nils-herrmann/clean_scirepeval huggingface dataset:
Profit maximization for viral marketing in Online Social Networks

Information can be disseminated widely and rapidly through Online Social Networks (OSNs) with “word-of-mouth” effects. Viral marketing is such a typical application in which new products or commercial activities are advertised by some seed users in OSNs to other users in a cascading manner. The budget allocation for seed selection reflects a tradeoff between the expense and reward of viral marketing. In this paper, we define a general profit metric that naturally combines the benefit of influence spread with the cost of seed selection in viral marketing to eliminate the need for presetting the budget for seed selection. We carry out a comprehensive study on finding a set of seed nodes to maximize the profit of viral marketing. We show that the profit metric is significantly different from the influence metric in that it is no longer monotone. As a result, from the computability perspective, the problem of profit maximization is much more challenging than that of influence maximization. We develop new seed selection algorithms for profit maximization with strong approximation guarantees. Experimental evaluations with real OSN datasets demonstrate the effectiveness of our algorithms.
Common visual pattern discovery and search

Automatically discovering common visual patterns from images and videos is a useful but challenging task. On the one hand, the definition of visual patterns is rather ambiguous, it refers to the spatial composition of frequently occurring visual primitives which correspond to local features, semantic visual parts or visual objects. For example, the wheels and the body of a car could be seen as different visual primitives, while the whole car can also be seen as an individual visual primitive. On the other hand, there exhibit large variations in visual appearance and structures even within the same kind of visual pattern, which makes visual pattern discovery a very challenging task. However, since to distinguish different kinds of visual patterns from each other is a fundamental problem of many tasks in computer vision, such as pattern recognition/classification, object detection/localization, content-based image search, many studies have been introduce to solve the problem of visual pattern discovery in the literature. In this paper, we will revisit the representative studies on discovering visual patterns and discuss these methods from the view of local-feature-based and object- proposal-based visual patterns. The local-feature-based visual pattern discovery aims to mine the visual primitives that share similar spatial layout, while the semantic-patch-based visual pattern discovery aims to mine similar semantic patterns from the object proposals that are likely to contain an entire object. Then the extensive applications of visual pattern discovery are presented.
Action Search by Example Using Randomized Visual Vocabularies

Because actions can be small video objects, it is a challenging problem to search for similar actions in crowded and dynamic scenes when a single query example is provided. We propose a fast action search method that can efficiently locate similar actions spatiotemporally. Both the query action and the video datasets are characterized by spatio-temporal interest points. Instead of using a unified visual vocabulary to index all interest points in the database, we propose randomized visual vocabularies to enable fast and robust interest point matching. To accelerate action localization, we have developed a coarse-to-fine video subvolume search scheme, which is several orders of magnitude faster than the existing spatio-temporal branch and bound search. Our experiments on cross-dataset action search show promising results when compared with the state of the arts. Additional experiments on a 5-h versatile video dataset validate the efficiency of our method, where an action search can be finished in just 37.6 s on a regular desktop machine.
Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection

The rapid growth of consumer videos requires an effective and efficient content summarization method to provide a user-friendly way to manage and browse the huge amount of video data. Compared with most previous methods that focus on sports and news videos, the summarization of personal videos is more challenging because of its unconstrained content and the lack of any pre-imposed video structures. We formulate video summarization as a novel dictionary selection problem using sparsity consistency, where a dictionary of key frames is selected such that the original video can be best reconstructed from this representative dictionary. An efficient global optimization algorithm is introduced to solve the dictionary selection model with the convergence rates as O(1/K2) (where K is the iteration counter), in contrast to traditional sub-gradient descent methods of O(1/√K). Our method provides a scalable solution for both key frame extraction and video skim generation, because one can select an arbitrary number of key frames to represent the original videos. Experiments on a human labeled benchmark dataset and comparisons to the state-of-the-art methods demonstrate the advantages of our algorithm.
Topical video object discovery from key frames by modeling word co-occurrence prior

