nils-herrmann/clean_scirepeval huggingface dataset:
Linear Time Maximum Margin Clustering

Maximum margin clustering (MMC) is a newly proposed clustering method which has shown promising performance in recent studies. It extends the computational techniques of support vector machine (SVM) to the unsupervised scenario. Traditionally, MMC is formulated as a nonconvex integer programming problem which makes it difficult to solve. Several methods have been proposed in the literature to solve the MMC problem based on either semidefinite programming (SDP) or alternating optimization. However, these methods are still time demanding when handling large scale data sets, which limits its application in real-world problems. In this paper, we propose a cutting plane maximum margin clustering (CPMMC) algorithm. It first decomposes the nonconvex MMC problem into a series of convex subproblems by making use of the constrained concave-convex procedure (CCCP), then for each subproblem, our algorithm adopts the cutting plane algorithm to solve it. Moreover, we show that the CPMMC algorithm takes O(sn) time to converge with guaranteed accuracy, where n is the number of samples in the data set and s is the sparsity of the data set, i.e., the average number of nonzero features of the data samples. We also derive the multiclass version of our CPMMC algorithm. Experimental evaluations on several real-world data sets show that CPMMC performs better than existing MMC methods, both in efficiency and accuracy.
Cyborg Intelligence: Recent Progress and Future Directions

The combination of biological and artificial intelligence is a promising methodology to construct a novel intelligent modality, proposed as cyborg intelligence. The hierarchical conceptual framework is based on the interaction and combination of comparable components of biological cognitive units and computing intelligent units. The authors extend the previous conceptual framework and focus on sensorimotor circuits to explore the representation and integration of sensation. They then present a cognitive computing model for brain-computer integration and design efficient machine-learning algorithms for neural signal decoding. They also propose biological reconstruction methods for sensorimotor circuits to not only restore but enhance functionalities with AI. They develop a series of demonstrating systems to validate the conceptual framework of cyborg intelligence and possibly herald bright prospects and high values in diversified aspects of theoretical research, engineering techniques, and clinical applications. Finally, the authors summarize the latest research trends and challenges, which they believe will further boost new scientific frontiers in cyborg intelligence.
Robust non-negative matrix factorization via joint sparse and graph regularization

In real world applications, we often have to deal with some high-dimensional, sparse and noisy data. In this paper, we aim to handle this kind of complex data by a Robust Non-negative Matrix Factorization via joint Sparse and Graph regularization model (RSGNMF). We provide a novel efficient and elegant iterative updating algorithm with rigorous convergence analysis for RSGNMF model. Experimental results on image data sets demonstrate that our RSGNMF model outperforms existing start-of-art methods.
Blind Separation of Superimposed Moving Images Using Image Statistics.

We address the problem of blind separation of multiple source layers from their linear mixtures with unknown mixing coefficients and unknown layer motions. Such mixtures can occur when one takes photos through a transparent medium, like a window glass, and the camera or the medium moves between snapshots. To understand how to achieve correct separation, we study the statistics of natural images in the Labelme data set. We not only confirm the well-known sparsity of image gradients, but also discover new joint behavior patterns of image gradients. Based on these statistical properties, we develop a sparse blind separation algorithm to estimate both layer motions and linear mixing coefficients and then recover all layers. This method can handle general parameterized motions, including translations, scalings, rotations, and other transformations. In addition, the number of layers is automatically identified, and all layers can be recovered, even in the underdetermined case where mixtures are fewer than layers. The effectiveness of this technology is shown in experiments on both simulated and real superimposed images.
Image-Text Surgery: Efficient Concept Learning in Image Captioning by Generating Pseudopairs

Image captioning aims to generate natural language sentences to describe the salient parts of a given image. Although neural networks have recently achieved promising results, a key problem is that they can only describe concepts seen in the training image-sentence pairs. Efficient learning of novel concepts has thus been a topic of recent interest to alleviate the expensive manpower of labeling data. In this paper, we propose a novel method, Image-Text Surgery, to synthesize pseudoimage-sentence pairs. The pseudopairs are generated under the guidance of a knowledge base, with syntax from a seed data set (i.e., MSCOCO) and visual information from an existing large-scale image base (i.e., ImageNet). Via pseudodata, the captioning model learns novel concepts without any corresponding human-labeled pairs. We further introduce adaptive visual replacement, which adaptively filters unnecessary visual features in pseudodata with an attention mechanism. We evaluate our approach on a held-out subset of the MSCOCO data set. The experimental results demonstrate that the proposed approach provides significant performance improvements over state-of-the-art methods in terms of F1 score and sentence quality. An ablation study and the qualitative results further validate the effectiveness of our approach.
TurboPixel Segmentation Using Eigen-Images

