Search

In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China's A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long–short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets. [ABSTRACT FROM AUTHOR]

This paper is concerned with the ℓ 1 , ∞ -norm ball constrained multi-task learning problem, which has received extensive attention in many research areas such as machine learning, cognitive neuroscience, and signal processing. To address the challenges of solving large-scale multi-task Lasso problems, this paper develops an inexact semismooth Newton-based augmented Lagrangian (Ssnal) algorithm. When solving the inner problems in the Ssnal algorithm, the semismooth Newton (Ssn) algorithm with superlinear or even quadratic convergence is applied. Theoretically, this paper presents the global and asymptotically superlinear local convergence of the Ssnal algorithm under standard conditions. Computationally, we derive an efficient procedure to construct the generalized Jacobian of the projector onto ℓ 1 , ∞ -norm ball, which is an important component of the Ssnal algorithm, making the computational cost in the Ssn algorithm very cheap. Comprehensive numerical experiments on the multi-task Lasso problems demonstrate that the Ssnal algorithm is more efficient and robust than several existing state-of-the-art first-order algorithms. [ABSTRACT FROM AUTHOR]

To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]

Aiming to obtain more discriminative features in scene images and overcome the impacts of intra-class differences and inter-class similarities, the paper proposes a scene recognition method that combines attention and context information. First, we introduce the attention mechanism and build a multi-scale attention model. Discriminative information considers salient objects and regions by means of channel attention and spatial attention. Besides, the central loss function joint supervision strategy is introduced to further reduce the misjudgment of intra-class differences. Second, a model based on multi-level context information is proposed to describe the positional relationship between objects, which can effectively alleviate the influence of the similarity of objects between classes. Finally, the two models are merged to give full play to the compatibility of features, so that the final feature representation not only focuses on the effective discriminant information, but also manifests the relative position relationship between significant objects. Extensive experiments have proved that the method in this paper effectively solves the problem of insufficient feature representation in scene recognition tasks, and improves the accuracy of scene recognition. [ABSTRACT FROM AUTHOR]

Recently, deep learning has become a promising technique for constructing gesture recognition classifiers from surface electromyography (sEMG) signals in human–computer interaction. In this paper, we propose a gesture recognition method with sEMG signals based on a deep multi-parallel convolutional neural network (CNN), which solves the problem that traditional machine learning methods may lose too much useful information during feature extraction. CNNs provide an efficient way to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. Sophisticated feature extraction is to be avoided and hand gestures are to be classified directly. A multi-parallel and multi-convolution layer convolution structure is proposed to classify hand gestures. Experiment results show that in comparison with five traditional machine learning methods, the proposed method could achieve higher accuracy. [ABSTRACT FROM AUTHOR]

In reinforcement learning (RL), the exploration/exploitation (E/E) dilemma is a very crucial issue, which can be described as searching between the exploration of the environment to find more profitable actions, and the exploitation of the best empirical actions for the current state. We focus on the single trajectory RL problem where an agent is interacting with a partially unknown MDP over single trajectories, and try to deal with the E/E in this setting. Given the reward function, we try to find a good E/E strategy to address the MDPs under some MDP distribution. This is achieved by selecting the best strategy in mean over a potential MDP distribution from a large set of candidate strategies, which is done by exploiting single trajectories drawn from plenty of MDPs. In this paper, we mainly make the following contributions: (1) We discuss the strategy-selector algorithm based on formula set and polynomial function. (2) We provide the theoretical and experimental regret analysis of the learned strategy under an given MDP distribution. (3) We compare these methods with the 'state-of-the-art' Bayesian RL method experimentally. [ABSTRACT FROM AUTHOR]

Sparse subspace learning has drawn more and more attentions recently, however, most of them are unsupervised and unsuitable for classification tasks. In this paper, a new discriminant sparsity preserving analysis (DSPA) method by integrating sparse reconstructive weighting into Fisher criterion is proposed for face recognition. We first get sparsity preserving space spanned by the eigenvectors of sparsity preserving projections (SPP). Then, the optimal projection can be obtained by solving an eigenvalue and eigenvector problem of the between-class scatter matrix in sparsity preserving space. The method not only preserves the sparse reconstructive relationship of the data, but also encodes the discriminant information. Extensive experiments on four face image datasets (Yale, ORL, AR and CMU PIE) demonstrate the effectiveness of the proposed DSPA method. [ABSTRACT FROM AUTHOR]