A topical video object refers to an object, that is, frequently highlighted in a video. It could be, e.g., the product logo and the leading actor/actress in a TV commercial. We propose a topic model that incorporates a word co-occurrence prior for efficient discovery of topical video objects from a set of key frames. Previous work using topic models, such as latent Dirichelet allocation (LDA), for video object discovery often takes a bag-of-visual-words representation, which ignored important co-occurrence information among the local features. We show that such data driven co-occurrence information from bottom-up can conveniently be incorporated in LDA with a Gaussian Markov prior, which combines top-down probabilistic topic modeling with bottom-up priors in a unified model. Our experiments on challenging videos demonstrate that the proposed approach can discover different types of topical objects despite variations in scale, view-point, color and lighting changes, or even partial occlusions. The efficacy of the co-occurrence prior is clearly demonstrated when compared with topic models without such priors.
Direct mining co-occurrence features for visual recognition: A branch and bound method

The co-occurrence features are the composition of base features that have more discriminative power than individual base features. Although they show promising performance in visual recognition applications such as object and scene recognition, the discovery of discriminative co-occurrence features is usually a computational demanding task. Unlike previous feature mining methods that fix the order of the co-occurrence features or rely on a two-stage frequent pattern mining to select the optimal feature co-occurrence, we propose a novel branch-and-bound based co-occurrence feature mining algorithm that can directly mine both optimal conjunctions (AND) and disjunctions (OR) of individual features at arbitrary orders simultaneously. This feature mining process is integrated into a multi-class boosting framework Adaboost. MH such that the weighted error is minimized by the discovered co-occurrence features in each boosting step. Experiments on the benchmark datasets and scene recognition dataset validate the advantages of our proposed method.
Simultaneously Discovering and Localizing Common Objects in Wild Images

Motivated by the recent success of supervised and weakly supervised common object discovery, in this paper, we move forward one step further to tackle common object discovery in a fully unsupervised way. Generally, object co-localization aims at simultaneously localizing objects of the same class across a group of images. Traditional object localization/detection usually trains specific object detectors which require bounding box annotations of object instances, or at least image-level labels to indicate the presence/absence of objects in an image. Given a collection of images without any annotations, our proposed fully unsupervised method is to simultaneously discover images that contain common objects and also localize common objects in corresponding images. Without requiring to know the total number of common objects, we formulate this unsupervised object discovery as a sub-graph mining problem from a weighted graph of object proposals, where nodes correspond to object proposals, and edges represent the similarities between neighbouring proposals. The positive images and common objects are jointly discovered by finding sub-graphs of strongly connected nodes, with each sub-graph capturing one object pattern. The optimization problem can be efficiently solved by our proposed maximal-flow-based algorithm. Instead of assuming that each image contains only one common object, our proposed solution can better address wild images where each image may contain multiple common objects or even no common object. Moreover, our proposed method can be easily tailored to the task of image retrieval in which the nodes correspond to the similarity between query and reference images. Extensive experiments on PASCAL VOC 2007 and Object Discovery data sets demonstrate that even without any supervision, our approach can discover/localize common objects of various classes in the presence of scale, view point, appearance variation, and partial occlusions. We also conduct broad experiments on image retrieval benchmarks, Holidays and Oxford5k data sets, to show that our proposed method, which considers both the similarity between query and reference images and also similarities among reference images, can help to improve the retrieval results significantly.
Efficient ground object segmentation in 3D LIDAR based on cascaded mode seeking

Lidar segmentation is a common preprocessing step for performing ground object detection in autonomous vehicle applications. However, many common approaches are affected by at least one of the following three issues: they are prone to under-segmentation/over-segmentation; they are not able to effectively handle missing points caused by black objects or bright sunlight; or they are too complex for real time use on commodity hardware. In this paper we propose a modification to distance based segmentation algorithms, which is able to improve the performance without incurring significant computational cost. Numerical evaluations on the KITTI dataset confirm the methods applicability.
Learning weighted geometric pooling for image classification

Local feature extraction, coding, spatial pooling, and image classification are the four typical steps for state-of-the-art visual recognition systems. Unlike previous work that treats spatial pooling and image classification as separated steps, we propose to jointly learn the geometric pooling and image classifier such that class-specific geometric information of local descriptors can be incorporated to improve classification performance. Inspired by previous work of spatial pyramid matching and receptive field learning, we also propose spatial pyramid geometric pooling, receptive field geometric pooling and random partition geometric pooling approaches to further exploit the spatial structural information to boost classification performance. Experiments on 15-scene dataset validate the advantages of our proposed algorithms.
Object tracking via online metric learning