TurboPixel (TP) is a powerful tool for image over-segmentation. It is fast and can yield a lattice-like structure of superpixel regions with uniform size. This paper presents a method to learn eigen-images from the image to be segmented. Such eigen-images are used to generate the evolution speed in the TP framework. The task is formulated as a problem of pixel clustering. Specifically, for the pixels in each local window, a linear transformation is introduced to map their color vectors to be the cluster indicator vectors. The errors under all such linear transformations are estimated and summed together to obtain an objective function, from which a global optimum is finally obtained. In this process, the eigen-images are constructed. Based upon these eigen-images, multidimensional image gradient operator is defined to evaluate the gradient, which is supplied to the TP algorithm to obtain the final superpixel segmentations. The computational issues are discussed, and an image pyramid is introduced to speed up the computation. Comparative experiments illustrate the effectiveness of our method.
Beyond Banditron: A Conservative and Efficient Reduction for Online Multiclass Prediction with Bandit Setting Model

In this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. Aiming at learning from partial feedback of online classification results, i.e. “true” when the predicting label is right or “false” when the predicting label is wrong, this new kind of problems arouses much of researchers’ interest due to its close relations to real world internet applications and human cognitive procedure. While some algorithms have been brought forward, we propose a novel algorithm to deal with such problems. First, we reduce the multiclass prediction problem to binary based on Conservative one-versus-all others Reduction scheme; Then Online Passive-Aggressive Algorithm is embedded as binary learning algorithm to solve the reduced problem. Also we derive a pleasing cumulative mistake bound for our algorithm and a time complexity bound linear to the sample size. Further experimental evaluation on several real world multiclass datasets including RCV1, MNIST, 20 Newsgroups and USPS shows that our method outperforms the existing algorithms with a great improvement.
Kernel K-means Based Framework for Aggregate Outputs Classification

Aggregate outputs learning is a newly proposed setting in data mining and machine learning. It differs from the classical supervised learning setting in that, training samples are packed into bags with only the aggregate outputs (labels for classification or real values for regression) provided. This problem is associated with several kinds of application background. We focus on the aggregate outputs classification problem in this paper, and set up a framework based on kernel K-means to solve it. Two concrete algorithms based on our framework are proposed, each of which can cope with both binary and multi-class scenarios. The experimental results suggest that our algorithms outperform the state-of-art technique. Also, we propose a new setting for patch extraction in the Content Based Image Retrieval procedure by using the algorithm.
Semi-Supervised Classification Using Linear Neighborhood Propagation

In this paper, we address the general problem of learning from both labeled and unlabeled data. Based on the reasonable assumption that the label of each data can be linearly reconstructed from its neighbors’ labels, we develop a novel approach, called Linear Neighborhood Propagation (LNP), to learn the linear construction weights and predict the labels of the unlabeled data from the labeled data. The main procedure is composed of two steps: (1) it computes the local neighborhood geometry according to the image information by solving a constrained least squares problem, (2) the obtained geometry will be used to predict the labels of unlabeled data by solving a linearly constrained quadratic optimization problem. LNP has at least three characteristics. Firstly, it is much practical in that only one free parameter, the size of neighborhood, requires user-tuning. Secondly it can be easily extended to out-of-sample examples. Finally the labels achieved from LNP can be sufficiently smooth with respect to the intrinsic structure collectively revealed by the labeled and unlabeled points. Many promising experimental results, such as object recognition, data ranking and interactive image segmentation, demonstrate the effectiveness and efficiency of the proposed approach.
W-Boost and its application to Web image classification

When training data is not sufficient, boosting algorithms tend to overfit as more weak learners are combined to form a strong classifier. In this paper, we propose a new variant of RealBoost, called W-Boost, which is based on a novel weight update scheme and uses changeable bin number to estimate marginal distributions in weak learner design. This new boosting procedure results in both fast convergence rate and small generalization error. Experimental results on synthetic data and Web image classification demonstrate the effectiveness of our approach.
Short-term traffic flow forecasting based on Markov chain model

