OpenRaiser/Intern-Atlas huggingface dataset:
Fuse: Full spectral clustering
Authors: Wei Ye, Sebastian Goebl, Claudia Plant, Christian Böhm
Venue: Conference KDD 2016
Year: 2016

Multi-scale data which contains structures at different scales of size and density is a big challenge for spectral clustering. Even given a suitable locally scaled affinity matrix, the first k eigenvectors of such a matrix still cannot separate clusters well. Thus, in this paper, we exploit the fusion of the cluster-separation information from all eigenvectors to achieve a better clustering result. Our method FUll Spectral ClustEring (FUSE) is based on Power Iteration (PI) and Independent Component Analysis (ICA). PI is used to fuse all eigenvectors to one pseudo-eigenvector which inherits all the cluster-separation information. To conquer the cluster-collision problem, we utilize PI to generate p (p > k) pseudo-eigenvectors. Since these pseudo-eigenvectors are redundant and the cluster-separation information is contaminated with noise, ICA is adopted to rotate the pseudo-eigenvectors to make them pairwise statistically independent. To let ICA overcome local optima and speed up the search process, we develop a self-adaptive and self-learning greedy search method. Finally, we select k rotated pseudo-eigenvectors (independent components) which have more cluster-separation information measured by kurtosis for clustering. Various synthetic and real-world data verifies the effectiveness and efficiency of our FUSE method.
Behavior-based community detection: Application to host assessment in enterprise information networks
Authors: Cheng Cao, Zhengzhang Chen, James Caverlee, Lu-An Tang, Chen Luo, Zhichun Li
Venue: Conference CIKM 2018
Year: 2018

Community detection in complex networks is a fundamental problem that attracts much attention across various disciplines. Previous studies have been mostly focusing on external connections between nodes (i.e., topology structure) in the network whereas largely ignoring internal intricacies (i.e., local behavior) of each node. A pair of nodes without any interaction can still share similar internal behaviors. For example, in an enterprise information network, compromised computers controlled by the same intruder often demonstrate similar abnormal behaviors even if they do not connect with each other. In this paper, we study the problem of community detection in enterprise information networks, where large-scale internal events and external events coexist on each host. The discovered host communities, capturing behavioral affinity, can benefit many comparative analysis tasks such as host anomaly assessment. In particular, we propose a novel community detection framework to identify behavior-based host communities in enterprise information networks, purely based on large-scale heterogeneous event data. We continue proposing an efficient method for assessing host's anomaly level by leveraging the detected host communities. Experimental results on enterprise networks demonstrate the effectiveness of our model.
Collaborative alert ranking for anomaly detection
Authors: Ying Lin, Zhengzhang Chen, Cheng Cao, Lu-An Tang, Kai Zhang, Wei Cheng, Zhichun Li
Venue: Conference CIKM 2018
Year: 2018

Given a large number of low-quality heterogeneous categorical alerts collected from an anomaly detection system, how to characterize the complex relationships between different alerts and deliver trustworthy rankings to end users? While existing techniques focus on either mining alert patterns or filtering out false positive alerts, it can be more advantageous to consider the two perspectives simultaneously in order to improve detection accuracy and better understand abnormal system behaviors. In this paper, we propose CAR, a collaborative alert ranking framework that exploits both temporal and content correlations from heterogeneous categorical alerts. CAR first builds a hierarchical Bayesian model to capture both short-term and long-term dependencies in each alert sequence. Then, an entity embedding-based model is proposed to learn the content correlations between alerts via their heterogeneous categorical attributes. Finally, by incorporating both temporal and content dependencies into a unified optimization framework, CAR ranks both alerts and their corresponding alert patterns. Our experiments-using both synthetic and real-world enterprise security alert data-show that CAR can accurately identify true positive alerts and successfully reconstruct the attack scenarios at the same time.
The face of quality in crowdsourcing relevance labels: Demographics, personality and labeling accuracy
Authors: Gabriella Kazai, Jaap Kamps, Natasa Milic-Frayling
Venue: Conference CIKM 2012
Year: 2012

Information retrieval systems require human contributed relevance labels for their training and evaluation. Increasingly such labels are collected under the anonymous, uncontrolled conditions of crowdsourcing, leading to varied output quality. While a range of quality assurance and control techniques have now been developed to reduce noise during or after task completion, little is known about the workers themselves and possible relationships between workers' characteristics and the quality of their work. In this paper, we ask how do the relatively well or poorly-performing crowds, working under specific task conditions, actually look like in terms of worker characteristics, such as demographics or personality traits. Our findings show that the face of a crowd is in fact indicative of the quality of their work.
Predicting user behavior in display advertising via dynamic collective matrix factorization
Authors: Sheng Li, Jaya Kawale, Yun Fu
Venue: Conference SIGIR 2015
Year: 2015

