OpenRaiser/Intern-Atlas huggingface dataset:
Intrusion as (anti)social communication: characterization and detection
Authors: Qi Ding, Natallia Katenka, Paul Barford, Eric D. Kolaczyk, Mark Crovella
Venue: Conference KDD 2012
Year: 2012

A reasonable definition of intrusion is: entering a community to which one does not belong. This suggests that in a network, intrusion attempts may be detected by looking for communication that does not respect community boundaries. In this paper, we examine the utility of this concept for identifying malicious network sources. In particular, our goal is to explore whether this concept allows a core-network operator using flow data to augment signature-based systems located at network edges. We show that simple measures of communities can be defined for flow data that allow a remarkably effective level of intrusion detection simply by looking for flows that do not respect those communities. We validate our approach using labeled intrusion attempt data collected at a large number of edge networks. Our results suggest that community-based methods can offer an important additional dimension for intrusion detection systems.
Meta learning shared hierarchies
Authors: Kevin Frans, Jonathan Ho, Xi Chen 0022, Pieter Abbeel, John Schulman
Venue: Conference ICLR 2018
Year: 2018

We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps. Specifically, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies. We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks. We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies. We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes. We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.
Incorporating heterogeneous information for personalized tag recommendation in social tagging systems
Authors: Wei Feng, Jianyong Wang
Venue: Conference KDD 2012
Year: 2012

A social tagging system provides users an effective way to collaboratively annotate and organize items with their own tags. A social tagging system contains heterogeneous information like users' tagging behaviors, social networks, tag semantics and item profiles. All the heterogeneous information helps alleviate the cold start problem due to data sparsity. In this paper, we model a social tagging system as a multi-type graph. To learn the weights of different types of nodes and edges, we propose an optimization framework, called OptRank. OptRank can be characterized as follows:(1) Edges and nodes are represented by features. Different types of edges and nodes have different set of features. (2) OptRank learns the best feature weights by maximizing the average AUC (Area Under the ROC Curve) of the tag recommender. We conducted experiments on two publicly available datasets, i.e., Delicious and Last.fm. Experimental results show that: (1) OptRank outperforms the existing graph based methods when only (user, tag, item) relation is available. (2) OptRank successfully improves the results by incorporating social network, tag semantics and item profiles.
Social media recommendation based on people and tags
Authors: Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, Erel Uziel
Venue: Conference SIGIR 2010
Year: 2010

We study personalized item recommendation within an enterprise social media application suite that includes blogs, bookmarks, communities, wikis, and shared files. Recommendations are based on two of the core elements of social media - people and tags. Relationship information among people, tags, and items, is collected and aggregated across different sources within the enterprise. Based on these aggregated relationships, the system recommends items related to people and tags that are related to the user. Each recommended item is accompanied by an explanation that includes the people and tags that led to its recommendation, as well as their relationships with the user and the item. We evaluated our recommender system through an extensive user study. Results show a significantly better interest ratio for the tag-based recommender than for the people-based recommender, and an even better performance for a combined recommender. Tags applied on the user by other people are found to be highly effective in representing that user's topics of interest.
Tag-based social interest discovery
Authors: Xin Li, Lei Guo, Yihong Eric Zhao
Venue: Conference WWW 2008
Year: 2008

The success and popularity of social network systems, such as del.icio.us, Facebook, MySpace, and YouTube, have generated many interesting and challenging problems to the research community. Among others, discovering social interests shared by groups of users is very important because it helps to connect people with common interests and encourages people to contribute and share more contents. The main challenge to solving this problem comes from the difficulty of detecting and representing the interest of the users. The existing approaches are all based on the online connections of users and so unable to identify the common interest of users who have no online connections.
Autotag: a collaborative approach to automated tag assignment for weblog posts
Authors: Gilad Mishne
Venue: Conference WWW 2006
Year: 2006

This paper describes AutoTag, a tool which suggests tags for weblog posts using collaborative filtering methods. An evaluation of AutoTag on a large collection of posts shows good accuracy; coupled with the blogger's final quality control, AutoTag assists both in simplifying the tagging process and in improving its quality.
Flickr tag recommendation based on collective knowledge
Authors: Börkur Sigurbjörnsson, Roelof van Zwol
Venue: Conference WWW 2008
Year: 2008

Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide additional contextual and semantical information. In this paper we investigate how we can assist users in the tagging phase. The contribution of our research is twofold. We analyse a representative snapshot of Flickr and present the results by means of a tag characterisation focussing on how users tags photos and what information is contained in the tagging. Based on this analysis, we present and evaluate tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo. The results of the empirical evaluation show that we can effectively recommend relevant tags for a variety of photos with different levels of exhaustiveness of original tagging.
Tag2word: Using tags to generate words for content based tag recommendation
Authors: Yong Wu, Yuan Yao, Feng Xu, Hanghang Tong, Jian Lu
Venue: Conference CIKM 2016
Year: 2016

Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.
Leveraging the crowd to detect and reduce the spread of fake news and misinformation
Authors: Jooyeon Kim, Behzad Tabibian, Alice Oh, Bernhard Schölkopf, Manuel Gomez-Rodriguez
Venue: Conference WSDM 2018
Year: 2018

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, CURB, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.
Detect rumors using time series of social context information on microblogging websites
Authors: Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, Kam-Fai Wong
Venue: Conference CIKM 2015
Year: 2015

Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state-of-the-art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.
Tracing fake-news footprints: Characterizing social media messages by how they propagate
Authors: Liang Wu, Huan Liu
Venue: Conference WSDM 2018
Year: 2018

When a message, such as a piece of news, spreads in social networks, how can we classify it into categories of interests, such as genuine or fake news? Classification of social media content is a fundamental task for social media mining, and most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to make it look like real news. To address this problem, this paper concentrates on modeling the propagation of messages in a social network. Specifically, we propose a novel approach, TraceMiner, to (1) infer embeddings of social media users with social network structures; and (2) utilize an LSTM-RNN to represent and classify propagation pathways of a message. Since content information is sparse and noisy on social media, adopting TraceMiner allows to provide a high degree of classification accuracy even in the absence of content information. Experimental results on real-world datasets show the superiority over state-of-the-art approaches on the task of fake news detection and news categorization.
Attentive moment retrieval in videos
Authors: Meng Liu, Xiang Wang, Liqiang Nie, Xiangnan He, Baoquan Chen, Tat-Seng Chua
Venue: Conference SIGIR 2018
Year: 2018

In the past few years, language-based video retrieval has attracted a lot of attention. However, as a natural extension, localizing the specific video moments within a video given a description query is seldom explored. Although these two tasks look similar, the latter is more challenging due to two main reasons: 1) The former task only needs to judge whether the query occurs in a video and returns an entire video, but the latter is expected to judge which moment within a video matches the query and accurately returns the start and end points of the moment. Due to the fact that different moments in a video have varying durations and diverse spatial-temporal characteristics, uncovering the underlying moments is highly challenging. 2) As for the key component of relevance estimation, the former usually embeds a video and the query into a common space to compute the relevance score. However, the later task concerns moment localization where not only the features of a specific moment matter, but the context information of the moment also contributes a lot. For example, the query may contain temporal constraint words, such as "first'', therefore need temporal context to properly comprehend them. To address these issues, we develop an Attentive Cross-Modal Retrieval Network. In particular, we design a memory attention mechanism to emphasize the visual features mentioned in the query and simultaneously incorporate their context. In the light of this, we obtain the augmented moment representation. Meanwhile, a cross-modal fusion sub-network learns both the intra-modality and inter-modality dynamics, which can enhance the learning of moment-query representation. We evaluate our method on two datasets: DiDeMo and TACoS. Extensive experiments show the effectiveness of our model as compared to the state-of-the-art methods.
Total recall: Automatic query expansion with a generative feature model for object retrieval
Authors: Ondrej Chum, James Philbin, Josef Sivic, Michael Isard, Andrew Zisserman
Venue: Conference ICCV 2007
Year: 2007

Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which has proven successful in achieving high precision at low recall. Unfortunately, feature detection and quantization are noisy processes and this can result in variation in the particular visual words that appear in different images of the same object, leading to missed results. In the text retrieval literature a standard method for improving performance is query expansion. A number of the highly ranked documents from the original query are reissued as a new query. In this way, additional relevant terms can be added to the query. This is a form of blind relevance feedback and it can fail if `outlier' (false positive) documents are included in the reissued query. In this paper we bring query expansion into the visual domain via two novel contributions. Firstly, strong spatial constraints between the query image and each result allow us to accurately verify each return, suppressing the false positives which typically ruin text-based query expansion. Secondly, the verified images can be used to learn a latent feature model to enable the controlled construction of expanded queries. We illustrate these ideas on the 5000 annotated image Oxford building database together with more than 1M Flickr images. We show that the precision is substantially boosted, achieving total recall in many cases.
Co-clustering based classification for out-of-domain documents
Authors: Wenyuan Dai, Gui-Rong Xue, Qiang Yang, Yong Yu
Venue: Conference KDD 2007
Year: 2007

