OpenRaiser/Intern-Atlas huggingface dataset:
Detecting insider threats in a real corporate database of computer usage activity
Authors: Ted E. Senator, Henry G. Goldberg, Alex Memory, William T. Young, Brad Rees, Robert Pierce, Daniel Huang, Matthew Reardon, David A. Bader, Edmond Chow, Irfan A. Essa, Joshua Jones, Vinay Bettadapura, Duen Horng Chau, Oded Green, Oguz Kaya, Anita Zakrzewska, Erica Briscoe, Rudolph L. Mappus IV, Robert McColl, Lora Weiss, Thomas G. Dietterich, Alan Fern, Weng-Keen Wong, Shubhomoy Das, Andrew Emmott, Jed Irvine, Jay Yoon Lee, Danai Koutra, Christos Faloutsos, Daniel D. Corkill, Lisa Friedland, Amanda Gentzel, David D. Jensen
Venue: Conference KDD 2013
Year: 2013

This paper reports on methods and results of an applied research project by a team consisting of SAIC and four universities to develop, integrate, and evaluate new approaches to detect the weak signals characteristic of insider threats on organizations’ information systems. Our system combines structural and semantic information from a real corporate database of monitored activity on their users ’ computers to detect independently developed red team inserts of malicious insider activities. We have developed and applied multiple algorithms for anomaly detection based on suspected scenarios of malicious insider behavior, indicators of unusual activities, high-dimensional statistical patterns, temporal sequences, and normal graph evolution. Algorithms and representations for dynamic graph processing provide the ability to scale as needed for enterpriselevel deployments on real-time data streams. We have also developed a visual language for specifying combinations of features, baselines, peer groups, time periods, and algorithms to detect anomalies suggestive of instances of insider threat behavior. We defined over 100 data features in seven categories based on approximately 5.5 million actions per day from approximately 5,500 users. We have achieved area under the ROC curve values of up to 0.979 and lift values of 65 on the top 50 user-days identified on two months of real data.
Machine Learning Robustness, Fairness, and their Convergence
Authors: Jae-Gil Lee, Yuji Roh, Hwanjun Song, Steven Euijong Whang
Venue: Conference KDD 2021
Year: 2021

Responsible AI becomes critical where robustness and fairness must be satisfied together. Traditionally, the two topics have been studied by different communities for different applications. Robust training is designed for noisy or poisoned data where image data is typically considered. In comparison, fair training primarily deals with biased data where structured data is typically considered. Nevertheless, robust training and fair training are fundamentally similar in considering that both of them aim at fixing the inherent flaws of real-world data. In this tutorial, we first cover state-of-the-art robust training techniques where most of the research is on combating various label noises. In particular, we cover label noise modeling, robust training approaches, and real-world noisy data sets. Then, proceeding to the related fairness literature, we discuss pre-processing, in-processing, and post-processing unfairness mitigation techniques, depending on whether the mitigation occurs before, during, or after the model training. Finally, we cover the recent trend emerged to combine robust and fair training in two flavors: the former is to make the fair training more robust (i.e., robust fair training), and the latter is to consider robustness and fairness as two equals to incorporate them into a holistic framework. This tutorial is indeed timely and novel because the convergence of the two topics is increasingly common, but yet to be addressed in tutorials. The tutors have extensive experience publishing papers in top-tier machine learning and data mining venues and developing machine learning platforms.
Unifying explicit and implicit feedback for collaborative filtering
Authors: Nathan Nan Liu, Evan Wei Xiang, Min Zhao, Qiang Yang
Venue: Conference CIKM 2010
Year: 2010

