OpenRaiser/Intern-Atlas huggingface dataset:
Privacy-preserving data exploration in genome-wide association studies
Authors: Aaron Johnson, Vitaly Shmatikov
Venue: Conference KDD 2013
Year: 2013

Genome-wide association studies (GWAS) have become a popular method for analyzing sets of DNA sequences in order to discover the genetic basis of disease. Unfortunately, statistics published as the result of GWAS can be used to identify individuals participating in the study. To prevent privacy breaches, even previously published results have been removed from public databases, impeding researchers' access to the data and hindering collaborative research. Existing techniques for privacy-preserving GWAS focus on answering specific questions, such as correlations between a given pair of SNPs (DNA sequence variations). This does not fit the typical GWAS process, where the analyst may not know in advance which SNPs to consider and which statistical tests to use, how many SNPs are significant for a given dataset, etc. We present a set of practical, privacy-preserving data mining algorithms for GWAS datasets. Our framework supports <i>exploratory</i> data analysis, where the analyst does not know a priori how many and which SNPs to consider. We develop privacy-preserving algorithms for computing the number and location of SNPs that are significantly associated with the disease, the significance of any statistical test between a given SNP and the disease, any measure of correlation between SNPs, and the block structure of correlations. We evaluate our algorithms on real-world datasets and demonstrate that they produce significantly more accurate results than prior techniques while guaranteeing differential privacy.
Predicting disk replacement towards reliable data centers
Authors: Mirela Madalina Botezatu, Ioana Giurgiu, Jasmina Bogojeska, Dorothea Wiesmann
Venue: Conference KDD 2016
Year: 2016

Disks are among the most frequently failing components in today's IT environments. Despite a set of defense mechanisms such as RAID, the availability and reliability of the system are still often impacted severely. In this paper, we present a highly accurate SMART-based analysis pipeline that can correctly predict the necessity of a disk replacement even 10-15 days in advance. Our method has been built and evaluated on more than 30000 disks from two major manufacturers, monitored over 17 months. Our approach employs statistical techniques to automatically detect which SMART parameters correlate with disk replacement and uses them to predict the replacement of a disk with even 98% accuracy.
Citybeat: Real-time social media visualization of hyper-local city data
Authors: Chaolun Xia, Raz Schwartz, Ke Eddie Xie, Adam Krebs, Andrew Langdon, Jeremy Ting, Mor Naaman
Venue: Conference WWW 2014
Year: 2014

With the increasing volume of location-annotated content from various social media platforms like Twitter, Instagram and Foursquare, we now have real-time access to people's daily documentation of local activities, interests and attention. In this demo paper, we present CityBeat, a real-time visualization of hyper-local social media content for cities. The main objective of CityBeat is to provide users -- with a specific focus on journalists -- with information about the city's ongoings, and alert them to unusual activities. The system collects a stream of geo-tagged photos as input, uses time series analysis and classification techniques to detect hyper-local events, and compute trends and statistics. The demo includes a visualization of this information that is designed to be installed on a large-screen in a newsroom, as an ambient display.
Real-time automatic deceit detection from involuntary facial expressions
Authors: Zhi Zhang, Vartika Singh, Thomas E. Slowe, Sergey Tulyakov, Venugopal Govindaraju
Venue: Conference CVPR 2007
Year: 2007

Being the most broadly used tool for deceit measurement, the polygraph is a limited method as it suffers from human operator subjectivity and the fact that target subjects are aware of the measurement, which invites the opportunity to alter their behavior or plan counter-measures in advance. The approach presented in this paper attempts to circumvent these problems by unobtrusively and automatically measuring several prior identified deceit indicators (DIs) based upon involuntary, so-called reliable facial expressions through computer vision analysis of image sequences in real time. Reliable expressions are expressions said by the psychology community to be impossible for a significant percentage of the population to convincingly simulate, without feeling a true inner felt emotion. The strategy is to detect the difference between those expressions which arise from internal emotion, implying verity, and those expressions which are simulated, implying deceit. First, a group of facial action units (AUs) related to the reliable expressions are detected based on distance and texture based features. The DIs then can be measured and finally a decision of deceit or verity will be made accordingly. The performance of this proposed approach is evaluated by its real time implementation for deceit detection.
Identifying opinion leaders in the blogosphere
Authors: Xiaodan Song, Yun Chi, Koji Hino, Belle L. Tseng
Venue: Conference CIKM 2007
Year: 2007

Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate them down to the masses, and thus influence the opinions and decisions of others by a fashion of word of mouth. Opinion leaders capture the most representative opinions in the social network, and consequently are important for understanding the massive and complex blogosphere. In this paper, we propose a novel algorithm called InfluenceRank to identify opinion leaders in the blogosphere. The InfluenceRank algorithm ranks blogs according to not only how important they are as compared to other blogs, but also how novel the information they can contribute to the network. Experimental results indicate that our proposed algorithm is effective in identifying influential opinion leaders.
Fusing generic objectness and visual saliency for salient object detection
Authors: Kai-Yueh Chang, Tyng-Luh Liu, Hwann-Tzong Chen, Shang-Hong Lai
Venue: Conference ICCV 2011
Year: 2011

We present a novel computational model to explore the relatedness of objectness and saliency, each of which plays an important role in the study of visual attention. The proposed framework conceptually integrates these two concepts via constructing a graphical model to account for their relationships, and concurrently improves their estimation by iteratively optimizing a novel energy function realizing the model. Specifically, the energy function comprises the objectness, the saliency, and the interaction energy, respectively corresponding to explain their individual regularities and the mutual effects. Minimizing the energy by fixing one or the other would elegantly transform the model into solving the problem of objectness or saliency estimation, while the useful information from the other concept can be utilized through the interaction term. Experimental results on two benchmark datasets demonstrate that the proposed model can simultaneously yield a saliency map of better quality and a more meaningful objectness output for salient object detection.
Graphscope: Parameter-free mining of large time-evolving graphs
Authors: Jimeng Sun, Christos Faloutsos, Spiros Papadimitriou, Philip S. Yu
Venue: Conference KDD 2007
Year: 2007

How can we find communities in dynamic networks of socialinteractions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity time-points in such streams of graphs, in an on-line, any-time fashion? We propose GraphScope, that addresses both problems, using information theoretic principles. Contrary to the majority of earlier methods, it needs no user-defined parameters. Moreover, it is designed to operate on large graphs, in a streaming fashion. We demonstrate the efficiency and effectiveness of our GraphScope on real datasets from several diverse domains. In all cases it produces meaningful time-evolving patterns that agree with human intuition.
Come-and-go patterns of group evolution: A dynamic model
Authors: Tianyang Zhang, Peng Cui, Christos Faloutsos, Yunfei Lu, Hao Ye, Wenwu Zhu, Shiqiang Yang
Venue: Conference KDD 2016
Year: 2016

How do social groups, such as Facebook groups and Wechat groups, dynamically evolve over time? How do people join the social groups, uniformly or with burst? What is the pattern of people quitting from groups? Is there a simple universal model to depict the come-and-go patterns of various groups?
Integrating community matching and outlier detection for mining evolutionary community outliers
Authors: Manish Gupta, Jing Gao, Yizhou Sun, Jiawei Han
Venue: Conference KDD 2012
Year: 2012

Temporal datasets, in which data evolves continuously, exist in a wide variety of applications, and identifying anomalous or outlying objects from temporal datasets is an important and challenging task. Different from traditional outlier detection, which detects objects that have quite different behavior compared with the other objects, temporal outlier detection tries to identify objects that have different evolutionary behavior compared with other objects. Usually objects form multiple communities, and most of the objects belonging to the same community follow similar patterns of evolution. However, there are some objects which evolve in a very different way relative to other community members, and we define such objects as evolutionary community outliers. This definition represents a novel type of outliers considering both temporal dimension and community patterns. We investigate the problem of identifying evolutionary community outliers given the discovered communities from two snapshots of an evolving dataset. To tackle the challenges of community evolution and outlier detection, we propose an integrated optimization framework which conducts outlier-aware community matching across snapshots and identification of evolutionary outliers in a tightly coupled way. A coordinate descent algorithm is proposed to improve community matching and outlier detection performance iteratively. Experimental results on both synthetic and real datasets show that the proposed approach is highly effective in discovering interesting evolutionary community outliers.
Simpler core vector machines with enclosing balls
Authors: Ivor W. Tsang, András Kocsor, James T. Kwok
Venue: Conference ICML 2007
Year: 2007

The core vector machine (CVM) is a recent approach for scaling up kernel methods based on the notion of minimum enclosing ball (MEB). Though conceptually simple, an efficient implementation still requires a sophisticated numerical solver. In this paper, we introduce the enclosing ball (EB) problem where the ball's radius is fixed and thus does not have to be minimized. We develop efficient (1 + e)-approximation algorithms that are simple to implement and do not require any numerical solver. For the Gaussian kernel in particular, a suitable choice of this (fixed) radius is easy to determine, and the center obtained from the (1 + e)-approximation of this EB problem is close to the center of the corresponding MEB. Experimental results show that the proposed algorithm has accuracies comparable to the other large-scale SVM implementations, but can handle very large data sets and is even faster than the CVM in general.
BOAT: Building auto-tuners with structured Bayesian optimization
Authors: Valentin Dalibard, Michael Schaarschmidt, Eiko Yoneki
Venue: Conference WWW 2017
Year: 2017

