OpenRaiser/Intern-Atlas huggingface dataset:
Not all benignware are alike: Enhancing clean-label attacks on malware classifiers
Authors: Xutong Wang, Yun Feng, Bingsheng Bi, Yaqin Cao, Ze Jin, Xinyu Liu, Yuling Liu, Yunpeng Li
Venue: Conference WWW 2025
Year: 2025

Machine Learning (ML) based malware classifiers are vulnerable to exploitation during the training phase due to the necessity of regular retraining with samples collected from the wild. Recent studies have highlighted the efficacy of backdoor attacks in the malware domain, where attackers can manipulate the model during training by injecting samples embedded with specific triggers, causing the model to establish an association between the trigger and a designated class, thereby achieving evasion of detection. While research on backdoor attacks has been extensively explored in the field of computer vision, it has been largely overlooked in the malware domain. Unlike in the computer vision domain, the threat model in the malware domain typically restricts attackers to employing clean-label attacks (i.e., attackers do not have control over the labeling of poisoned data). However, clean-label attack methods are generally less effective compared to those that involve embedding triggers and altering sample labels to the target class (called corrupted-label attacks). To address this limitation, we propose a simple yet effective method that involves Poisoning Malware-Similar Benignware (PMSB) instead of random selection, thereby approximating the scenario of corrupted-label attacks and enhancing the effectiveness of clean-label attacks. Additionally, we introduce three similarity measurement methods based on feature-based distance, distribution-based distance, and contribution-based difference to select malware-similar benignware. Comprehensive evaluations across three different trigger types and three datasets demonstrate the superiority and general applicability of PMSB.
Absolute scale in structure from motion from a single vehicle mounted camera by exploiting nonholonomic constraints
Authors: Davide Scaramuzza 0001, Friedrich Fraundorfer, Marc Pollefeys, Roland Siegwart
Venue: Conference ICCV 2009
Year: 2009

In structure-from-motion with a single camera it is well known that the scene can be only recovered up to a scale. In order to compute the absolute scale, one needs to know the baseline of the camera motion or the dimension of at least one element in the scene. In this paper, we show that there exists a class of structure-from-motion problems where it is possible to compute the absolute scale completely automatically without using this knowledge, that is, when the camera is mounted on wheeled vehicles (e.g. cars, bikes, or mobile robots). The construction of these vehicles puts interesting constraints on the camera motion, which are known as “nonholonomic constraints”. The interesting case is when the camera has an offset to the vehicle's center of motion. We show that by just knowing this offset, the absolute scale can be computed with a good accuracy when the vehicle turns. We give a mathematical derivation and provide experimental results on both simulated and real data over a large image dataset collected during a 3 Km path. To our knowledge this is the first time nonholonomic constraints of wheeled vehicles are used to estimate the absolute scale. We believe that the proposed method can be useful in those research areas involving visual odometry and mapping with vehicle mounted cameras.
Concept-based biomedical text retrieval
Authors: Ming Zhong, Xiangji Huang
Venue: Conference SIGIR 2006
Year: 2006

One challenging problem for biomedical text retrieval is to find accurate synonyms or name variants for biomedical entities. In this paper, we propose a new concept-based approach to tackle this problem. In this approach, a set of concepts instead of keywords will be extracted from a query first. Then these concepts will be used for retrieval purpose. The experiment results show that the proposed approach can boost the retrieval performance and it generates very good results on 2005 TREC Genomics data sets.
Multidimensional Relevance Modeling via Psychometrics and Crowdsourcing
Authors: Yinglong Zhang, Jin Zhang, Matthew Lease, Jacek Gwizdka
Venue: Conference SIGIR 2014
Year: 2014

While many multidimensional models of relevance have been posited, prior studies have been largely exploratory rather than confirmatory. Lacking a methodological framework to quantify the relationships among factors or measure model fit to observed data, many past models could not be empirically tested or falsified. To enable more positivist experimentation, Xu and Chen [77] proposed a psychometric framework for multidimensional relevance modeling. However, we show their framework exhibits several methodological limitations which could call into question the validity of findings drawn from it. In this work, we identify and address these limitations, scale their methodology via crowdsourcing, and describe quality control methods from psychometrics which stand to benefit crowdsourcing IR studies in general. Methodology we describe for relevance judging is expected to benefit both human-centered and systems-centered IR.
Information search and re-access strategies of experienced web users
Authors: Anne Aula, Natalie Jhaveri, Mika Käki
Venue: Conference WWW 2005
Year: 2005