Conversion prediction and click prediction are two important and intertwined problems in display advertising, but existing approaches usually look at them in isolation. In this paper, we aim to predict the conversion response of users by jointly examining the past purchase behavior and the click response behavior. Additionally, we model the temporal dynamics between the click response and purchase activity into a unified framework. In particular, a novel matrix factorization approach named dynamic collective matrix factorization (DCMF) is proposed to address this problem. Our model considers temporal dynamics of post-click conversions and also takes advantages of the side information of users, advertisements, and items. Experiments on a real-world marketing dataset show that our model achieves significant improvements over several baselines.
Log2intent: Towards interpretable user modeling via recurrent semantics memory unit
Authors: Zhiqiang Tao, Sheng Li, Zhaowen Wang, Chen Fang, Longqi Yang, Handong Zhao, Yun Fu
Venue: Conference KDD 2019
Year: 2019

Modeling user behavior from unstructured software log-trace data is critical in providing personalized service (\emphe.g., cross-platform recommendation). Existing user modeling approaches cannot well handle the long-term temporal information in log data, or produce semantically meaningful results for interpreting user logs. To address these challenges, we propose a Log2Intent framework for interpretable user modeling in this paper. Log2Intent adopts a deep sequential modeling framework that contains a temporal encoder, a semantic encoder and a log action decoder, and it fully captures the long-term temporal information in user sessions. Moreover, to bridge the semantic gap between log-trace data and human language, a recurrent semantics memory unit (RSMU) is proposed to encode the annotation sentences from an auxiliary software tutorial dataset, and the output of RSMU is fed into the semantic encoder of Log2Intent. Comprehensive experiments on a real-world Photoshop log-trace dataset with an auxiliary Photoshop tutorial dataset demonstrate the effectiveness of the proposed Log2Intent framework over the state-of-the-art log-trace user modeling method in three different tasks, including log annotation retrieval, user interest detection and user next action prediction.
Predictive representations for policy gradient in pomdps
Authors: Abdeslam Boularias, Brahim Chaib-draa
Venue: Conference ICML 2009
Year: 2009

We consider the problem of estimating the policy gradient in Partially Observable Markov Decision Processes (POMDPs) with a special class of policies that are based on Predictive State Representations (PSRs). We compare PSR policies to Finite-State Controllers (FSCs), which are considered as a standard model for policy gradient methods in POMDPs. We present a general Actor-Critic algorithm for learning both FSCs and PSR policies. The critic part computes a value function that has as variables the parameters of the policy. These latter parameters are gradually updated to maximize the value function. We show that the value function is polynomial for both FSCs and PSR policies, with a potentially smaller degree in the case of PSR policies. Therefore, the value function of a PSR policy can have less local optima than the equivalent FSC, and consequently, the gradient algorithm is more likely to converge to a global optimal solution.
Tracking the evolution of congestion in dynamic urban road networks
Authors: Tarique Anwar, Chengfei Liu, Hai Le Vu, Md. Saiful Islam
Venue: Conference CIKM 2016
Year: 2016

The congestion scenario on a road network is often represented by a set of differently congested partitions having homogeneous level of congestion inside. Due to the changing traffic, these partitions evolve with time. In this paper, we propose a two-layer method to incrementally update the differently congested partitions from those at the previous time point in an efficient manner, and thus track their evolution. The physical layer performs low-level computations to incrementally update a set of small-sized road network building blocks, and the logical layer provides an interface to query the physical layer about the congested partitions. At each time point, the unstable road segments are identified and moved to their most suitable building blocks. Our experimental results on different datasets show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy.
Profiling entity matching benchmark tasks
Authors: Anna Primpeli, Christian Bizer
Venue: Conference CIKM 2020
Year: 2020

