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This paper considers the offline assignment of fixed priorities in partitioned preemptive real-time systems where tasks have precedence constraints. This problem is crucial in this type of systems, as having a good fixed priority assignment allows for an efficient use of the processing resources while meeting all the deadlines. In the literature, we can find several proposals to solve this problem, which offer varying trade-offs between the quality of their results and their computational complexities. In this paper , we propose a new approach, leveraging existing algorithms that are widely exploited in the field of Machine Learning: Gradient Descent, the Adam Optimizer, and Gradient Noise. We show how to adapt these algorithms to the problem of fixed priority assignment in conjunction with existing worst-case response time analyses. We demonstrate the performance of our proposal on synthetic task-sets with different sizes. This evaluation shows that our proposal is able to find more schedulable solutions than previous heuristics, approximating optimal but intractable algorithms such as MILP or brute-force, while requiring reasonable execution times. [ABSTRACT FROM AUTHOR]

Multi-agent reinforcement learning (MARL) has been successfully applied in many fields. In MARL, the policy evaluation problem is one of crucial problems. In order to solve this problem, distributed Temporal-Difference (TD) learning algorithm is one of the most popular methods in a cooperative manner. Despite its empirical success, however, the theory of the adaptive variant of distributed TD learning still remain limited. To fill this gap, we propose an adaptive distributed temporal-difference algorithm (referred to as MS - ADTD) under Markovian sampling over time-varying networks. Furthermore, we rigorously analyze the convergence of MS - ADTD , the theoretical results show that the local estimation can converge linearly to the optimal neighborhood. Meanwhile, the theoretical results are verified by simulation experiments. • This paper proposes a distributed adaptive TD learning algorithm under Markovian sampling. • It theoretically analyzes the non-asymptotic convergence performance of the proposed algorithm. • It verifies the performance of the proposed algorithm by experiments. [ABSTRACT FROM AUTHOR]

Abstract: Incremental learning of neural networks has attracted much interest in recent years due to its wide applicability to large scale data sets and to distributed learning scenarios. Moreover, nonstationary learning paradigms have also emerged as a subarea of study in Machine Learning literature due to the problems of classical methods when dealing with data set shifts. In this paper we present an algorithm to train single layer neural networks with nonlinear output functions that take into account incremental, nonstationary and distributed learning scenarios. Moreover, it is demonstrated that introducing a regularization term into the proposed model is equivalent to choosing a particular initialization for the devised training algorithm, which may be suitable for real time systems that have to work under noisy conditions. In addition, the algorithm includes some previous models as special cases and can be used as a block component to build more complex models such as multilayer perceptrons, extending the capacity of these models to incremental, nonstationary and distributed learning paradigms. In this paper, the proposed algorithm is tested with standard data sets and compared with previous approaches, demonstrating its higher accuracy. [Copyright &y& Elsevier]

This paper considers the loopy belief propagation (LBP) algorithm applied to Gaussian graphical models. It is known for Gaussian belief propagation that, if LBP converges, LBP computes the exact posterior means but incorrect variances. In this paper, we analytically derive the posterior variances for some special structured graphs and clarify the accuracy of LBP. For the graphs of a single cycle, we derive a rigorous solution for the posterior variances and thereby find the quantity that determines the accuracy of LBP. Based on this result, we state a necessary condition for LBP convergence. The quantity above also plays an important role in graphs of a single cycle with arbitrary trees. For arbitrary topological graphs, we consider the situation where correlations between any pair of nodes are comparatively small and show analytically the principal values that determine the accuracy of LBP. [Copyright &y& Elsevier]

Generally, training images are essential for a computer vision model to classify specific object class accurately. Unfortunately, there exist countless number of different object classes in real world, and it is almost impossible for a computer vision model to obtain a complete training images for each of the different object class. To overcome this problem, zero-shot learning algorithm was emerged to learn unknown object classes from a set of known object classes information. Among these methods, attributes and image hierarchy are the widely used methods. In this paper, we combine both the strength of attributes and image hierarchy by proposing Attributes Relationship Model (ARM) to perform zero-shot learning. We tested the efficiency of the proposed algorithm on Animals with Attributes (AwA) dataset and manage to achieve state-of-the-art accuracy (50.61%) compare to other recent methods. [ABSTRACT FROM AUTHOR]