Due to their complexity, modern systems expose many configuration parameters which users must tune to maximize performance. Auto-tuning has emerged as an alternative in which a black-box optimizer iteratively evaluates configurations to find efficient ones. Unfortunately, for many systems, such as distributed systems, evaluating performance takes too long and the space of configurations is too large for the optimizer to converge within a reasonable time.
Bayesian budget feasibility with posted pricing
Authors: Eric Balkanski, Jason D. Hartline
Venue: Conference WWW 2016
Year: 2016

We consider the problem of budget feasible mechanism design proposed by Singer, but in a Bayesian setting. A principal has a public value for hiring a subset of the agents and a budget, while the agents have private costs for being hired. We consider both additive and submodular value functions of the principal. We show that there are simple, practical, ex post budget balanced posted pricing mechanisms that approximate the value obtained by the Bayesian optimal mechanism that is budget balanced only in expectation. A main motivating application for this work is crowdsourcing, e.g., on Mechanical Turk, where workers are drawn from a large population and posted pricing is standard. Our analysis methods relate to contention resolution schemes in submodular optimization of Vondràk et al. and the correlation gap analysis of Yan.
iHR: an online recruiting system for Xiamen Talent Service Center
Authors: Wenxing Hong, Lei Li, Tao Li, Wenfu Pan
Venue: Conference KDD 2013
Year: 2013

Online recruiting systems have gained immense attention in the wake of more and more job seekers searching jobs and enterprises finding candidates on the Internet. A critical problem in a recruiting system is how to maximally satisfy the desires of both job seekers and enterprises with reasonable recommendations or search results. In this paper, we investigate and compare various online recruiting systems from a product perspective. We then point out several key functions that help achieve a win-win situation between job seekers and enterprises for a successful recruiting system. Based on the observations and key functions, we design, implement and deploy a web-based application of recruiting system, named iHR, for Xiamen Talent Service Center. The system utilizes the latest advances in data mining and recommendation technologies to create a user-oriented service for a myriad of audience in job marketing community. Empirical evaluation and online user studies demonstrate the efficacy and effectiveness of our proposed system. Currently, iHR has been deployed at http://i.xmrc.com.cn/XMRCIntel.
Modeling and predicting learning behavior in moocs
Authors: Jiezhong Qiu, Jie Tang, Tracy Xiao Liu, Jie Gong, Chenhui Zhang, Qian Zhang, Yufei Xue
Venue: Conference WSDM 2016
Year: 2016

Massive Open Online Courses (MOOCs), which collect complete records of all student interactions in an online learning environment, offer us an unprecedented opportunity to analyze students' learning behavior at a very fine granularity than ever before.
Training agent for first-person shooter game with actor-critic curriculum learning
Authors: Yuxin Wu, Yuandong Tian
Venue: Conference ICLR 2017
Year: 2017

In this paper, we propose a novel framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom. Our framework combines the state-of-the-art reinforcement learning approach (Asynchronous Advantage Actor-Critic (A3C) model) with curriculum learning. Our model is simple in design and only uses game states from the AI side, rather than using opponents' information. On a known map, our agent won 10 out of the 11 attended games and the champion of Track1 in ViZDoom AI Competition 2016 by a large margin, 35\% higher score than the second place.
Finding a team of experts in social networks
Authors: Theodoros Lappas, Kun Liu, Evimaria Terzi
Venue: Conference KDD 2009
Year: 2009

Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the TEAM FORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the TEAM FORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results.
Sequence mining in categorical domains: incorporating constraints
Authors: Mohammed Javeed Zaki
Venue: Conference CIKM 2000
Year: 2000

We present cSPADE, an efficient algorithm for mining frequent sequences considering a variety of syntactic constraints. These take the form of length or width limitations on the sequences, minimum or maximum gap constraints on consecutive sequence elements, applying a time window on allowable sequences, incorporating item constraints, and finding sequences predictive of one or more classes, even rare ones. Our method is efficient and scalable. Experiments on a number of synthetic and real databases show the utility and performance of considering such constraints on the set of mined sequences.
On clustering heterogeneous social media objects with outlier links
Authors: Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang
Venue: Conference WSDM 2012
Year: 2012

