OpenRaiser/Intern-Atlas huggingface dataset:
Attribute-enhanced dual channel representation learning for session-based recommendation
Authors: Qian Chen, Jianjun Li, Zhiqiang Guo, Guohui Li, Zhiying Deng
Venue: Conference CIKM 2023
Year: 2023

Session-based recommendation (SBR) aims to predict the anonymous user's next-click items by modeling the short-term sequence pattern. As most existing SBR models generally generate item representations based only on information propagation over the short sequence while ignoring additional valuable knowledge, their expressive abilities are somewhat limited by data sparsity caused by short sequence. Though there have been some attempts on utilizing items' attributes, they basically embed attributes into items directly, ignoring the fact that 1) there is no contextual relationship among attributes; and 2) users have varying levels of attention to different attributes, which still leads to unsatisfactory performance. To tackle the issues, we propose a novel Attribute-enhanced Dual Channel Representation Learning (ADRL) model for SBR, in which we independently model session representations in attribute-related pattern and sequence-related pattern. Specifically, we learn session representations with sequence patterns from the session graph, and we further design an frequency-driven attribute aggregator to generate the attribute-related session representations within a session. The proposed attribute aggregator is plug-and-play, as it can be coupled with most existing SBR models. Extensive experiments on three real-world public datasets demonstrate the superiority of the proposed ADRL over several state-of-the-art baselines, as well as the effectiveness and efficiency of our attribute aggregator module.
Learning the dynamics in sequential recommendation by exploiting real-time information
Authors: Rujiao Zhang, Hao Zhang, Yucong Luo, Zhiding Liu, Mingyue Cheng, Qi Liu, Enhong Chen
Venue: Conference CIKM 2024
Year: 2024

Sequential recommender systems offer personalized suggestions by modeling users' interactions chronologically to capture dynamic user interest. Existing approaches typically fail to adequately describe the dynamics of the entire recommender system, including shifts in both user interest and item availability. To address this, we propose a simple yet effective framework with three key perspectives, tailored to the dynamics of recommender system by fully exploiting the time information. Firstly, we propose a dynamic candidate set construction approach to prevent the model from learning future interactions. Secondly, assuming that user behaviors remain consistent over short terms but may evolve over long terms, we employ a interval-weighted optimization target to model the correlation of users' historical interactions. Finally, we introduce a specialized time-aware attention module to enhance recommendations within specific temporal contexts. Extensive experiments demonstrate the effectiveness and generalizability of our framework. We make our codes publicly available.
Autogen: An automated dynamic model generation framework for recommender system
Authors: Chenxu Zhu, Bo Chen, Huifeng Guo, Hang Xu, Xiangyang Li, Xiangyu Zhao, Weinan Zhang, Yong Yu, Ruiming Tang
Venue: Conference WSDM 2023
Year: 2023

Considering the balance between revenue and resource consumption for industrial recommender systems, intelligent recommendation computing has been emerging recently. Existing solutions deploy the same recommendation model to serve users indiscriminately, which is sub-optimal for total revenue maximization. We propose a multi-model service solution by deploying different-complexity models to serve different-valued users. An automated dynamic model generation framework AutoGen is elaborated to efficiently derive multiple parameter-sharing models with diverse complexities and adequate predictive capabilities. A mixed search space is designed and an importance-aware progressive training scheme is proposed to prevent interference between different architectures, which avoids the model retraining and improves the search efficiency, thereby efficiently deriving multiple models. Extensive experiments are conducted on two public datasets to demonstrate the effectiveness and efficiency of AutoGen.
Trid-mae: A generic pre-trained model for multivariate time series with missing values
Authors: Kai Zhang, Chao Li, Qinmin Yang
Venue: Conference CIKM 2023
Year: 2023

Multivariate time series(MTS) is a universal data type related to various real-world applications. Data imputation methods are widely used in MTS applications to deal with the frequent data missing problem. However, these methods inevitably introduce biased imputation and training-redundancy problems in downstream training. To address these challenges, we propose TriD-MAE, a generic pre-trained model for MTS data with missing values. Firstly, we introduce TriD-TCN, an end-to-end module based on TCN that effectively extracts temporal features by integrating dynamic kernel mechanisms and a time-flipping trick. Building upon that, we designed an MAE-based pre-trained model as the precursor of specialized downstream models. Our model cooperates with a dynamic positional embedding mechanism to represent the missing information and generate transferable representation through our proposed encoder units. The overall mixed data feed-in strategy and weighted loss function are established to ensure adequate training of the whole model. Comparative experiment results in time series prediction and classification manifest that our TriD-MAE model outperforms the other state-of-the-art methods within six real-world datasets. Moreover, ablation and interpretability experiments are delivered to verify the validity of TriD-MAE's
Usage patterns and the economics of the public cloud
Authors: Cinar Kilcioglu, Justin M. Rao, Aadharsh Kannan, R. Preston McAfee
Venue: Conference WWW 2017
Year: 2017