Learning from imbalanced data is a problem which arises in many real-world scenarios, so does the need to build classifiers able to predict more than one class label simultaneously (multilabel classification). Dealing with imbalance by means of resampling methods is an approach that has been deeply studied lately, primarily in the context of traditional (non-multilabel) classification. In this paper the process of synthetic instance generation for multilabel datasets (MLDs) is studied and MLSMOTE (Multilabel Synthetic Minority Over-sampling Technique), a new algorithm aimed to produce synthetic instances for imbalanced MLDs, is proposed. An extensive review on how imbalance in the multilabel context has been tackled in the past is provided, along with a thorough experimental study aimed to verify the benefits of the proposed algorithm. Several multilabel classification algorithms and other multilabel oversampling methods are considered, as well as ensemble-based algorithms for imbalanced multilabel classification. The empirical analysis shows that MLSMOTE is able to improve the classification results produced by existent proposals. [ABSTRACT FROM AUTHOR]

Application of evolutionary computation techniques is relatively novel for machine learning. Motivated by different types of evolutionary computation techniques, different types of automatic programming were proposed. Biogeography-Based Optimization (BBO) is a new evolutionary algorithm that is inspired by the science of biogeography and has been shown to be competitive to other population-based algorithms. Inspired by biogeography theory and previous results, in this paper Biogeography-Based Programming (BBP) is proposed as a new type of automatic programming for creating polynomial regression models. In order to show the effectiveness of the proposed BBP, a number of experiments were carried out on a suite set of benchmark functions and the results were also compared with several existing automatic programming algorithms. Furthermore, sensitivity analysis was performed for the parameter settings of the proposed BBP. The results indicate that the proposed model is promising in terms of success rate and accuracy and it performs better than other algorithms investigated in this consideration. [ABSTRACT FROM AUTHOR]

In this paper, a new sequential learning algorithm is constructed by combining the Online Sequential Extreme Learning Machine (OS-ELM) and Kalman filter regression. The Kalman Online Sequential Extreme Learning Machine (KOSELM) handles the problem of multicollinearity of the OS-ELM, which can generate poor predictions and unstable models. The KOSELM learns the training data one-by-one or chunk-by-chunk by adjusting the variance of the output weights through the Kalman filter. The performance of the proposed algorithm has been validated on benchmark regression datasets, and the results show that KOSELM can achieve a higher learning accuracy than OS-ELM and its related extensions. A statistical validation for the differences of the accuracy for all algorithms is performed, and the results confirm that KOSELM has better stability than ReOS-ELM, TOSELM and LS-IELM. [ABSTRACT FROM AUTHOR]

Community structure is a common and important property of complex networks. The detection of communities has great significance for understanding the function and organization of networks. Generally, community detection can be formulated as a modularity optimization problem. However, traditional modularity optimization based algorithms have the resolution limit that they may fail to find communities which are smaller than a certain size. In this paper, we propose a cellular learning automata based algorithm for detecting communities in complex networks. Our algorithm models the whole network as an irregular cellular learning automata (ICLA) and reveals the optimal community structure through the evolution of the cellular learning automata. By interacting with both the global and local environments, our algorithm effectively solves the resolution limit problem of modularity optimization. The experiments on both synthetic and real-world networks demonstrate that our algorithm is effective and efficient at detecting community structure in complex networks. [ABSTRACT FROM AUTHOR]

