)plans on the secondary distributed system. The focus is on improving two important aspects of disruption ? downtime and transparency to the user. The proposed mechanisms achieve quick recovery, reduced duration of downtime and isolation of errors during query execution, thus improving execution transparency for the users.
Sub-Task Imputation via Self-Labelling to Train Image Moderation Models on Sparse Noisy Data
Authors: Indraneil Paul, Sumit Negi
Venue: CIKM
Year: 2022

E-commerce marketplaces protect shopper experience and trust at scale by deploying deep learning models trained on human annotated moderation data, for the identification and removal of advert imagery that does not comply with moderation policies (a.k.a. defective images). However, human moderation labels can be hard to source for smaller advert programs that target specific device types with separate formats or for recently launched locales with unique moderation policies. Additionally, the sourced labels can be noisy due to annotator biases or policy rules clubbing multiple types of transgressions into a single category. Therefore, training advert image moderation models necessitates an approach that can effectively improve the sample efficiency of training, weed out noise and discover latent moderation sub-labels in one go. Our work demonstrates the merits of automated sub-label discovery using self-labelling. We show that self-labelling approaches can be used to decompose an image moderation task into its hidden sub-tasks (corresponding to intercepting a single sub-label) in an unsupervised manner, thus helping with cases where the granularity of labels is inadequate. This enables us to bootstrap useful representations quickly, via low-capacity but fast-learning teacher models that each specialize in a single distinct sub-task of the main classification task. These sub-task specialists then distil their logits to a high-capacity but slow-learning generalist student model, thus allowing it to perform well on complex moderation tasks with relatively fewer labels than vanilla supervised training. We conduct all our experiments on the moderation of sexually explicit advert images (though this method can be utilized for any defect type) and show a sizeable improvement in NPV (+30.2% absolute gain) viz-a-viz regular supervised baselines at a 1% FPR level. A long-term A/B test of our deployed model shows a significant relative reduction (-45.6%) in the prevalence of such advertisements compared to the previously deployed model.
PLASMA: A Semantic Modeling Tool for Domain Experts
Authors: Alexander Paulus, Andreas Burgdorf, Tristan Langer, André Pomp, Tobias Meisen, Sebastian Pol
Venue: CIKM
Year: 2022

In recent years, Knowledge Graphs and Ontology-based Data Management have proven to be particularly effective in the efficient management and consolidation of heterogeneous data sources. In this context, semantic modeling has proven to be a useful approach for creating semantic data annotations. However, automatically generated semantic models usually need to be revised by a domain expert, who is often not familiar with semantic technologies. For addressing this issue, we propose the PLASMA semantic modeling tool, which aims at enabling domain experts to build semantic models from scratch or refine models created by automatic algorithms. We demonstrate the use of the tool and its user interface in two different semantic data management use cases for integrating smart city data in a public funded project, called City Dataspace, and for creating semantic models in an industrial use case at Siemens AG.
How Does the Crowd Impact the Model? A Tool for Raising Awareness of Social Bias in Crowdsourced Training Data
Authors: Periklis Perikleous, Andreas Kafkalias, Zenonas Theodosiou, Pinar Barlas, Evgenia Christoforou, Jahna Otterbacher, Gianluca Demartini, Andreas Lanitis
Venue: CIKM
Year: 2022

It is increasingly easy for interested parties to play a role in the development of predictive algorithms, with a range of available tools and platforms for building datasets, as well as for training and evaluating machine learning (ML) models. For this reason, it is essential to create awareness among practitioners on the ethical challenges, such as the presence of social bias in training data. We present RECANT (Raising Awareness of Social Bias in Crowdsourced Training Data), a tool that allows users to explore the behaviors of four biometric models -- predicting the gender and race, as well as the perceived attractiveness and trustworthiness, of the person depicted in an input image. These models have been trained on a crowdsourced dataset of passport-style people images, where crowd annotators described attributes of the images, and reported their own demographic characteristics. With RECANT, users can explore the correct and wrong predictions made by each model, when using different subsets of the data in training, based on annotator attributes. We present its features, along with sample exercises, as a hands-on tool for raising awareness of potential pitfalls in data practices surrounding ML.
Plotly.plus, an Improved Dataset for Visualization Recommendation
Authors: Luca Podo, Paola Velardi
Venue: CIKM
Year: 2022

Visualization recommendation is a novel and challenging field of study, whose aim is to provide non-expert users with automatic tools for insight discovery from data. Advances in this research area are hindered by the absence of reliable datasets on which to train the recommender systems. To the best of our knowledge, Plotly corpus is the only publicly available dataset, but as complained by many authors and discussed in this article, it contains many labeling errors, which greatly limits its usefulness. We release an improved version of the original dataset, named Plotly.plus, which we obtained through an automated procedure with minimal post-editing. In addition to a manual validation by a group of data science students, we demonstrate that when training two state-of-the-art abstract image classifiers on Plotly.plus, systems' performance improves more than twice as much as when the original dataset is used, showing that Plotly.plus facilitates the discovery of significant perceptual patterns.
X-Vision: Explainable Image Retrieval by Re-Ranking in Semantic Space
Authors: Sayantan Polley, Subhajit Mondal, Venkata Srinath Mannam, Kushagra Kumar, Subhankar Patra, Andreas Nürnberger
Venue: CIKM
Year: 2022