Most collaborative filtering algorithms are based on certain statistical models of user interests built from either explicit feedback (eg: ratings, votes) or implicit feedback (eg: clicks, purchases). Explicit feedbacks are more precise but more difficult to collect from users while implicit feedbacks are much easier to collect though less accurate in reflecting user preferences. In the existing literature, separate models have been developed for either of these two forms of user feedbacks due to their heterogeneous representation. However in most real world recommended systems both explicit and implicit user feedback are abundant and could potentially complement each other. It is desirable to be able to unify these two heterogeneous forms of user feedback in order to generate more accurate recommendations. In this work, we developed matrix factorization models that can be trained from explicit and implicit feedback simultaneously. Experimental results of multiple datasets showed that our algorithm could effectively combine these two forms of heterogeneous user feedback to improve recommendation quality.
A Taxation Perspective for Fair Re-ranking
Authors: Chen Xu, Xiaopeng Ye, Wenjie Wang, Liang Pang, Jun Xu, Tat-Seng Chua
Venue: Conference SIGIR 2024
Year: 2024

Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure responsibility and ethics. The exploration of redistribution problems has a long history in economics, offering valuable insights for conceptualizing fair re-ranking as a taxation process. Such a formulation provides us with a fresh perspective to re-examine fair re-ranking and inspire the development of new methods. From a taxation perspective, we theoretically demonstrate that most previous fair re-ranking methods can be reformulated as an item-level tax policy. Ideally, a good tax policy should be effective and conveniently controllable to adjust ranking resources. However, both empirical and theoretical analyses indicate that the previous item-level tax policy cannot meet two ideal controllable requirements: (1) continuity, ensuring minor changes in tax rates result in small accuracy and fairness shifts; (2) controllability over accuracy loss, ensuring precise estimation of the accuracy loss under a specific tax rate. To overcome these challenges, we introduce a new fair re-ranking method named Tax-rank, which levies taxes based on the difference in utility between two items. Then, we efficiently optimize such an objective by utilizing the Sinkhorn algorithm in optimal transport. Upon a comprehensive analysis, Our model Tax-rank offers a superior tax policy for fair re-ranking, theoretically demonstrating both continuity and controllability over accuracy loss. Experimental results show that Tax-rank outperforms all state-of-the-art baselines in terms of effectiveness and efficiency on recommendation and advertising tasks.
MGMAE: Molecular Representation Learning by Reconstructing Heterogeneous Graphs with A High Mask Ratio
Authors: Jinjia Feng, Zhen Wang, Yaliang Li, Bolin Ding, Zhewei Wei, Hongteng Xu
Venue: Conference CIKM 2022
Year: 2022

Masked autoencoder (MAE), as an effective self-supervised learner for computer vision and natural language processing, has been recently applied to molecule representation learning. In this paper, we identify two issues in applying MAE to pre-train Transformer-based models on molecular graphs that existing works have ignored. (1) As only atoms are abstracted as tokens and then reconstructed, the chemical bonds are not decided in the decoded molecule, making molecules with different arrangements of the same atoms indistinguishable. (2) Although a high mask ratio that corresponds to a challenging reconstruction task has been proved beneficial in the vision domain, it cannot be trivially leveraged on molecular graphs as there is less redundancy of information in graph data. To resolve these issues, we propose a novel framework, Molecular Graph Mask AutoEncoder (MGMAE). As the first step in MGMAE, we transform each molecular graph into a heterogeneous atom-bond graph to fully use the bond attributes and design unidirectional position encoding for such graphs. Then we propose a hybrid masking mechanism that exploits the complementary nature between atoms' attributive and spatial features. Meanwhile, we compensate for the mask embedding by a dynamic aggregation representation that exploits the correlations between topologically adjacent tokens. As a result, MGMAE can reconstruct the masked atoms, the masked bonds, and the relative distance among atoms simultaneously, with a high mask ratio. We compare MGMAE with the state-of-the-art methods on various molecular benchmarks and show the competitiveness of MGMAE in both regression and classification tasks.
Type nanotheories: a framework for term comparison
Authors: John M. Prager, Sarah Luger, Jennifer Chu-Carroll
Venue: Conference CIKM 2007
Year: 2007