Experienced web users have strategies for information search and re-access that are not directly supported by web browsers or search engines. We studied how prevalent these strategies are and whether even experienced users have problems with searching and re-accessing information. With this aim, we conducted a survey with 236 experienced web users. The results showed that this group has frequently used key strategies (e.g., using several browser windows in parallel) that they find important, whereas some of the strategies that have been suggested in previous studies are clearly less important for them (e.g., including URLs on a webpage). In some aspects, such as query formulation, this group resembles less experienced web users. For instance, we found that most of the respondents had misconceptions about how their search engine handles queries, as well as other problems with information search and re-access. In addition to presenting the prevalence of the strategies and rationales for their use, we present concrete designs solutions and ideas for making the key strategies also available to less experienced users.
Scaling down distributed infrastructure on wimpy machines for personal Web archiving
Authors: Jimmy Lin
Venue: Conference WWW 2015
Year: 2015

Warcbase is an open-source platform for storing, managing, and analyzing web archives using modern "big data" infrastructure on commodity clusters---specifically, HBase for storage and Hadoop for data analytics. This paper describes an effort to scale "down" Warcbase onto a Raspberry Pi, an inexpensive single-board computer about the size of a deck of playing cards. Apart from an interesting technology demonstration, such a design presents new opportunities for personal web archiving, in enabling a low-cost, low-power, portable device that is able to continuously capture a user's web browsing history---not only the URLs of the pages that a user has visited, but the contents of those pages---and allowing the user to revisit any previously-encountered page, as it appeared at that time. Experiments show that data ingestion throughput and temporal browsing latency are adequate with existing hardware, which means that such capabilities are already feasible today.
Beyond Sessions: Exploiting Hybrid Contextual Information for Web Search
Authors: Jia Chen
Venue: Conference WSDM 2020
Year: 2020

It is essential to fully understand user intents for the optimization of downstream tasks such as document ranking and query suggestion in web search. As users tend to submit ambiguous queries, numer- ous studies utilize contextual information such as query sequence and user clicks for the auxiliary of user intent modeling. Most of these work adopted Recurrent Neural Network (RNN) based frame- works to encode sequential information within a session, which is hard to realize parallel computation. To this end, we plan to adopt attention-based units to generate context-aware representations for elements in sessions. As intra-session contexts are deficient for handling the data sparsity and cold-start problems in session search, we would also attempt to integrate cross-session dependen- cies by constructing session graphs on the whole corpus to enrich the representation of queries and documents.
Incorporating post-click behaviors into a click model
Authors: Feimin Zhong, Dong Wang, Gang Wang, Weizhu Chen, Yuchen Zhang, Zheng Chen, Haixun Wang
Venue: Conference SIGIR 2010
Year: 2010

Much work has attempted to model a user's click-through behavior by mining the click logs. The task is not trivial due to the well-known position bias problem. Some break-throughs have been made: two newly proposed click models, DBN and CCM, addressed this problem and improved document relevance estimation. However, to further improve the estimation, we need a model that can capture more sophisticated user behaviors. In particular, after clicking a search result, a user's behavior (such as the dwell time on the clicked document, and whether there are further clicks on the clicked document) can be highly indicative of the relevance of the document. Unfortunately, such measures have not been incorporated in previous click models. In this paper, we introduce a novel click model, called the post-click click model (PCC), which provides an unbiased estimation of document relevance through leveraging both click behaviors on the search page and post-click behaviors beyond the search page. The PCC model is based on the Bayesian approach, and because of its incremental nature, it is highly scalable to large scale and constantly growing log data. Extensive experimental results illustrate that the proposed method significantly outperforms the state of the art methods merely relying on click logs.
A novel method for stemmer generation based on hidden Markov models
Authors: Massimo Melucci, Nicola Orio
Venue: Conference CIKM 2003
Year: 2003

In this paper, we present a method based on Hidden Markov Models (HMMs) to generate statistical stemmers. Using a list of words as training set, the method estimates the HMM parameters which are used to calculate the most probable stem for an arbitrary word. Stemming is performed by computing the most probable path, through the HMM states, corresponding to the input word. Linguistic knowledge or a training set of manually stemmed words are not required. We describe the method and the results of the experiments carried out using standard test collections for five different languages.
Introduction to a new Farsi stemmer
Authors: Alireza Mokhtaripour, Saber Jahanpour
Venue: Conference CIKM 2006
Year: 2006

In this poster, a new Farsi (also called Persian) stemmer which works without dictionary is introduced. Evaluation results show significant improvement in performance (precision/recall) of the Information Retrieval (IR) system using this stemmer.
Understanding and Mitigating Bias in Online Health Search
Authors: Anat Hashavit, Hongning Wang, Raz Lin, Tamar Stern, Sarit Kraus
Venue: Conference SIGIR 2021
Year: 2021

Search engines are perceived as a reliable source for general information needs. However, finding the answer to medical questions using search engines can be challenging for an ordinary user. Content can be biased and results may present different opinions. In addition, interpreting medically related content can be difficult for users with no medical background. All of these can lead users to incorrect conclusions regarding health related questions.
Understanding mobile searcher attention with rich ad formats
Authors: Dmitry Lagun, Donal McMahon, Vidhya Navalpakkam
Venue: Conference CIKM 2016
Year: 2016