Instance selection is one of the most important preprocessing steps in many machine learning tasks. Due to the increasing size of the problems, removing useless, erroneous or noisy instances is frequently an initial step that is performed before other data mining algorithms are applied. Instance selection as part of this data reduction task is one of the most relevant problems in current data mining research. Over the past decades, many different instance selection algorithms have been proposed, each with its own strengths and weaknesses. However, as in the case of classification, it is unlikely that a single instance selection algorithm would be able to achieve good results across many different datasets and application fields. In classification, one of the most successful ways of consistently improving the performance of a single learner is the construction of ensembles using boosting methods. In this paper, we propose a novel approach for instance selection based on boosting instance selection algorithms in the same way boosting is applied to classification. The proposed approach opens a new field of research in which to apply the many techniques developed for boosting classifiers, for instance selection and other data reduction techniques such as feature selection and simultaneous instance and feature selection. Using 60 datasets for balanced problems and 45 datasets for class-imbalanced problems, the experiments reported show a clear improvement in several state-of-the-art instance selection algorithms using the proposed methodology. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is recognized as one of the majority public and challenging problems in the Machine Learning domain. FS can be examined as an optimization problem that needs an effective optimizer to determine its optimal subset of more informative features. This paper proposes a wrapper FS method that combines chaotic maps (CMs) and binary Group Search Optimizer (GSO) called CGSO, which is used to solve the FS problem. In this method, five chaotic maps are incorporated with the GSO algorithm's main procedures, namely, Logistic, Piecewise, Singer, Sinusoidal, and Tent. The GSO algorithm is used as a search strategy, while k-NN is employed as an induction algorithm. The objective function is to integrate three main objectives: maximizing the classification accuracy value, minimizing the number of selected features, and minimizing the complexity of generated k-NN models. To evaluate the proposed methods' performance, twenty well-known UCI datasets are used and compared with other well-known published methods in the literature. The obtained results reveal the superiority of the proposed methods in outperforming other well-known methods, especially when using binary GSO with Tent CM. Finally, it is a beneficial method to be utilized in systems that require FS pre-processing. • A novel feature selection method is proposed for classification task. • Five Chaotic Group Search Optimizers are investigated. • Twenty UCI datasets were used in the experiments. • Our methods got a higher accuracy results using a fewer number of features. [ABSTRACT FROM AUTHOR]

Similarity learning plays a fundamental role in the problem of database retrieval and nearest classification problem. Traditional pairwise similarity measure ignores the contextual information, and the Graph Transduction (GT) has been proposed as a contextual similarity learning algorithm to utilize the contextual information, which is embedded in a nearest neighbor graph. On main shortage of this method is that it is difficult to choose the optimal graph since different graphs may focus on different aspects of the objects. Co-Transduction (CT) is lately proposed by fusing two different graphs. In this paper, we generalize this problem by using the ensemble of many candidate graphs with different models and parameters for transduction, by assuming that the optimal graph could be obtained by the weighted linear ensemble of these candidate graphs. The similarities and graph weights are modeled within one unified objective function, and optimized alternately in an iterative algorithm. The new proposed algorithm, named as Ensemble Transduction (ET), is tested on two challenging tasks and the experimental results show that it can outperform both the GT and CT. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce a novel algorithm to solve the problem of object tracking across multiple non-overlapping cameras by learning inter-camera transfer models. The transfer models are divided into two parts according to different kinds of cues, i.e. spatio-temporal cues and appearance cues. To learn spatio-temporal transfer models across cameras, an unsupervised topology recovering approach based on N-neighbor accumulated cross-correlations is proposed, which estimates the topology of a non-overlapping multi-camera network. Different from previous methods, the proposed topology recovering method can deal with large amounts of data without considering the size of time window. To learn inter-camera appearance transfer models, a color transfer method is used to model the changes of color characteristics across cameras, which has an advantage of low requirements to training samples, making update efficient when illumination conditions change. The experiments are performed on different datasets. Experimental results demonstrate the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: Traditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm — multiple-domain NMF (MDNMF) — was evaluated on two challenging multiple-domain learning problems — multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified. [Copyright &y& Elsevier]

Highlights: [•] This paper proposes a Column Generation Newton (CGN) algorithm for finding solution of the kernel 1-norm SVM. [•] CGN is combining the Column Generation and the Newton Linear Programming SVM method. [•] CGN is fast when solving the kernel 1-norm SVM. [ABSTRACT FROM AUTHOR]