We present in this paper Type Nanotheories (TN), a framework for representing the knowledge necessary for performing similarity comparisons between pairs of terms of the same type. TN itself uses another methodology, namely Support Outcomes, which is also introduced. Many IR and NLP applications use redundancy as a factor to increase confidence, and TN-based comparisons can determine redundancy better than simple string comparisons. Results include a showing of a 14% increase in Confidence-Weighted Score for an end-to-end QA system and an up to 68% improvement over baseline in an answer-key equivalencing experiment.
Extracting collective probabilistic forecasts from web games
Authors: David M. Pennock, Steve Lawrence, Finn Årup Nielsen, C. Lee Giles
Venue: Conference KDD 2001
Year: 2001

Game sites on the World Wide Web draw people from around the world with specialized interests, skills, and knowledge. Data from the games often reflects the players' expertise and will to win. We extract probabilistic forecasts from data obtained from three online games: the Hollywood Stock Exchange (HSX), the Foresight Exchange (FX), and the Formula One Pick Six (F1P6) competition. We find that all three yield accurate forecasts of uncertain future events. In particular, prices of so-called "movie stocks" on HSX are good indicators of actual box office returns. Prices of HSX securities in Oscar, Emmy, and Grammy awards correlate well with observed frequencies of winning. FX prices are reliable indicators of future developments in science and technology. Collective predictions from players in the F1 competition serve as good forecasts of true race outcomes. In some cases, forecasts induced from game data are more reliable than expert opinions. We argue that web games naturally attract well-informed and well-motivated players, and thus offer a valuable and oft-overlooked source of high-quality data with significant predictive value.
Understanding the functions of business accounts on twitter
Authors: Ana-Maria Popescu, Alpa Jain
Venue: Conference WWW 2011
Year: 2011

This paper performs an initial exploration of business Twitter accounts in order to start understanding how businesses interact with their users and viceversa. We provide an analysis of business tweet types and topics and show that specific business tweet classes such as deals and events can be reliably identified for customer use.
Identifying entity aspects in microblog posts
Authors: Damiano Spina, Edgar Meij, Maarten de Rijke, Andrei Oghina, Minh Thuong Bui, Mathias Breuss
Venue: Conference SIGIR 2012
Year: 2012

Online reputation management is about monitoring and handling the public image of entities (such as companies) on the Web. An important task in this area is identifying "aspects" of the entity of interest (such as products, services, competitors, key people, etc.) given a stream of microblog posts referring to the entity. In this paper we compare different IR techniques and opinion target identification methods for automatically identifying aspects and find that (i) simple statistical methods such as TF.IDF are a strong baseline for the task, significantly outperforming opinion-oriented methods, and (ii) only considering terms tagged as nouns improves the results for all the methods analyzed.
Data Extraction from the Web Using Wild Card Queries
Authors: Davood Rafiei, Haobin Li
Venue: Conference CIKM 2009
Year: 2009

This paper presents an overview of our work for searching and retrieving facts and relationships within natural language text sources. In this work, an extraction task over a text collection is expressed as a query that combines text fragments with wild cards, and the query result is a set of facts in the form of unary, binary and general n-ary tuples. Despite being both simple and declarative, the framework can be applied to a wide range of extraction tasks. This paper presents an overview of the work and its various components. We also report some of our experiments and an evaluation of the proposed querying framework in extracting relevant information to a task.
A Robust Index for Regular Expression Queries
Authors: Dominic Tsang, Sanjay Chawla
Venue: Conference CIKM 2011
Year: 2011

The like regular expression predicate has been part of the SQL standard since at least 1989. However, despite its popularity and wide usage, database vendors provide only limited indexing support for regular expression queries which almost always require a full table scan.
Revisiting the divergence minimization feedback model
Authors: Yuanhua Lv, ChengXiang Zhai
Venue: Conference CIKM 2014
Year: 2014