In this paper, the traffic flow is modeled as a high order Markov chain. And the transition probability from one state to the other state describes, given the current and recent values of the traffic flow, what the future value will be. Under the criteria of minimum mean square error, the optimal prediction is given as the conditional expectation according to the transition probability. However, in general, the transition probability is not known beforehand and we even don't know its form exactly. Gaussian Mixture Model (GMM), whose parameters are estimated with Expectation Maximum (EM) algorithm, is applied to approximate the transition probability. Then the representation of the optimal forecasting is given in terms of the parameters in GMM. A case study with real traffic data obtained from UTC/SCOOT system in Beijing shows the applicability and effectiveness of our proposed model.
Semi-Supervised Classification via Local Spline Regression

This paper presents local spline regression for semi-supervised classification. The core idea in our approach is to introduce splines developed in Sobolev space to map the data points directly to be class labels. The spline is composed of polynomials and Green's functions. It is smooth, nonlinear, and able to interpolate the scattered data points with high accuracy. Specifically, in each neighborhood, an optimal spline is estimated via regularized least squares regression. With this spline, each of the neighboring data points is mapped to be a class label. Then, the regularized loss is evaluated and further formulated in terms of class label vector. Finally, all of the losses evaluated in local neighborhoods are accumulated together to measure the global consistency on the labeled and unlabeled data. To achieve the goal of semi-supervised classification, an objective function is constructed by combining together the global loss of the local spline regressions and the squared errors of the class labels of the labeled data. In this way, a transductive classification algorithm is developed in which a globally optimal classification can be finally obtained. In the semi-supervised learning setting, the proposed algorithm is analyzed and addressed into the Laplacian regularization framework. Comparative classification experiments on many public data sets and applications to interactive image segmentation and image matting illustrate the validity of our method.
Location Signatures That You Don't See: Highlights from the IEEE Signal Processing Cup 2016 Student Competition [SP Education]

When thinking about using technologies to find one?s location, many of us may consider satellite-based global navigation systems, such as the U.S. global positioning system (GPS). Smartphones today use GPS type signals and cellular network signals to determine the phone user's current location and offer a broad range of location-related services. But when there was no such real-time connection and signal to analyze, for example, having only a piece of video or voice recording as an evidence of a news story or from child exploitation investigations (which have become an important global issue being addressed by the United Nation's UNICEF organization), are we out of luck?
Lower bounds estimation to KL transform in face representation and recognition

The Karhunen-Loeve (KL) transform is a very important method in data compression and pattern recognition, which has been successfully used in face recognition. Intuitively, the more training samples are used, the better performance would be, but the computation cost increases in proportion to cubic speed. The numbers of training samples and eigenvalues required by the KL method in face representation and recognition are considered. In this paper, we attempt to find the balance points by experiments and analyses.
Extracting the Optimal Dimensionality for Discriminant Analysis

For classification task, supervised dimensionality reduction is a very important method when facing with high-dimensional data. Linear discriminant analysis (LDA) is one of the most popular method for supervised dimensionality reduction. However, LDA suffers from the singularity problem, which makes it hard to work. Another problem is the determination of optimal dimensionality for discriminant analysis, which is an important issue but often been neglected previously. In this paper, we propose a new algorithm to address these two problems. Experiments show the effectiveness of our method and demonstrate much higher performance in comparison to LDA.
Face contour extraction with active shape models embedded knowledge

Face contour is an important feature of the human face and can be used for face indexing and retrieval. The key idea of face contour extraction here is to use the knowledge of the human face. We integrate the knowledge of the human face into the original active shape models (ASM) search. Due to the disturbance of hair, ears, eyes, mouth, or other noise, the face contour can not be exactly obtained by the original ASM method, so specific knowledge of the human face is employed in our method for more accurate face contour extraction. Experiments show the effectiveness of the proposed approaches.
Active model selection for Graph-Based Semi-Supervised Learning

The recent years have witnessed a surge of interest in graph-based semi-supervised learning (GBSSL). However, despite its extensive research, there has been little work on graph construction, which is at the heart of GBSSL. In this study, we propose a novel active learning method, active model selection (AMS), which aims at learning both data labels and the optimal graph by allowing the learner the flexibility to choose samples for labeling. AMS minimizes the regularization function in GBSSL by iterating between the active sample selection step and the graph reconstruction step, where the samples querying which leads to the optimal graph are selected. Experimental results on four real-world datasets are provided to demonstrate the effectiveness of AMS.
Analysis of Max-Min Eigenvalue of Constrained Linear Combinations of Symmetric Matrices

This paper studies the problem whether the smallest eigenvalue of constrained linear combinations of symmetric matrices can reach a desirable value, which actually extends the mathematical problem of finding a positive definite linear combination of symmetric matrices(PDLC), and provides a universal framework to maximize the minimal eigenvalue of linear combined symmetric matrices. For solving this problem, we cast an equivalent optimization task, and propose one general algorithm framework that is proved to be globally optimal and convergent. Both theoretical analysis and experiments under a typical constraint verify our algorithm's validity and efficiency.
A Bayesian network approach to time series forecasting of short-term traffic flows