By considering visual tracking as a similarity matching problem, we propose a self-supervised tracking method that incorporates adaptive metric learning and semi-supervised learning into the framework of object tracking. For object representation, the spatial-pyramid structure is applied by fusing both the shape and texture cues as descriptors. A metric learner is adaptively trained online to best distinguish the foreground object and background, and a new bi-linear graph is defined accordingly to propagate the label of each sample. Then high-confident samples are collected to self-update the model to handle large-scale issue. Experiments on the benchmark dataset and comparisons with the state-of-the-art methods validate the advantages of our algorithm.
Multiple instance boosting with global smoothness regularization

In multiple instance learning, the training set consists of labeled bags that include unlabeled instances, and the target is to predict the labels of unseen bags. A bag is labeled positive only if it contains at least one positive instance, otherwise it is a negative bag. Over the past years, many popular machine learning algorithms have been adapted to tackle the multiple instance learning problems. In this paper, to train a discriminative multiple instance classifier which generalize well, we present a boosting approach with global smoothness regularization, in which the weak learners are either hyper balls with the center at the instance of positive bags or random projection decision stumps. Experimental results show that our proposed algorithm is comparable to the classical Diverse Density algorithm on some multiple instance learning benchmark datasets.
Augmented tree partitioning for interactive image segmentation

In this paper, we propose a new fast semi-supervised image segmentation method based on augmented tree partitioning. Unlike many existing methods that use a graph structure to model the image, we use a tree-based structure called the augmented tree, which is built up by augmenting several abstract label nodes to the minimum spanning tree of the original graph. We then model image segmentation as the partitioning problem on the augmented tree. Dynamic programming is used to efficiently solve the optimization problem. Experimental results show that our method gives competitive segmentation results, and the speed is much faster than graph- based methods.
Discriminative Least Squares Regression for Multiclass Classification and Feature Selection

This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the ε-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.
Maximum Margin Embedding

We propose a new dimensionality reduction method called maximum margin embedding (MME), which targets to projecting data samples into the most discriminative subspace, where clusters are most well-separated. Specifically, MME projects input patterns onto the normal of the maximum margin separating hyperplanes. As a result, MME only depends on the geometry of the optimal decision boundary and not on the distribution of those data points lying further away from this boundary. Technically, MME is formulated as an integer programming problem and we propose a cutting plane algorithm to solve it. Moreover, we prove theoretically that the computational time of MME scales linearly with the dataset size. Experimental results on both toy and real world datasets demonstrate the effectiveness of MME.
Assessing features for electroencephalographic signal categorization

The classification of electroencephalographic (EEG) signals is an important issue in the ongoing research of brain-computer interface (BCI) technology. One such BCI uses slow cortical potential measures to infer user intent from the original brain activity. Seven features based on the standard low-level signal properties are evaluated for their ability to classify brain activities, and thus make up for the scarcity of signal features for the current EEG signal categorization. In addition, a paradigm is proposed to select effective low-level features for EEG signal classification. Combining the features selected by the paradigm with the DC value of slow cortical potentials for categorization based on a Bayesian classifier, we obtained significant improvement on classification accuracy for data set Ia of the BCI competition 2003, which is a typical representative of one kind of BCI data.
Efficient blind separation of reflection layers with nonparametric transformations

Superimposed images are very common when taking photos behind glass. We address the reflection separation problem using multiple superimposed images photographed in different viewpoints. With viewpoints changing, the reflected scenes could contain arbitrarily complicated variations between mixtures, like human's motions or other nonrigid motions. In this article, we propose a moderate hypothesis to tackle the reflected scenes' arbitrary variations as well as the parametric transformations of transmitted scenes. To rapidly recover high-quality image layers, we propose an Efficient Superimposition Recovering Algorithm (ESRA) by extending the framework of accelerated gradient method. Our recovering method has good converging performance and is more than 30 times faster than state-of-the-art methods. Experimental results on synthetic and real world images demonstrate that our method is promising.
Discriminant Additive Tangent Spaces for Object Recognition