We present X-Vision, an explainable AI (XAI) driven image retrieval system based on a re-ranking approach to support non-expert users. We generate textual explanations such as, ''This image is similar to query image in color by Y%, shape by Z%'' along with visual explanations that compare image features. Besides the XAI goal of making AI systems transparent, we address the semantic gap between user's perception and model ranking, which arises in content based image retrieval (CBIR). We attempt to explain the notion of similarity in images in a query-by-example scenario, starting with relatively simple features such as color, texture, objects, background-foreground segments, moving to semantic representations learned from hidden layers of deep networks. The base retrieval model compares the query vector with other image feature vectors to create rankings. This result list is transferred to a semantic feature space that allows rule-based re-rankings. The core contribution of this work is a re-ranking algorithm for generating explanations. Our re-ranking improves retrieval performance (MAP) when compared with a base ranker, a random baseline, and recent CBIR baseline rankers on PASCAL VOC data. We evaluate XAI focused aspects of user trust in an eye-tracker based user study, we find that explanations supported users in the search process and understanding the notion of similarity.
CLNews: The First Dataset of the Chilean Social Outbreak for Disinformation Analysis
Authors: Eliana Providel, Daniel Toro, Fabián Riquelme, Marcelo Mendoza, Eduardo Puraivan
Venue: CIKM
Year: 2022

Disinformation is one of the main threats that loom on social networks. Detecting disinformation is not trivial and requires training and maintaining fact-checking teams, which is labor-intensive. Recent studies show that the propagation structure of claims and user messages allows a better understanding of rumor dynamics. Despite these findings, the availability of verified claims and structural propagation data is low. This paper presents a new dataset with Twitter claims verified by fact-checkers along with the propagation structure of retweets and replies. The dataset contains verified claims checked during the Chilean social outbreak, which allows for studying the phenomenon of disinformation during this crisis. We study propagation patterns of verified content in CLNews, showing differences between false rumors and other types of content. Our results show that false rumors are more persistent than the rest of verified contents, reaching more people than truthful news and presenting low barriers of readability to users. The dataset is fully available and helps understand the phenomenon of disinformation during social crises being one of the first of its kind to be released.
Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling
Authors: Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca
Venue: CIKM
Year: 2022

Recent approaches to behavioural user profiling employ Graph Neural Networks (GNNs) to turn users' interactions with a platform into actionable knowledge. The effectiveness of an approach is usually assessed with accuracy-based perspectives, where the capability to predict user features (such as gender or age) is evaluated. In this work, we perform a beyond-accuracy analysis of the state-of-the-art approaches to assess the presence of disparate impact and disparate mistreatment, meaning that users characterised by a given sensitive feature are unintentionally, but systematically, classified worse than their counterparts. Our analysis on two real-world datasets shows that different user profiling paradigms can impact fairness results. The source code and the preprocessed datasets are available at: https://github.com/erasmopurif/do_gnns_build_fair_models.
FwSeqBlock: A Field-wise Approach for Modeling Behavior Representation in Sequential Recommendation
Authors: Hao Qian, Qintong Wu, Minghao Li, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Lihong Gu, Jinjie Gu
Venue: CIKM
Year: 2022

Modeling users' historical behaviors is an essential task in many industrial recommender systems. The user interest representation, in previous works, is obtained through the following paradigm: concrete behaviors are firstly embedded as low-dimensional behavior representations, which are then aggregated conditioning on the target item for final user interest representation. Most existing researches focus on the aggregation process that explores the intrinsic structure of the behavior sequences. However, the quality of behavior representation is largely ignored. In this paper, we present a pluggable module, FwSeqBlock, to enhance the expressiveness of behavior representations. Specifically, FwSeqBlock introduces the multiplicative operation among users' historical behaviors and the target item, where a field memory unit is designed to dynamically identify the dominant features from the behavior sequence and filter out the noise. Extensive experiments validate that FwSeqBlock consistently generates higher-quality user representations compared with competitive methods. Besides, online A/B testing reports a 4.46% improvement in Click-Through Rate (CTR), confirming the effectiveness of the proposed method.
A Multi-Interest Evolution Story: Applying Psychology in Query-based Recommendation for Inferring Customer Intention
Authors: Yuqi Qin, Pengfei Wang, Biyu Ma, Zhe Zhang
Venue: CIKM
Year: 2022