Mobile Search experiences have evolved significantly from a few blue links that require users to click. Recent search and ad units surface instant information to the user in a variety of visually rich formats that include images, horizontal swipes, and vertical scrolls. These innovative experiences call for new metrics and models to better understand searcher behavior on mobile phones. In this paper, we study how the presence of ads and their formats impacts searcher's gaze and satisfaction. We systematically vary presentation format of the sponsored result, while controlling for other factors, such as position and quality of organic results. We experiment with several configurations of text ad and rich ad formats. Our findings indicate that showing rich ad formats improve search experience, by drawing more attention to the information-rich ad and allowing users to interact to view more offers, which increases user satisfaction with search. In addition, we extend prior work by comparing the performance of various models to infer user's gaze from viewport data. Our models improve accuracy of existing viewport-based gaze inference methods by 30% in Pearson's correlation. Together, our findings show that viewport data can be used for fast, accurate and scalable measurement of user attention on a per-element basis, for both ads as well as organic search results.
Statistical significance, power, and sample sizes: A systematic review of SIGIR and TOIS, 2006-2015
Authors: Tetsuya Sakai
Venue: Conference SIGIR 2016
Year: 2016

We conducted a systematic review of 840 SIGIR full papers and 215 TOIS papers published between 2006 and 2015. The original objective of the study was to identify IR effectiveness experiments that are seriously underpowered (i.e., the sample size is far too small so that the probability of missing a real difference is extremely high) or overpowered (i.e., the sample size is so large that a difference will be considered statistically significant even if the actual effect size is extremely small). However, it quickly became clear to us that many IR effectiveness papers either lack significance testing or fail to report p-values and/or test statistics, which prevents us from conducting power analysis. Hence we first report on how IR researchers (fail to) report on significance test results, what types of tests they use, and how the reporting practices may have changed over the last decade. From those papers that reported enough information for us to conduct power analysis, we identify extremely overpowered and underpowered experiments, as well as appropriate sample sizes for future experiments. The raw results of our systematic survey of 1,055 papers and our R scripts for power analysis are available online. Our hope is that this study will help improve the reporting practices and experimental designs of future IR effectiveness studies.
Stochastic simulation of test collections: Evaluation scores
Authors: Julián Urbano, Thomas Nagler
Venue: Conference SIGIR 2018
Year: 2018

Part of Information Retrieval evaluation research is limited by the fact that we do not know the distributions of system effectiveness over the populations of topics and, by extension, their true mean scores. The workaround usually consists in resampling topics from an existing collection and approximating the statistics of interest with the observations made between random subsamples, as if one represented the population and the other a random sample. However, this methodology is clearly limited by the availability of data, the impossibility to control the properties of these data, and the fact that we do not really measure what we intend to. To overcome these limitations, we propose a method based on vine copulas for stochastic simulation of evaluation results where the true system distributions are known upfront. In the basic use case, it takes the scores from an existing collection to build a semi-parametric model representing the set of systems and the population of topics, which can then be used to make realistic simulations of the scores by the same systems but on random new topics. Our ability to simulate this kind of data not only eliminates the current limitations, but also offers new opportunities for research. As an example, we show the benefits of this approach in two sample applications replicating typical experiments found in the literature. We provide a full R package to simulate new data following the proposed method, which can also be used to fully reproduce the results in this paper.
TARexp: A Python Framework for Technology-Assisted Review Experiments
Authors: Eugene Yang, David D. Lewis
Venue: Conference SIGIR 2022
Year: 2022

Technology-assisted review (TAR) is an important industrial application of information retrieval (IR) and machine learning (ML). While a small TAR research community exists, the complexity of TAR software and workflows is a major barrier to entry. Drawing on past open source TAR efforts, as well as design patterns from the IR and ML open source software, we present an open source Python framework for conducting experiments on TAR algorithms. Key characteristics of this framework are declarative representations of workflows and experiment plans, the ability for components to play variable numbers of workflow roles, and state maintenance and restart capabilities. Users can draw on reference implementations of standard TAR algorithms while incorporating novel components to explore their research interests. The framework is available at https://github.com/eugene-yang/tarexp.
Improving top-k recommendation via jointcollaborative autoencoders
Authors: Ziwei Zhu, Jianling Wang, James Caverlee
Venue: Conference WWW 2019
Year: 2019