A novel approach based on Bayesian networks for short-term traffic flow forecasting is proposed. A Bayesian network is originally used to model the causal relationship of time series of traffic flows among a chosen link and its adjacent links in a road network. Then, a Gaussian mixture model (GMM), whose parameters are estimated through competitive expectation maximization (CEM) algorithm, is applied to approximate the joint probability distribution of all nodes in the constructed Bayesian network. Finally, traffic flow forecasting of the current link is performed under the rule of minimum mean square error (MMSE). To further improve the forecasting performance, principal component analysis (PCA) is also adopted before carrying out the CEM algorithm. Experiments show that, by using a Bayesian network for short-term traffic flow forecasting, one can improve the forecasting accuracy significantly, and that the Bayesian network is an attractive forecasting method for such kinds of forecasting problems.
Blur detection for digital images using wavelet transform

With the prevalence of digital cameras, the number of digital images increases quickly, which raises the demand for image quality assessment in terms of blur. Based on the edge type and sharpness analysis, using the Harr wavelet transform, a new blur detection scheme is proposed in this paper, which can determine whether an image is blurred or not and to what extent an image is blurred. Experimental results demonstrate the effectiveness of the proposed scheme.
Probabilistic Labeled Semi-supervised SVM

Semi-supervised learning has been paid increasing attention and is widely used in many fields such as data mining, information retrieval and knowledge management as it can utilize both labeled and unlabeled data. Laplacian SVM (LapSVM) is a very classical method whose effectiveness has been validated by large number of experiments. However, LapSVM is sensitive to labeled data and it exposes to cubic computation complexity which limit its application in large scale scenario. In this paper, we propose a multi-class method called Probabilistic labeled Semi-supervised SVM (PLSVM) in which the optimal decision surface is taught by probabilistic labels of all the training data including the labeled and unlabeled data. Then we propose a kernel version dual coordinate descent method to efficiently solve the dual problems of our Probabilistic labeled Semi-supervised SVM and decrease its requirement of memory. Synthetic data and several benchmark real world datasets show that PLSVM is less sensitive to labeling and has better performance over traditional methods like SVM, LapSVM (LapSVM) and Transductive SVM (TSVM).
Semisupervised Learning Using Negative Labels

The problem of semisupervised learning has aroused considerable research interests in the past few years. Most of these methods aim to learn from a partially labeled dataset, i.e., they assume that the exact labels of some data are already known. In this paper, we propose to use a novel type of supervision information to guide the process of semisupervised learning, which indicates whether a point does not belong to a specific category. We call this kind of information negative label (NL) and propose a novel approach called NL propagation (NLP) to efficiently make use of this type of information to assist the process of semisupervised learning. Specifically, NLP assumes that nearby points should have similar class indicators. The data labels are propagated under the guidance of NL information and the geometric structure revealed by both labeled and unlabeled points, by employing some specified initialization and parameter matrices. The convergence analysis, out-of-sample extension, parameter determination, computational complexity, and relations to other approaches are presented. We also interpret the proposed approach within the framework of regularization. Promising experimental results on image, digit, spoken letter, and text classification tasks are provided to show the effectiveness of our method.
Clustering with Local and Global Regularization

Clustering is an old research topic in data mining and machine learning. Most of the traditional clustering methods can be categorized as local or global ones. In this paper, a novel clustering method that can explore both the local and global information in the data set is proposed. The method, Clustering with Local and Global Regularization (CLGR), aims to minimize a cost function that properly trades off the local and global costs. We show that such an optimization problem can be solved by the eigenvalue decomposition of a sparse symmetric matrix, which can be done efficiently using iterative methods. Finally, the experimental results on several data sets are presented to show the effectiveness of our method.
Knowledge-embedded symmetry transform for accurate eyeball location

It is common sense that the more we know, the better we do. By means of embedding more distinctive task-specific knowledge into a model, we achieved superior performance in our application of eyeball location. In this paper, a general formulation of a knowledge-embedded symmetry transform (KEST) is proposed. The key to this new design are the three newly introduced knowledge operators. The first is the symmetry-related weighting, the second is the mask operator serving as a localization factor, the last is the expected directional template. It is due to the embedding of the effective constraint knowledge that better screening efficiency, accuracy, simplified preprocessing and post-processing treatment are achieved.