The query-based recommendation now is becoming a basic research topic in the e-commerce scenario. Generally, given a query that a user typed, it aims to provide a set of items that the user may be interested in. In this task, the customer intention (i.e., browsing or purchase) is an important factor to configure the corresponding recommendation strategy for better shopping experiences (i.e., providing diverse items when the user prefers to browse or recommending specific items when detecting the user is willing to purchase). Though necessary, this is usually overlooked in previous works. In addition, the diversity and evolution of user interests also bring challenges to inferring user intentions correctly. In this paper, we propose a predecessor task to infer two important customer intentions, which are purchasing and browsing respectively, and we introduce a novel Psychological Intention Prediction Model (PIPM for short) to address this issue. Inspired by cognitive psychology, we first devise a multi-interest extraction module to adaptively extract interests from the user-item interaction sequence. After this, we design an interest evolution layer to model the evolution of the mined multiple interests. Finally, we aggregate all evolved multiple interests to infer users' intentions in his/her next visit. Extensive experiments are conducted on a large-scale Taobao industrial dataset. The results demonstrate that PIPM gains a significant improvement on AUC and GAUC than state-of-the-art baselines. Notably, PIPM has been deployed on the Taobao e-commerce platform and obtained over 10% improvement on PCTR.
Probabilistic Model Incorporating Auxiliary Covariates to Control FDR
Authors: Lin Qiu, Nils Murrugarra-Llerena, Vítor Silva, Lin Lin, Vernon M. Chinchilli
Venue: CIKM
Year: 2022

Controlling False Discovery Rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods rely on the test-level covariates while ignoring metrics about test-level covariates. This strategy may not be optimal for complex large-scale problems, where indirect relations often exist among test-level covariates and auxiliary metrics or covariates. We incorporate auxiliary covariates among test-level covariates in a deep Black-Box framework (named as NeurT-FDR) which boosts statistical power and controls FDR for multiple hypothesis testing. Our method parametrizes the test-level covariates as a neural network and adjusts the auxiliary covariates through a regression framework, which enables flexible handling of high-dimensional features as well as efficient end-to-end optimization. We show that NeurT-FDR makes substantially more discoveries in three real datasets compared to competitive baselines.
Probing the Robustness of Pre-trained Language Models for Entity Matching
Authors: Mehdi Akbarian Rastaghi, Ehsan Kamalloo, Davood Rafiei
Venue: CIKM
Year: 2022

The paradigm of fine-tuning Pre-trained Language Models (PLMs) has been successful in Entity Matching (EM). Despite their remarkable performance, PLMs exhibit tendency to learn spurious correlations from training data. In this work, we aim at investigating whether PLM-based entity matching models can be trusted in real-world applications where data distribution is different from that of training. To this end, we design an evaluation benchmark to assess the robustness of EM models to facilitate their deployment in the real-world settings. Our assessments reveal that data imbalance in the training data is a key problem for robustness. We also find that data augmentation alone is not sufficient to make a model robust. As a remedy, we prescribe simple modifications that can improve the robustness of PLM-based EM models. Our experiments show that while yielding superior results for in-domain generalization, our proposed model significantly improves the model robustness, compared to state-of-the-art EM models.
Flow-based Perturbation for Cause-effect Inference
Authors: Shaogang Ren, Ping Li
Venue: CIKM
Year: 2022

A new causal discovery method is introduced to solve the bivariate causal discovery problem. The proposed algorithm leverages the expressive power of flow-based models and tries to learn the complex relationship between two variables. Algorithms have been developed to infer the causal direction according to empirical perturbation errors obtained from an invertible flow-based function. Theoretical results as well as experimental studies are presented to verify the proposed approach. Empirical evaluations demonstrate that our proposed method could outperform baseline methods on both synthetic and real-world datasets.
From Known to Unknown: Quality-aware Self-improving Graph Neural Network For Open Set Social Event Detection
Authors: Jiaqian Ren, Lei Jiang, Hao Peng, Yuwei Cao, Jia Wu, Philip S. Yu, Lifang He
Venue: CIKM
Year: 2022

State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous success in social event detection tasks when restricted to a closed set of events. However, considering the large amount of data needed for training and the limited ability of a neural network in handling previously unknown data, it is hard for existing GNN-based methods to operate in an open set setting. To address this problem, we design a Quality-aware Self-improving Graph Neural Network (QSGNN) which extends the knowledge from known to unknown by leveraging the best of known samples and reliable knowledge transfer. Specifically, to fully exploit the labeled data, we propose a novel supervised pairwise loss with an additional orthogonal inter-class relation constraint to train the backbone GNN encoder. The learnt, already-known events further serve as strong reference bases for the unknown ones, which greatly prompts knowledge acquisition and transfer. When the model is generalized to unknown data, to ensure the effectiveness and reliability, we further leverage the reference similarity distribution vectors for pseudo pairwise label generation, selection and quality assessment. Following the diversity principle of active learning, our method selects diverse pair samples with the generated pseudo labels to fine-tune the GNN encoder. Besides, we propose a novel quality-guided optimization in which the contributions of pseudo labels are weighted based on consistency. Experimental results validate that our model achieves state-of-the-art results and extends well to unknown events.