In this paper, we propose a Joint Collaborative Autoencoder framework that learns both user-user and item-item correlations simultaneously, leading to a more robust model and improved top-K recommendation performance. More specifically, we show how to model these user-item correlations and demonstrate the importance of careful normalization to alleviate the influence of feedback heterogeneity. Further, we adopt a pairwise hinge-based objective function to maximize the top-K precision and recall directly for top-K recommenders. Finally, a mini-batch optimization algorithm is proposed to train the proposed model. Extensive experiments on three public datasets show the effectiveness of the proposed framework over state-of-the-art non-neural and neural alternatives.
Modeling Latent Autocorrelation for Session- Based Recommendation
Authors: Xianghong Xu, Kai Ouyang, Liuyin Wang, Jiaxin Zou, Yanxiong Lu, Hai-Tao Zheng, Hong-Gee Kim
Venue: Conference CIKM 2022
Year: 2022

Session-based Recommendation (SBR) aims to predict the next item for the current session, which consists of several clicked items in a short period by an anonymous user. Most of the sequential modeling approaches to SBR are focusing on adopting advanced Deep Neural Networks (DNNs), and these methods require increasingly longer training times. Existing studies have shown that some traditional SBR methods can outperform some DNN-based sequential models, however, few studies have attempted to investigate the effectiveness of traditional methods in recent years. In this paper, we propose a novel and concise SBR model inspired by the basic concept of autocorrelation in the Stochastic Process. Autocorrelation measures the correlation of a process at different moments. Therefore, it is natural to use it to model the correlation of clicked item sequences at different time shifts. Specifically, we use Fast Fourier Transforms (FFT) to compute the autocorrelation and combine it with several linear transformations to enhance the session representation. By this means, our proposed method can learn better session preferences and is more efficient than most DNN-based models. Extensive experiments on two public datasets show that the proposed method outperforms state-of-the-art models in both effectiveness and efficiency.
Predicting entity popularity to improve spoken entity recognition by virtual assistants
Authors: Christophe Van Gysel, Manos Tsagkias, Ernest Pusateri, Ilya Oparin
Venue: Conference SIGIR 2020
Year: 2020

We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries. We introduce a method that uses historical user interactions to forecast which entities will gain in popularity and become trending, and it subsequently integrates the predictions within the Automated Speech Recognition (ASR) component of the VA. Experiments show that our proposed approach results in a 20% relative reduction in errors on emerging entity name utterances without degrading the overall recognition quality of the system.
Elecrec: Training sequential recommenders as discriminators
Authors: Yongjun Chen, Jia Li, Caiming Xiong
Venue: Conference SIGIR 2022
Year: 2022

Sequential recommendation is often considered as a generative task, i.e., training a sequential encoder to generate the next item of a user's interests based on her historical interacted items. Despite their prevalence, these methods usually require training with more meaningful samples to be effective, which otherwise will lead to a poorly trained model. In this work, we propose to train the sequential recommenders as discriminators rather than generators. Instead of predicting the next item, our method trains a discriminator to distinguish if a sampled item is a 'real' target item or not. A generator, as an auxiliary model, is trained jointly with the discriminator to sample plausible alternative next items and will be thrown out after training. The trained discriminator is considered as the final SR model and denoted as \modelname. Experiments conducted on four datasets demonstrate the effectiveness and efficiency of the proposed approach.
Leveraging ratings and reviews with gating mechanism for recommendation
Authors: Haifeng Xia, Zengmao Wang, Bo Du, Lefei Zhang, Shuai Chen, Gang Chun
Venue: Conference CIKM 2019
Year: 2019

Recommender system plays an important role to provide people with personalized information based on their history records. However, it is still a challenge to capture the preference of users accurately due to the sparsity of rating data and the heterogeneity of review data. In this paper, we propose a hybrid deep collaborative filtering model that jointly learns latent representations from ratings and reviews. Specifically, the model learns the rating feature and textual feature based on ratings and reviews simultaneously. Two embedding layers are employed to learn rating feature for users and items based on the user and item interactions, and two attention-based GRU networks learn context-aware representation from user and item reviews. Then a gating mechanism is used to leverage contributions from rating feature and textual feature. Experimental results on six real-world datasets demonstrate the superior performance of the proposed method over several state-of-the-art methods. Moreover, the keywords in reviews can be highlighted to interpret the predictions with the attention mechanism.
A Document-Centric Approach to Static Index Pruning in Text Retrieval Systems
Authors: Stefan Büttcher, Charles L. A. Clarke
Venue: Conference CIKM 2006
Year: 2006

We present a static index pruning method, to be used in ad-hoc document retrieval tasks, that follows a document-centric approach to decide whether a posting for a given term should remain in the index or not. The decision is made based on the term's contribution to the document's Kullback-Leibler divergence from the text collection's global language model. Our technique can be used to decrease the size of the index by over 90%, at only a minor decrease in retrieval effectiveness. It thus allows us to make the index small enough to fit entirely into the main memory of a single PC, even for large text collections containing millions of documents. This results in great efficiency gains, superior to those of earlier pruning methods, and an average response time around 20 ms on the GOV2 document collection.