• In order to solve the problem of unbalanced sample, a new Tradaboost algorithm is designed. • An industrial control anomaly detection system based on transfer learning is constructed. • The identification performance of both source and target domains is improved. • The experimental results show that the improved algorithm has higher performance. In industrial control systems, industrial infrastructure is often attacked by hackers. Due to the serious sample imbalance in industrial control data, the traditional machine learning method has poor performance in anomaly detection. In this paper, TrAdaboost algorithm is applied to industrial control anomaly detection. The samples that are easy to classify are taken as the source domain data, and the samples with poor classification effect are taken as the target domain. The source domain data is used to guide the target domain data training. Then, we improve the traditional TrAdaboost algorithm from two aspects of initial weight and final classifier, and apply it to industrial control anomaly detection. Finally, the performance of the algorithm on two different industrial control data sets is verified. And the improved algorithm is compared with other traditional algorithms. The experimental results show that the improved TrAdaboost algorithm has a significant advantage in predicting categories with a small sample size. This algorithm can accurately identify a few abnormal samples. Moreover, the F1 value, recall and precision value of the improved TrAdaboost algorithm on the two data sets have been significantly improved. This indicates that the improved TrAdaboost algorithm greatly improves the overall prediction accuracy of the model. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we study the multiclass classification problem. We derive a framework to solve this problem by providing algorithms with the complexity of a single binary classifier. The resulting multiclass machines can be decomposed into two categories. The first category corresponds to vector-output machines, where we develop several algorithms. In the second category, we show that the least-squares classifier can be easily cast into a multiclass one-versus-all scheme, without the need to train multiple binary classifiers. The proposed framework shows that, while keeping the classification accuracy essentially unchanged, the computational complexity is orders of magnitude lower than those previously reported in the literature. This makes our approach extremely powerful and conceptually simple. Moreover, we study the coding of the multiclass labels, and demonstrate that several celebrated approaches are equivalent. These arguments are illustrated with experimentations on well-known benchmarks. [Copyright &y& Elsevier]

Abstract: Look-up tables are commonly used in the automotive field for handling operating point variations. However, constant maps cannot cope with systems variations and ageing. Methods, such as Kalman filter or Extended Kalman filter for non-linear cases, can be used for table adaptation providing an optimal solution to the problem. But these methods are computationally intensive, making difficult to implement them on commercial engine control units. The current paper proposes a learning method for online updating of look-up tables or maps. This algorithm uses precalculated membership functions based on a standard Kalman filter observer for weighting the adaptation. The main contribution of the method is the derivation of a steady-state Kalman filter observer that lowers the calculation burden and simplifies the implementation against the standard Kalman filter implementation that requires higher computational cost. As far as table is updated online while engine runs, this allows correcting drift errors and the unit-to-unit dispersion. The method is illustrated for mapping engine variables such as and in a Diesel engine by using an adaptive look-up table, and its characteristics make it suitable for implementing in commercial engine electronic control units for online purposes. [Copyright &y& Elsevier]

Abstract: Discovering structure within a collection of high-dimensional input vectors is a problem that often recurs in the area of machine learning. A very suitable and widely used algorithm for solving such tasks is Non-negative Matrix Factorization (NMF). The high-dimensional vectors are arranged as columns in a data matrix, which is decomposed into two non-negative matrix factors of much lower rank. Here, we adopt the NMF learning scheme proposed by Van hamme (2008) . It involves combining the training data with supervisory data, which imposes the low-dimensional structure known to be present. The reconstruction of such supervisory data on previously unseen inputs then reveals their underlying structure in an explicit way. It has been noted that for many problems, not all features of the training data correlate equally well with the underlying structure. In other words, some features are relevant for detecting patterns in the data, while others are not. In this paper, we propose an algorithm that builds upon the learning scheme of Van hamme (2008) , and automatically weights each input feature according to its relevance. Applications include both data improvement and feature selection. We experimentally show that our algorithm outperforms similar techniques on both counts. [Copyright &y& Elsevier]

Abstract: In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly. [Copyright &y& Elsevier]

Abstract: This paper studies a single machine scheduling problem simultaneously with deteriorating jobs and learning effects. The objectives are to minimize the makespan and the number of tardy jobs, respectively. Two polynomial time algorithms are proposed to solve these problems optimally. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of optimal feature extraction from a wavelet representation. Our work aims at building features by selecting wavelet coefficients resulting from signal or image decomposition on an adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin context the wavelet shape as well as the appropriate scale and translation of the wavelets, hence the name “wavelet kernel learning”. This problem is posed as a multiple kernel learning problem, where the number of kernels can be very large. For solving such a problem, we introduce a novel multiple kernel learning algorithm based on active constraints methods. We furthermore propose some variants of this algorithm that can produce approximate solutions more efficiently. Empirical analysis show that our active constraint MKL algorithm achieves state-of-the art efficiency. When used for wavelet kernel learning, our experimental results show that the approaches we propose are competitive with respect to the state-of-the-art on brain–computer interface and Brodatz texture datasets. [Copyright &y& Elsevier]