Entity matching is a central task in data integration which has been researched for decades. Over this time, a wide range of benchmark tasks for evaluating entity matching methods has been developed. This resource paper systematically complements, profiles, and compares 21 entity matching benchmark tasks. In order to better understand the specific challenges associated with different tasks, we define a set of profiling dimensions which capture central aspects of the matching tasks. Using these dimensions, we create groups of benchmark tasks having similar characteristics. Afterwards, we assess the difficulty of the tasks in each group by computing baseline evaluation results using standard feature engineering together with two common classification methods. In order to enable the exact reproducibility of evaluation results, matching tasks need to contain exactly defined sets of matching and non-matching record pairs, as well as a fixed development and test split. As this is not the case for some widely-used benchmark tasks, we complement these tasks with fixed sets of non-matching pairs, as well as fixed splits, and provide the resulting development and test sets for public download. By profiling and complementing the benchmark tasks, we support researchers to select challenging as well as diverse tasks and to compare matching systems on clearly defined grounds.
Machamp: A generalized entity matching benchmark
Authors: Jin Wang, Yuliang Li, Wataru Hirota
Venue: Conference CIKM 2021
Year: 2021

Entity Matching (EM) refers to the problem of determining whether two different data representations refer to the same real-world entity. It has been a long-standing interest of the data management community. Many efforts have been paid in creating benchmark tasks as well as in developing advanced matching techniques for EM. However, existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the tables in data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem - Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications. We summarize existing EM benchmark tasks for structured tables and conduct a series of processing and cleaning efforts to transform them into matching tasks between tables with different structures. Based on that, we further conduct comprehensive profiling of the proposed tasks and evaluate several popular entity matching approaches on them. With the help of Machamp, it is the first time that researchers can evaluate EM techniques between data collections with different structures. It is public available via link: https://github.com/megagonlabs/machamp.
Sliding cross entropy for self-knowledge distillation
Authors: Hanbeen Lee, Jeongho Kim, Simon S. Woo
Venue: Conference CIKM 2022
Year: 2022

Knowledge distillation (KD) is a powerful technique for improving the performance of a small model by leveraging the knowledge of a larger model. Despite its remarkable performance boost, KD has a drawback with the substantial computational cost of pre-training larger models in advance. Recently, a method called self-knowledge distillation has emerged to improve the model's performance without any supervision. In this paper, we present a novel plug-in approach called Sliding Cross Entropy (SCE) method, which can be combined with existing self-knowledge distillation to significantly improve the performance. Specifically, to minimize the difference between the output of the model and the soft target obtained by self-distillation, we split each softmax representation by a certain window size, and reduce the distance between sliced parts. Through this approach, the model evenly considers all the inter-class relationships of a soft target during optimization. The extensive experiments show that our approach is effective in various tasks, including classification, object detection, and semantic segmentation. We also demonstrate SCE consistently outperforms existing baseline methods.
Shedding light on the weather
Authors: Srinivasa G. Narasimhan, Shree K. Nayar
Venue: Conference CVPR 2003
Year: 2003

Virtually all methods in image processing and computer vision, for removing weather effects from images, assume single scattering of light by particles in the atmosphere. In reality, multiple scattering effects are significant. A common manifestation of multiple scattering is the appearance of glows around light sources in bad weather. Modeling multiple scattering is critical to understanding the complex effects of weather on images, and hence essential for improving the performance of outdoor vision systems. We develop a new physics-based model for the multiple scattering of light rays as they travel from a source to an observer. This model is valid for various weather conditions including fog, haze, mist and rain. Our model enables us to recover from a single image the shapes and depths of sources in the scene. In addition, the weather condition and the visibility of the atmosphere can be estimated. These quantities can, in turn, be used to remove the glows of sources to obtain a clear picture of the scene. Based on these results, we demonstrate that a camera observing a distant source can serve as a "visual weather meter". The model and techniques described in this paper can also be used to analyze scattering in other media, such as fluids and tissues. Therefore, in addition to vision in bad weather, our work has implications for medical and underwater imaging.
Hypercomplex graph collaborative filtering
Authors: Anchen Li, Bo Yang, Huan Huo, Farookh Khadeer Hussain
Venue: Conference WWW 2022
Year: 2022