Pseudo-relevance feedback (PRF) has proven to be an ef-fective strategy for improving retrieval accuracy. In this paper, we revisit a PRF method based on statistical lan-guage models, namely the divergence minimization model (DMM). DMM not only has apparently sound theoretical foundation, but also has been shown to satisfy most of the retrieval constraints. However, it turns out to perform sur-prisingly poorly in many previous experiments. We investi-gate the cause, and reveal that DMM inappropriately tackles the entropy of the feedback model, which generates highly skewed feedback model. To address this problem, we pro-pose a maximum-entropy divergence minimization model (MEDMM) by introducing an entropy term to regularize DMM. Our experiments on various TREC collections demon-strate that MEDMM not only works much better than DMM, but also outperforms several other state of the art PRF methods, especially on web collections. Moreover, unlike existing PRF models that have to be combined with the original query to perform well, MEDMM can work effec-tively even without being combined with the original query.
Exploring simultaneous keyword and key sentence extraction: improve graph-based ranking using wikipedia
Authors: Xun Wang, Lei Wang, Jiwei Li, Sujian Li
Venue: Conference CIKM 2012
Year: 2012

Summarization and Keyword Selection are two important tasks in NLP community. Although both aim to summarize the source articles, they are usually treated separately by using sentences or words. In this paper, we propose a two-level graph based ranking algorithm to generate summarization and extract keywords at the same time. Previous works have reached a consensus that important sentence is composed by important keywords. In this paper, we further study the mutual impact between them through context analysis. We use Wikipedia to build a two-level concept-based graph, instead of traditional term-based graph, to express their homogenous relationship and heterogeneous relationship. We run PageRank and HITS rank on the graph to adjust both homogenous and heterogeneous relationships. A more reasonable relatedness value will be got for key sentence selection and keyword selection. We evaluate our algorithm on TAC 2011 data set. Traditional term-based approach achieves a score of 0.255 in ROUGE-1 and a score of 0.037 and ROUGE-2 and our approach can improve them to 0.323 and 0.048 separately.
Overcoming browser cookie churn with clustering
Authors: Anirban Dasgupta, Maxim Gurevich, Liang Zhang, Belle L. Tseng, Achint Oommen Thomas
Venue: Conference WSDM 2012
Year: 2012

Many large Internet websites are accessed by users anonymously, without requiring registration or logging-in. However, to provide personalized service these sites build anonymous, yet persistent, user models based on repeated user visits. Cookies, issued when a web browser first visits a site, are typically employed to anonymously associate a website visit with a distinct user (web browser). However, users may reset cookies, making such association short-lived and noisy. In this paper we propose a solution to the cookie churn problem: a novel algorithm for grouping similar cookies into clusters that are more persistent than individual cookies. Such clustering could potentially allow more robust estimation of the number of unique visitors of the site over a certain long time period, and also better user modeling which is key to plenty of web applications such as advertising and recommender systems.
The demographics of web search
Authors: Ingmar Weber, Carlos Castillo
Venue: Conference SIGIR 2010
Year: 2010

How does the web search behavior of "rich" and "poor" people differ? Do men and women tend to click on difffferent results for the same query? What are some queries almost exclusively issued by African Americans? These are some of the questions we address in this study.
On the retrieval of wikipedia articles containing claims on controversial topics
Authors: Haggai Roitman, Shay Hummel, Ella Rabinovich, Benjamin Sznajder, Noam Slonim, Ehud Aharoni
Venue: Conference WWW 2016
Year: 2016

This work presents a novel claim-oriented document retrieval task. For a given controversial topic, relevant articles containing claims that support or contest the topic are retrieved from a Wikipedia corpus. For that, a two-step retrieval approach is proposed. At the first step, an initial pool of articles that are relevant to the topic are retrieved using state-of-the-art retrieval methods. At the second step, articles in the initial pool are re-ranked according to their potential to contain as many relevant claims as possible using several claim discovery features. Hence, the second step aims at maximizing the overall claim recall of the retrieval system. Using a recently published claims benchmark, the proposed retrieval approach is demonstrated to provide more relevant claims compared to several other retrieval alternatives.
The language of deceivers: Linguistic features of crowdfunding scams
Authors: Wafa Shafqat, Seunghun Lee, Sehrish Malik, Hyun-chul Kim
Venue: Conference WWW 2016
Year: 2016