We examine the economics of demand and supply in cloud computing. The public cloud offers three main benefits to firms: 1) utilization can be scaled up or down easily; 2) capital expenditure (on-premises servers) can be converted to operating expenses, with the capital incurred by a specialist; 3) software can be ``pay-as-you-go.'' These benefits increase with the firm's ability to dynamically scale resource utilization and thus point to the need for dynamic prices to shape demand to the (short-run) fixed datacenter supply. Detailed utilization analysis reveals the large swings in utilization at the hourly, daily or weekly level are very rare at the customer level and non-existent at the datacenter level. Furthermore, few customers show volatility patterns that are excessively correlated with the market. These results explain why fixed prices currently prevail despite the seeming need for time-varying dynamics. Examining the actual CPU utilization provides a lens into the future. Here utilization varies by order half the datacenter capacity, but most firms are not dynamically scaling their assigned resources at-present to take advantage of these changes. If these gains are realized, demand fluctuations would be on par with the three classic industries where dynamic pricing is important (hotels, electricity, airlines) and dynamic prices would be essential for efficiency.
Learning multi-view molecular representations with structured and unstructured knowledge
Authors: Yizhen Luo, Kai Yang, Massimo Hong, Xing Yi Liu, Zikun Nie, Hao Zhou 0012, Zaiqing Nie
Venue: Conference KDD 2024
Year: 2024

Capturing molecular knowledge with representation learning approaches holds significant potential in vast scientific fields such as chemistry and life science. An effective and generalizable molecular representation is expected to capture the consensus and complementary molecular expertise from diverse views and perspectives. However, existing works fall short in learning multi-view molecular representations, due to challenges in explicitly incorporating view information and handling molecular knowledge from heterogeneous sources. To address these issues, we present MV-Mol, a molecular representation learning model that harvests multi-view molecular expertise from chemical structures, unstructured knowledge from biomedical texts, and structured knowledge from knowledge graphs. We utilize text prompts to model view information and design a fusion architecture to extract view-based molecular representations. We develop a two-stage pre-training procedure, exploiting heterogeneous data of varying quality and quantity. Through extensive experiments, we show that MV-Mol provides improved representations that substantially benefit molecular property prediction. Additionally, MV-Mol exhibits state-of-the-art performance in multi-modal comprehension of molecular structures and texts. Code and data are available at https://github.com/PharMolix/OpenBioMed.
Exploiting pre-trained models for drug target affinity prediction with nearest neighbors
Authors: Qizhi Pei, Lijun Wu, Zhenyu He, Jinhua Zhu, Yingce Xia, Shufang Xie, Rui Yan
Venue: Conference CIKM 2024
Year: 2024

Drug-Target binding Affinity (DTA) prediction is essential for drug discovery. Despite the application of deep learning methods to DTA prediction, the achieved accuracy remain suboptimal. In this work, inspired by the recent success of retrieval methods, we propose kNN-DTA, a non-parametric embedding-based retrieval method adopted on a pre-trained DTA prediction model, which can extend the power of the DTA model with no or negligible cost. Different from existing methods, we introduce two neighbor aggregation ways from both embedding space and label space that are integrated into a unified framework. Specifically, we propose a label aggregation with pair-wise retrieval and a representation aggregation with point-wise retrieval of the nearest neighbors. This method executes in the inference phase and can efficiently boost the DTA prediction performance with no training cost. In addition, we propose an extension, Ada-kNN-DTA, an instance-wise and adaptive aggregation with lightweight learning. Results on four benchmark datasets show that kNN-DTA brings significant improvements, outperforming previous state-of-the-art (SOTA) results, e.g, on BindingDB IC50 and Ki testbeds, kNN-DTA obtains new records of RMSE 0.684 and 0.750 . The extended Ada-kNN-DTA further improves the performance to be 0.675 and 0.735 RMSE. These results strongly prove the effectiveness of our method. Results in other settings and comprehensive studies/analyses also show the great potential of our kNN-DTA approach.
A hybrid unsupervised approach for document clustering
Authors: Mihai Surdeanu, Jordi Turmo, Alicia Ageno
Venue: Conference KDD 2005
Year: 2005

We propose a hybrid, unsupervised document clustering approach that combines a hierarchical clustering algorithm with Expectation Maximization. We developed several heuristics to automatically select a subset of the clusters generated by the first algorithm as the initial points of the second one. Furthermore, our initialization algorithm generates not only an initial model for the iterative refinement algorithm but also an estimate of the model dimension, thus eliminating another important element of human supervision. We have evaluated the proposed system on five real-world document collections. The results show that our approach generates clustering solutions of higher quality than both its individual components.
Extracting projective structure from single perspective views of 3D point sets
Authors: Charlie Rothwell, David A. Forsyth, Andrew Zisserman, Joseph L. Mundy
Venue: Conference ICCV 1993
Year: 1993