Pattern variation is a major factor that affects the performance of recognition systems. In this paper, a novel manifold tangent modeling method called discriminant additive tangent spaces (DATS) is proposed for invariant pattern recognition. In DATS, intra-class variations for traditional tangent learning are called positive tangent samples. In addition, extra-class variations are introduced as negative tangent samples. We use log-odds to measure the significance of samples being positive or negative, and then directly characterizes this log-odds using generalized additive models (GAM). This model is estimated to maximally discriminate positive and negative samples. Besides, since traditional GAM fitting algorithm can not handle the high dimensional data in visual recognition tasks, we also present an efficient, sparse solution for GAM estimation. The resulting DATS is a nonparametric discriminant model based on quite weak prior hypotheses, hence it can depict various pattern variations effectively. Experiments demonstrate the effectiveness of our method in several recognition tasks.
Learning probability density functions from marginal distributions with applications to Gaussian mixtures

Probability density function (PDF) estimation is a constantly important topic in the fields related to artificial intelligence and machine learning. This paper is dedicated to considering problems on the estimation of a density function simply from its marginal distributions. The possibility of the learning problem is first investigated and a uniqueness proposition involving a large family of distribution functions is proposed. The learning problem is then reformulated into an optimization task which is studied and applied to Gaussian mixture models (GMM) via the generalized expectation maximization procedure (GEM) and Monte Carlo method. Experimental results show that our approach for GMM, only using partial information of the coordinates of the samples, can obtain satisfactory performance, which in turn verifies the proposed reformulation and proposition.
The Selective Random Subspace Predictor for Traffic Flow Forecasting

Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems. Due to practical limitations, traffic flow data may be incomplete (partially missing or substantially contaminated by noises), which will aggravate the difficulties for traffic flow forecasting. In this paper, a new approach, termed the selective random subspace predictor (SRSP), is developed, which is capable of implementing traffic flow forecasting effectively whether incomplete data exist or not. It integrates the entire spatial and temporal traffic flow information in a transportation network to carry out traffic flow forecasting. To forecast the traffic flow at an object road link, the Pearson correlation coefficient is adopted to select some candidate input variables that compose the selective input space. Then, a number of subsets of the input variables in the selective input space are randomly selected to, respectively, serve as specific inputs for prediction. The multiple outputs are combined through a fusion methodology to make final decisions. Both theoretical analysis and experimental results demonstrate the effectiveness and robustness of the SRSP for traffic flow forecasting, whether for complete data or for incomplete data
Topic model with constrainted word burstiness intensities

Word burstiness phenomenon, which means that if a word occurs once in a document it is likely to occur repeatedly, has interested the text analysis field recently. Dirichlet Compound Multinomial Latent Dirichlet Allocation (DCMLDA) introduces this word burstiness mechanism into Latent Dirichlet Allocation (LDA). However, in DCMLDA, there is no restriction on the word burstiness intensity of each topic. Consequently, as shown in this paper, the burstiness intensities of words in major topics will become extremely low and the topics' ability to represent different semantic meanings will be impaired. In order to get topics that represent semantic meanings of documents well, we introduce constraints on topics' word burstiness intensities. Experiments demonstrate that DCMLDA with constrained word burstiness intensities achieves better performance than the original one without constraints. Besides, these additional constraints help to reveal the relationship between two key properties inherited from DCM and LDA respectively. These two properties have a great influence on the combined model's performance and their relationship revealed by this paper is an important guidance for further study of topic models.
Enhancing Classification by Perceptual Characteristic for the P300 Speller Paradigm

We find that in P300 speller paradigm, there exists difference between electroencephalogram (EEG) signals evoked by flashing adjacent to the row (column) containing the target and those evoked by other non-target stimuli. This difference might arise out of users' perceptual confusion. We use a machine learning approach to learn the difference of this perceptual characteristic, and employ the classifier to aid classification in P300 speller. Experiments show that our method remarkably improves the performance