In many real world applications, labeled data are in short supply. It often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled data from a related but different domain. Traditional machine learning is not able to cope well with learning across different domains. In this paper, we address this problem for a text-mining task, where the labeled data are under one distribution in one domain known as in-domain data, while the unlabeled data are under a related but different domain known as out-of-domain data. Our general goal is to learn from the in-domain and apply the learned knowledge to out-of-domain. We propose a co-clustering based classification (CoCC) algorithm to tackle this problem. Co-clustering is used as a bridge to propagate the class structure and knowledge from the in-domain to the out-of-domain. We present theoretical and empirical analysis to show that our algorithm is able to produce high quality classification results, even when the distributions between the two data are different. The experimental results show that our algorithm greatly improves the classification performance over the traditional learning algorithms.
Self-taught clustering
Authors: Wenyuan Dai, Qiang Yang 0001, Gui-Rong Xue, Yong Yu 0001
Venue: Conference ICML 2008
Year: 2008

This paper focuses on a new clustering task, called self-taught clustering. Self-taught clustering is an instance of unsupervised transfer learning, which aims at clustering a small collection of target unlabeled data with the help of a large amount of auxiliary unlabeled data. The target and auxiliary data can be different in topic distribution. We show that even when the target data are not sufficient to allow effective learning of a high quality feature representation, it is possible to learn the useful features with the help of the auxiliary data on which the target data can be clustered effectively. We propose a co-clustering based self-taught clustering algorithm to tackle this problem, by clustering the target and auxiliary data simultaneously to allow the feature representation from the auxiliary data to influence the target data through a common set of features. Under the new data representation, clustering on the target data can be improved. Our experiments on image clustering show that our algorithm can greatly outperform several state-of-the-art clustering methods when utilizing irrelevant unlabeled auxiliary data.
Cross-modality submodular dictionary learning for information retrieval
Authors: Fan Zhu, Ling Shao, Mengyang Yu
Venue: Conference CIKM 2014
Year: 2014

This paper addresses the problem of joint modeling of multimedia components in different media forms. We consider the information retrieval task across both text and image documents, which includes retrieving relevant images that closely match the description in a text query and retrieving text documents that best explain the content of an image query. A greedy dictionary construction approach is introduced for learning an isomorphic feature space, to which cross-modality data can be adapted while data smoothness is guaranteed. The proposed objective function consists of two reconstruction error terms for both modalities and a Maximum Mean Discrepancy (MMD) term that measures the cross-modality discrepancy. Optimization of the reconstruction terms and the MMD term yields a compact and modality-adaptive dictionary pair. We formulate the joint combinatorial optimization problem by maximizing variance reduction over a candidate signal set while constraining the dictionary size and coefficients' sparsity. By exploiting the submodularity and the monotonicity property of the proposed objective function, the optimization problem can be solved by a highly efficient greedy algorithm, and is guaranteed to be at least a (e - 1)=/e≈0.632- approximation to the optimum. The proposed method achieves state-of-the-art performance on the Wikipedia dataset.
Personalized top-n sequential recommendation via convolutional sequence embedding
Authors: Jiaxi Tang, Ke Wang
Venue: Conference WSDM 2018
Year: 2018

Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (\emph{Caser}) as a solution to address this requirement. The idea is to embed a sequence of recent items into an `image' in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public datasets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.
Learning hierarchical representation model for nextbasket recommendation
Authors: Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, Xueqi Cheng
Venue: Conference SIGIR 2015
Year: 2015

Next basket recommendation is a crucial task in market basket analysis. Given a user's purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items that the user most probably would like. Ideally, a good recommender should be able to explore the sequential behavior (i.e., buying one item leads to buying another next), as well as account for users' general taste (i.e., what items a user is typically interested in) for recommendation. Moreover, these two factors may interact with each other to influence users' next purchase. To tackle the above problems, in this paper, we introduce a novel recommendation approach, namely hierarchical representation model (HRM). HRM can well capture both sequential behavior and users' general taste by involving transaction and user representations in prediction. Meanwhile, the flexibility of applying different aggregation operations, especially nonlinear operations, on representations allows us to model complicated interactions among different factors. Theoretically, we show that our model subsumes several existing methods when choosing proper aggregation operations. Empirically, we demonstrate that our model can consistently outperform the state-of-the-art baselines under different evaluation metrics on real-world transaction data.
A Dynamic Recurrent Model for Next Basket Recommendation
Authors: Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
Venue: Conference SIGIR 2016
Year: 2016

Next basket recommendation becomes an increasing concern. Most conventional models explore either sequential transaction features or general interests of users. Further, some works treat users' general interests and sequential behaviors as two totally divided matters, and then combine them in some way for next basket recommendation. Moreover, the state-of-the-art models are based on the assumption of Markov Chains (MC), which only capture local sequential features between two adjacent baskets. In this work, we propose a novel model, Dynamic REcurrent bAsket Model (DREAM), based on Recurrent Neural Network (RNN). DREAM not only learns a dynamic representation of a user but also captures global sequential features among baskets. The dynamic representation of a specific user can reveal user's dynamic interests at different time, and the global sequential features reflect interactions of all baskets of the user over time. Experiment results on two public datasets indicate that DREAM is more effective than the state-of-the-art models for next basket recommendation.