A number of recent papers have argued that invariants do not exist for three-dimensional point sets in general position, which has often been misinterpreted to mean that invariants cannot be computed for any three-dimensional structure. It is proved by example that although the general statement is true, invariants do exist for structured three-dimensional point sets. Projective invariants are derived for two object classes: the first is for points that lie on the vertices of polyhedra, and the second for objects that are projectively equivalent to ones possessing a bilateral symmetry. The motivations for computing such invariants are twofold: they can be used for recognition, and they can be used to compute projective structure. Examples of invariants computed from real images are given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Facial Expression Recognition using a Dynamic Model and Motion Energy
Authors: Irfan A. Essa, Alex Pentland
Venue: Conference ICCV 1995
Year: 1995

Previous efforts at facial expression recognition have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static facial "mugshots." We develop new more accurate representations for facial expression by building a video database of facial expressions and then probabilistically characterizing the facial muscle activation associated with each expression using a detailed physical model of the skin and muscles. This produces a muscle based representation of facial motion, which is then used to recognize facial expressions in two different ways. The first method uses the physics based model directly, by recognizing expressions through comparison of estimated muscle activations. The second method uses the physics based model to generate spatio temporal motion energy templates of the whole face for each different expression. These simple, biologically plausible motion energy "templates" are then used for recognition. Both methods show substantially greater accuracy at expression recognition than has been previously achieved.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Resolving Occlusion in Augmented Reality : a Contour Based Approach without 3D Reconstruction
Authors: Marie-Odile Berger
Venue: Conference CVPR 1997
Year: 1997

We present a new approach for resolving occlusions in augmented reality. The main interest is that it does not require 3D reconstruction of the considered scene. Our idea is to use a contour based approach and to label each contour point as being "behind" or "in front of", depending on whether it is in front of or behind the virtual object. This labeling step only requires that the contours can be tracked from frame to frame. A proximity graph is then built in order to group the contours that belong to the same occluding object. Finally, we use some kind of active contours to accurately recover the mask of the occluding object.
Rectangle extraction in catadioptric images
Authors: Jean-Charles Bazin, Inso Kweon, Cédric Demonceaux, Pascal Vasseur
Venue: Conference ICCV 2007
Year: 2007

Nowadays, robotic systems are more and more equipped with catadioptric cameras. However several problems associated to catadioptric vision have been studied only slightly. Especially algorithms for detecting rectangles in catadioptric images have not yet been developed whereas it is required in diverse applications such as building extraction in aerial images. We show that working in the equivalent sphere provides an appropriate framework to detect lines, parallelism, orthogonality and therefore rectangles. Finally, we present experimental results on synthesized and real data.
A New Perspective on Shape from Shading
Authors: Ariel Tankus, Nir A. Sochen, Yehezkel Yeshurun
Venue: Conference ICCV 2003
Year: 2003

Shape-from-shading (SFS) is a fundamental problem in computer vision. The vast majority of research in this field have assumed orthography as its projection model. This paper reexamines the basis of SFS, the image irradiance equation, under an assumption of perspective projection. The paper also shows that the perspective image irradiance equation depends merely on the natural logarithm of the depth function (and not on the depth function itself), and as such it is invariant to scale changes of the depth function. We then suggest a simple reconstruction algorithm based on the perspective formula, and compare it to existing orthographic SFS algorithms. This simple algorithm obtained lower error rates than legacy SFS algorithms, and equated with and sometimes surpassed state-of-the-art algorithms. These findings lend support to the assumption that transition to a more realistic set of assumptions improves reconstruction significantly.
Linear and quadratic subsets for template-based tracking
Authors: Selim Benhimane, Alexander Ladikos, Vincent Lepetit, Nassir Navab
Venue: Conference CVPR 2007
Year: 2007

We propose a method that dramatically improves the performance of template-based matching in terms of size of convergence region and computation time. This is done by selecting a subset of the template that verifies the assumption (made during optimization) of linearity or quadraticity with respect to the motion parameters. We call these subsets linear or quadratic subsets. While subset selection approaches have already been proposed, they generally do not attempt to provide linear or quadratic subsets and rely on heuristics such as textured-ness. Because a naive search for the optimal subset would result in a combinatorial explosion for large templates, we propose a simple algorithm that does not aim for the optimal subset but provides a very good linear or quadratic subset at low cost, even for large templates. Simulation results and experiments with real sequences show the superiority of the proposed method compared to existing subset selection approaches.
Exploiting uncertainty in random sample consensus
Authors: Rahul Raguram, Jan-Michael Frahm, Marc Pollefeys
Venue: Conference ICCV 2009
Year: 2009