The clustering of social media objects provides intrinsic understanding of the similarity relationships between documents, images, and their contextual sources. Both content and link structure provide important cues for an effective clustering algorithm of the underlying objects. While link information provides useful hints for improving the clustering process, it also contains a significant amount of noisy information. Therefore, a robust clustering algorithm is required to reduce the impact of noisy links. In order to address the aforementioned problems, we propose heterogeneous random fields to model the structure and content of social media networks. We design a probability measure on the social media networks which output a configuration of clusters that are consistent with both content and link structure. Furthermore, noisy links can also be detected, and their impact on the clustering algorithm can be significantly reduced. We conduct experiments on a real social media network and show the advantage of the method over other state-of-the-art algorithms.
Learning occlusion with likelihoods for visual tracking
Authors: Suha Kwak, Woonhyun Nam, Bohyung Han, Joon Hee Han
Venue: Conference ICCV 2011
Year: 2011

We propose a novel algorithm to detect occlusion for visual tracking through learning with observation likelihoods. In our technique, target is divided into regular grid cells and the state of occlusion is determined for each cell using a classifier. Each cell in the target is associated with many small patches, and the patch likelihoods observed during tracking construct a feature vector, which is used for classification. Since the occlusion is learned with patch likelihoods instead of patches themselves, the classifier is universally applicable to any videos or objects for occlusion reasoning. Our occlusion detection algorithm has decent performance in accuracy, which is sufficient to improve tracking performance significantly. The proposed algorithm can be combined with many generic tracking methods, and we adopt L1 minimization tracker to test the performance of our framework. The advantage of our algorithm is supported by quantitative and qualitative evaluation, and successful tracking and occlusion reasoning results are illustrated in many challenging video sequences.
A theoretical framework for learning from a pool of disparate data sources
Authors: Shai Ben-David, Johannes Gehrke, Reba Schuller
Venue: Conference KDD 2002
Year: 2002

Many enterprises incorporate information gathered from a variety of data sources into an integrated input for some learning task. For example, aiming towards the design of an automated diagnostic tool for some disease, one may wish to integrate data gathered in many different hospitals. A major obstacle to such endeavors is that different data sources may vary considerably in the way they choose to represent related data. In practice, the problem is usually solved by a manual construction of semantic mappings and translations between the different sources. Recently there have been attempts to introduce automated algorithms based on machine learning tools for the construction of such translations.In this work we propose a theoretical framework for making classification predictions from a collection of different data sources, without creating explicit translations between them. Our framework allows a precise mathematical analysis of the complexity of such tasks, and it provides a tool for the development and comparison of different learning algorithms. Our main objective, at this stage, is to demonstrate the usefulness of computational learning theory to this practically important area and to stimulate further theoretical and experimental research of questions related to this framework.
Exs: Explainable search using local model agnostic interpretability
Authors: Jaspreet Singh, Avishek Anand
Venue: Conference WSDM 2019
Year: 2019

Retrieval models in information retrieval are used to rank documents for typically under-specified queries. Today machine learning is used to learn retrieval models from click logs and/or relevance judgments that maximizes an objective correlated with user satisfaction. As these models become increasingly powerful and sophisticated, they also become harder to understand. Consequently, it is hard for to identify artifacts in training, data specific biases and intents from a complex trained model like neural rankers even if trained purely on text features. EXS is a search system designed specifically to provide its users with insight into the following questions: "What is the intent of the query according to the ranker?'', "Why is this document ranked higher than another?'' and "Why is this document relevant to the query?''. EXS uses a version of a popular posthoc explanation method for classifiers -- LIME, adapted specifically to answer these questions. We show how such a system can effectively help a user understand the results of neural rankers and highlight areas of improvement.
Modeling user consumption sequences
Authors: Austin R. Benson, Ravi Kumar, Andrew Tomkins
Venue: Conference WWW 2016
Year: 2016

We study sequences of consumption in which the same item may be consumed multiple times. We identify two macroscopic behavior patterns of repeated consumptions. First, in a given user's lifetime, very few items live for a long time. Second, the last consumptions of an item exhibit growing inter-arrival gaps consistent with the notion of increasing boredom leading up to eventual abandonment.
Prediction of sparse user-item consumption rates with Zero-Inflated Poisson regression
Authors: Moshe Lichman, Padhraic Smyth
Venue: Conference WWW 2018
Year: 2018

In this paper we address the problem of building user models that can predict the rate at which individuals consume items from a finite set, including items they have consumed in the past and items that are new. This combination of repeat and new item consumption is common in applications such as listening to music, visiting web sites, and purchasing products. We use zero-inflated Poisson (ZIP) regression models as the basis for our modeling approach, leading to a general framework for modeling user-item consumption rates over time. We show that these models are more flexible in capturing user behavior than alternatives such as well-known latent factor models based on matrix factorization. We compare the performance of ZIP regression and latent factor models on three different data sets involving music, restaurant reviews, and social media. The ZIP regression models are systematically more accurate across all three data sets and across different prediction metrics.