Abstract: This paper deals with the super-resolution (SR) problem based on a single low-resolution (LR) image. Inspired by the local tangent space alignment algorithm in for nonlinear dimensionality reduction of manifolds, we propose a novel patch-learning method using locally affine patch mapping (LAPM) to solve the SR problem. This approach maps the patch manifold of low-resolution image to the patch manifold of the corresponding high-resolution (HR) image. This patch mapping is learned by a training set of pairs of LR/HR images, utilizing the affine equivalence between the local low-dimensional coordinates of the two manifolds. The latent HR image of the input (an LR image) is estimated by the HR patches which are generated by the proposed patch mapping on the LR patches of the input. We also give a simple analysis of the reconstruction errors of the algorithm LAPM. Furthermore we propose a global refinement technique to improve the estimated HR image. Numerical results are given to show the efficiency of our proposed methods by comparing these methods with other existing algorithms. [Copyright &y& Elsevier]

Abstract: In this paper a new learning algorithm is proposed for the problem of simultaneous learning of a function and its derivatives as an extension of the study of error minimized extreme learning machine for single hidden layer feedforward neural networks. Our formulation leads to solving a system of linear equations and its solution is obtained by Moore–Penrose generalized pseudo-inverse. In this approach the number of hidden nodes is automatically determined by repeatedly adding new hidden nodes to the network either one by one or group by group and updating the output weights incrementally in an efficient manner until the network output error is less than the given expected learning accuracy. For the verification of the efficiency of the proposed method a number of interesting examples are considered and the results obtained with the proposed method are compared with that of other two popular methods. It is observed that the proposed method is fast and produces similar or better generalization performance on the test data. [Copyright &y& Elsevier]

Abstract: Objectives: Balancing the trade-offs between solution quality, problem-solving efficiency, and transparency is an important challenge in medical applications of conversational case-based reasoning (CCBR). For example, test selection in CCBR is often based on strategies in which the absence of a specific hypothesis (e.g., diagnosis) to be confirmed makes it difficult to explain the relevance of test results that users are asked to provide. In this paper, we present an approach to CCBR in medical classification and diagnosis that aims to increase transparency while also providing high levels of accuracy and efficiency. Methods: We present an algorithm for CCBR called iNN(k) in which feature selection is driven by the goal of confirming a target class and informed by a measure of a feature''s discriminating power in favor of the target class. As we demonstrate in a CCBR system called CBR-Confirm, this enables a CCBR system to explain the relevance of any question it asks the user. We evaluate the algorithm''s accuracy and efficiency on a selection of datasets related to medicine and health care. Results: The performance of iNN(k) on a given dataset is shown to depend on the value of k and on whether local or global feature selection is used in the algorithm. The combination of these parameters for which iNN(k) is most effective in addressing the trade-off between accuracy and efficiency is identified for each of the selected datasets. For example, only 42% and 51% on average of features in a complete problem description were needed by iNN(k) to provide accuracy levels of 86.5% and 84.3% respectively on the lymphography and SPECT heart datasets from the UCI machine learning repository. Conclusion: Our results demonstrate the ability of iNN(k) to provide high levels of accuracy on most of the selected datasets, while often requiring the user to provide only a small subset of the features in a complete problem description, and enabling a CCBR system to explain the relevance of any question it asks the user. [Copyright &y& Elsevier]

• A cross-layer parallel attention network is proposed to further refine the semantic information of the high-level fusion features and the fine-grained information of the low-level fusion features. • A multiscale perception module is proposed to improve the robustness of the network to object scale change. • An effective anchor box allocation strategy is proposed to detect small objects at the low-level feature maps. • Our model can achieve mAP of 88.1% on the GDUT-HWD dataset and mAP of 80.5% on the PASCAL VOC 2007 dataset respectively. To solve the problem of low accuracy in existing safety helmet detection methods, a novel object detection algorithm based on Single Shot Multibox Detector (SSD) is proposed in this paper. The algorithm uses the spatial attention mechanism for low-level features and the channel attention mechanism for high-level features, this cross-layer attention mechanism can further refine the feature information of the object region. The proposed detection algorithm introduces a feature pyramid and multiscale perception module to improve its robustness to object scale change. In addition, an effective anchor box adaptive adjustment method is designed to adaptively adjust the scale distribution of each layer of the anchor boxes based on the feature map size. Experiment results demonstrate that our detection model has mean Average Precision (mAP) of 88.1% and 80.5% on helmet dataset and VOC 2007 dataset respectively, which is better than baseline by 15.65% and 3.4%. The network framework of CASP detection algorithm for safety helmet-wearing detection. [Display omitted] [ABSTRACT FROM AUTHOR]