In this work, we present a technique for robust estimation, which by explicitly incorporating the inherent uncertainty of the estimation procedure, results in a more efficient robust estimation algorithm. In addition, we build on recent work in randomized model verification, and use this to characterize the `non-randomness' of a solution. The combination of these two strategies results in a robust estimation procedure that provides a significant speed-up over existing RANSAC techniques, while requiring no prior information to guide the sampling process. In particular, our algorithm requires, on average, 3-10 times fewer samples than standard RANSAC, which is in close agreement with theoretical predictions. The efficiency of the algorithm is demonstrated on a selection of geometric estimation problems.
Double window optimisation for constant time visual slam
Authors: Hauke Strasdat, Andrew J. Davison, J. M. M. Montiel, Kurt Konolige
Venue: Conference ICCV 2011
Year: 2011

We present a novel and general optimisation framework for visual SLAM, which scales for both local, highly accurate reconstruction and large-scale motion with long loop closures. We take a two-level approach that combines accurate pose-point constraints in the primary region of interest with a stabilising periphery of pose-pose soft constraints. Our algorithm automatically builds a suitable connected graph of keyposes and constraints, dynamically selects inner and outer window membership and optimises both simultaneously. We demonstrate in extensive simulation experiments that our method approaches the accuracy of offline bundle adjustment while maintaining constant-time operation, even in the hard case of very loopy monocular camera motion. Furthermore, we present a set of real experiments for various types of visual sensor and motion, including large scale SLAM with both monocular and stereo cameras, loopy local browsing with either monocular or RGB-D cameras, and dense RGB-D object model building.
Constant interaction-time scatter/gather browsing of very large document collections
Authors: Douglas R. Cutting, David R. Karger, Jan O. Pedersen 0001
Venue: Conference SIGIR 1993
Year: 1993

The Scatter/Gather document browsing method uses fast document clustering to produce table-of-contents-like outlines of large document collections. Previous work [1] developed linear-time document clustering algorithms to establish the feasibility of this method over moderately large collections. However, even linear-time algorithms are too slow to support interactive browsing of very large collections such as Tipster, the DARPA standard text retrieval evaluation collection. We present a scheme that supports constant interaction-time Scatter/Gather of arbitrarily large collections after near-linear time preprocessing. This involves the construction of a cluster hierarchy. A modification of Scatter/Gather employing this scheme, and an example of its use over the Tipster collection are presented.
Intelligent caching: Selecting, representing, and reusing data in an information server
Authors: Yigal Arens, Craig A. Knoblock
Venue: Conference CIKM 1994
Year: 1994

Accessing information sources to retrieve data requested by a user can be expensive, especially when dealing with distributed information sources. One way to reduce this cost is to cache the results of queries, or related classes of data. This paper presents an approach to caching and addresses the issues of which information to cache, how to describe what has been cached, and how to use the cached information to answer future queries. We consider these issues in the context of the SIMS information server, which is a system for retrieving information from multiple heterogeneous and distributed information sources. The design of this information server is ideal for representing and reusing cached information since each class of cached information is simply viewed as another information source that is available for answering future queries.
Visualization Support for a User-Centered KDD Process
Authors: Tu Bao Ho, Trong Dung Nguyen, DucDung Nguyen
Venue: Conference KDD 2002
Year: 2002

Viewing knowledge discovery as a user-centered process that requires an effective collaboration between the user and the discovery system, our work aims to support an active role of the user in that process by developing synergistic visualization tools integrated in our discovery system D2MS. These tools provide an ability of visualizing the entire process of knowledge discovery in order to help the user with data preprocessing, selecting mining algorithms and parameters, evaluating and comparing discovered models, and taking control of the whole discover process. Our case-studies with two medical datasets on meningitis and stomach cancer show that, with visualization tools in D2MS, the user gains better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge.
Automatic mining of fruit fly embryo images
Authors: Jia-Yu Pan, André G. R. Balan, Eric P. Xing, Agma J. M. Traina, Christos Faloutsos
Venue: Conference KDD 2006
Year: 2006

We present FEMine, an automatic system for image-based gene expression analysis. We perform experiments on the largest publicly available collection of Drosophila ISH (in situ hybridization) images, showing that our FEMine system achieves excellent performance in classification, clustering, and content-based image retrieval. The major innovation of FEMine is the use of automatically discovered latent spatial "themes" of gene expressions, LGEs, in the whole-embryo context, as opposed to patterns in nearly disjoint portions of an embryo proposed in previous methods.