Hypercomplex algebras are well-developed in the area of mathematics. Recently, several hypercomplex recommendation approaches have been proposed and yielded great success. However, two vital issues have not been well-considered in existing hypercomplex recommenders. First, these methods are only designed for specific and low-dimensional hypercomplex algebras (e.g., complex and quaternion algebras), ignoring the exploration and utilization of high-dimensional ones. Second, most recommenders treat every user-item interaction as an isolated data instance, without considering high-order collaborative relationships. To bridge these gaps, in this paper, we propose a novel recommendation framework named HyperComplex Graph Collaborative Filtering (HCGCF). To study the high-dimensional hypercomplex algebras, we introduce Cayley–Dickson construction which utilizes a recursive process to define hypercomplex algebras and their mathematical operations. Based on Cayley–Dickson construction, we devise a hypercomplex graph convolution operator to learn user and item representations. Specifically, the operator models both the neighborhood summary and interaction relations with neighbors in hypercomplex spaces, effectively exploiting the high-order connectivity in the user-item bipartite graph. To the best of our knowledge, it is the first time that Cayley-Dickson construction and graph convolution techniques have been explicitly discussed and used in hypercomplex recommender systems. Compared with several state-of-the-art recommender baselines, HCGCF achieves superior performance in both click-through rate prediction and top-K recommendation on three real-world datasets.
Redundancy-aware maximal cliques
Authors: Jia Wang, James Cheng, Ada Wai-Chee Fu
Venue: Conference KDD 2013
Year: 2013

Recent research efforts have made notable progress in improving the performance of (exhaustive) maximal clique enumeration (MCE). However, existing algorithms still suffer from exploring the huge search space of MCE. Furthermore, their results are often undesirable as many of the returned maximal cliques have large overlapping parts. This redundancy leads to problems in both computational efficiency and usefulness of MCE.
Hypothesis Testing and Statistically-sound Pattern Mining
Authors: Leonardo Pellegrina, Matteo Riondato, Fabio Vandin
Venue: Conference KDD 2019
Year: 2019

The availability of massive datasets has highlighted the need of computationally efficient and statistically-sound methods to extracts patterns while providing rigorous guarantees on the quality of the results, in particular with respect to false discoveries. In this tutorial we survey recent methods that properly combine computational and statistical considerations to efficiently mine statistically reliable patterns from large datasets. We start by introducing the fundamental concepts in statistical hypothesis testing, including conditional and unconditional tests, which may not be familiar to everyone in the data mining community. We then explain how the computational and statistical challenges in pattern mining have been tackled in different ways. Finally, we describe the application of these methods in areas such as market basket analysis, subgraph mining, social networks analysis, and cancer genomics.
Mcrapper: Monte-carlo rademacher averages for poset families and approximate pattern mining
Authors: Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato
Venue: Conference KDD 2020
Year: 2020

We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both statistically-significant functions (i.e., patterns) when the available data is seen as a sample from an unknown distribution, and approximations of collections of high-expectation functions (e.g., frequent patterns) when the available data is a small sample from a large dataset. This feature is a strong improvement over previously proposed solutions that could only achieve one of the two. MCRapper uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself. To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining. TFP-R gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power (recall) than existing methods offering the same guarantees. We evaluate MCRapper and TFP-R and show that they outperform the state-of-the-art for their respective tasks.
Multimodal automl for image, text and tabular data
Authors: Nick Erickson, Xingjian Shi, James Sharpnack, Alexander J. Smola
Venue: Conference KDD 2022
Year: 2022

Automated machine learning (AutoML) offers the promise of translating raw data into accurate predictions without the need for significant human effort, expertise, and manual experimentation. In this lecture-style tutorial, we demonstrate fundamental techniques that powers up multimodal AutoML. Different from most AutoML systems that focus on solving tabular tasks that contain categorical and numerical features, we consider supervised learning tasks on various types of data including tabular features, text, and image, as well as their combinations. Rather than technical descriptions of how individual ML models work, we emphasize how to best use models within an overall ML pipeline that takes in raw training data and outputs predictions for test data. A major focus of our tutorial is on automatically building and training deep learning models, which are powerful yet cumbersome to manage manually. Hardly any educational material describes their successful automation. Each topic covered in the tutorial is accompanied by a hands-on Jupyter notebook that implements best practices (which will be available on GitHub before and after the tutorial). Most of the code is adopted from AutoGluon (https://auto.gluon.ai/), a recent open-source AutoML toolkit that is both state-of-the-art and easy-to-use.
Warm Start for Parameter Selection of Linear Classifiers
Authors: Bo-Yu Chu, Chia-Hua Ho, Cheng-Hao Tsai, Chieh-Yen Lin, Chih-Jen Lin
Venue: Conference KDD 2015
Year: 2015

In linear classification, a regularization term effectively remedies the overfitting problem, but selecting a good regularization parameter is usually time consuming. We consider cross validation for the selection process, so several optimization problems under different parameters must be solved. Our aim is to devise effective warm-start strategies to efficiently solve this sequence of optimization problems. We detailedly investigate the relationship between optimal solutions of logistic regression/linear SVM and regularization parameters. Based on the analysis, we develop an efficient tool to automatically find a suitable parameter for users with no related background knowledge.