Crowdfunding sites with recent explosive growth are equally attractive platforms for swindlers or scammers. Though the growing number of articles on crowdfunding scams indicate that the fraud threats are accelerating, there has been little knowledge on the scamming practices and patterns. The key contribution of this research is to discover the hidden clues in the text by exploring linguistic features to distinguish scam campaigns from non-scams. Our results indicate that by providing less information and writing more carefully (and less informally), scammers deliberately try to deceive people; (i) they use less number of words, verbs, and sentences in their campaign pages. (ii) scammers make less typographical errors, 4.5-4.7 times lower than non-scammers.(iii) Expressivity of scams is 2.6-8.5 times lower as well.
G2t: Generating fluent descriptions for knowledge graph
Authors: Yunzhou Shi, Zhiling Luo, Pengcheng Zhu, Feng Ji, Wei Zhou, Haiqing Chen, Yujiu Yang 0001
Venue: Conference SIGIR 2020
Year: 2020

Generating natural language descriptions for knowledge graph (KG) is an important category for intelligent writing. Recent models on this task substitute the sequence encoder in a commonly used encoder-decoder framework with a graph encoder. However, these models suffer from entity missing and repetition. In this paper, we propose a novel end-to-end generation model named G2T, which integrates a novel Graph Structure Enhanced Mechanism (GSEM) and a Copy Coverage Loss (CCL). Instead of just considering graph structure in the encoding phase in most existing methods, our GSEM fully utilizes graph structure in the decoding phase and helps to mitigate entity missing problem. Moreover, our CCL can further improve performance by avoiding generating repeated entities. With their help, our model is capable of generating fluent description for KG. The results of automatic and human evaluations show that our model outperforms the state-of-the-art models.
Substructure similarity measurement in chinese recipes
Authors: Liping Wang, Qing Li, Na Li, Guozhu Dong, Yu Yang
Venue: Conference WWW 2008
Year: 2008

Improving the precision of information retrieval has been a challenging issue on Chinese Web. As exemplified by Chinese recipes on the Web, it is not easy/natural for people to use keywords (e.g. recipe names) to search recipes, since the names can be literally so abstract that they do not bear much, if any, information on the underlying ingredients or cooking methods. In this paper, we investigate the underlying features of Chinese recipes, and based on workflow-like cooking procedures, we model recipes as graphs. We further propose a novel similarity measurement based on the frequent patterns, and devise an effective filtering algorithm to prune unrelated data so as to support efficient on-line searching. Benefiting from the characteristics of graphs, frequent common patterns can be mined from a cooking graph database. So in our prototype system called RecipeView, we extend the subgraph mining algorithm FSG to cooking graphs and combine it with our proposed similarity measurement, resulting in an approach that well caters for specific users' needs. Our initial experimental studies show that the filtering algorithm can efficiently prune unrelated cooking graphs without affecting the retrieval performance and the similarity measurement gets a relatively higher precision/recall against its counterparts.
Learning to generate correct numeric values in news headlines
Authors: Jui Chu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: Conference WWW 2020
Year: 2020

Motivated by the significant role of numeric values to convey concise and accurate information in news headlines, we focus the headline generation task on displaying correct numbers. We propose various ways to present the numeric values to the generative model. In the end, we come up with a simple but effective pre-train task to guide the generator to correctly process the values, which outperforms other base models even if the numbers in the headline are newly generated from the article.
Sebi regulation biography
Authors: Sathvik Sanjeev Buggana, Deepti Saravanan, Shravya Kanchi, Ujwal Narayan, Shivam Mangale, Lini T. Thomas, Kamalakar Karlapalem, Natraj Raman
Venue: Conference WWW 2022
Year: 2022

The Securities and Exchange Board of India is the regulatory body for securities and commodity market in India. A growing number of SEBI documents ranging from government regulations to legal case files are now available in the digital form. Advances in natural language processing and machine learning provide opportunities for extracting semantic insights from these documents. We present here a system that performs semantic processing of SEBI documents using state-of-the-art language models to produce enriched regulations containing timelines of amendments and cross references to legal case files.