In this paper, We deal with the scheduling problems with simple linear deterioration function on unrelated parallel-machine and identical parallel-machine. We assume that all the jobs (tasks) are available at time zero, but all machines (processors) may not be available simultaneously at time zero. The objective functions are to minimize the total logarithm of completion time, the total logarithm of machine load, the total absolute differences in logarithm completion times and the total absolute differences in logarithm waiting times respectively. We provide the polynomial-time algorithms to solve the problems under the proposed model respectively. [ABSTRACT FROM AUTHOR]

Multi-label classification has attracted significant attentions in machine learning. In multi-label classification, exploiting correlations among labels is an essential but nontrivial task. First, labels may be correlated in various degrees. Second, the scalability may suffer from the large number of labels, because the number of combinations among labels grows exponentially as the number of labels increases. In this paper, a multi-label hypernetwork (MLHN) is proposed to deal with these problems. By extending the traditional hypernetwork model, MLHN can represent arbitrary order correlations among labels. The classification model of MLHN is simple and the computational complexity of MLHN is linear with respect to the number of labels, which contribute to the good scalability of MLHN. We perform experiments on a variety of datasets. The results illustrate that the proposed MLHN achieves competitive performances against state-of-the-art multi-label classification algorithms in terms of both effectiveness and scalability with respect to the number of labels. [ABSTRACT FROM AUTHOR]

This paper studies the online hierarchical scheduling problem on two uniform machines with rejection. Two uniform machines M1, M2 run at the speeds of s ∈ (0, +∞), 1 separately; and they are provided with different capabilities. Each machine has a certain GOS level 1 or 2 and every job is also associated with a hierarchy 1 or 2. The job can only be assigned to the machine whose GOS level does not exceed the job's hierarchy. Preemption is permitted but idle is not introduced. Jobs come one by one over list. When a job arrives, it can be accepted and scheduled on some machine or rejected by paying its penalty. The objective is to minimize the sum of makespan yielded by accepted jobs and total penalties of all rejected jobs. For this problem, we propose a family of several online algorithms according to the range of s and the related lower bound is also obtained. These algorithms achieve optimal competitive ratio when s ∈ (0, 1) ∪ [1.618, +∞), but have a small gap between upper bound and lower bound in interval [1, 1.618). [ABSTRACT FROM AUTHOR]

This paper considers a single machine scheduling with both deterioration and positional effects and due-window assignment problem. The job-dependent due-windows are obtained by the common flow allowance criterion. The objective is to schedule the jobs, and the due-windows so as to minimize the sum of earliness, tardiness, and due-window starting time and due-window size costs. We introduce a polynomial solution for the problem. Furthermore, we show how the solutions can be extended to the setting with job rejection. [ABSTRACT FROM AUTHOR]

Job learning and deterioration coexist in many realistic machine-job scheduling situations. However, in literature, the constructed forms of the machine scheduling models with job learning and/or deteriorating effects were specific types of functions, which constrained their applicability in practice. This paper introduces a new single-machine scheduling model, where the actual processing time of a job is a general function of its starting time as well as scheduled position, which shows a broad generalization in contrast to that of certain existing models. For three objectives corresponding to the single-machine scheduling problem-total weighted completion time, discounted total weighted completion time, and maximum lateness - this paper presents their respective approximation result on the basis of the worst-case bound analysis from the optimal algorithm. The results demonstrate that under our proposed model, minimization of scheduling operations such as the makespan, sum of the kth power of completion times, and total lateness are polynomially solvable. Moreover, under some feasible conditions for the scheduling parameters, the minimum optimization problems of the total weighted completion time, discounted total weighted completion time, maximum lateness, and total tardiness are all recognized as polynomial forms and their solutions are provided. [ABSTRACT FROM AUTHOR]

Fuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characterized by ambiguity, and/or non-trivial causalities among its variables. The rich literature that is found related to FCMs reports very clearly many successful studies solved through the use of FCMs; however, when it comes to software implementations, where domain experts can design FCM-based systems, run simulations or perform more advanced experiments, not much is found or documented. The few existing implementations are not proficient in providing options for experimentation. Therefore, we believe that a gap exists, specifically between the theoretical advances and the development of accurate, transparent and sound FCM-based systems; and we advocate for the creation of more complete and exible software products. The goal of this paper is to introduce "FCM Expert", a software tool for fuzzy cognitive modeling, where we focus on scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters that might define a model, optimize its network topology and improve the system convergence without losing information. Also, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface. [ABSTRACT FROM AUTHOR]

This paper presents a method for adapting the cost function in the Monge-Kantorovich Problem (MKP) to a classification task. More specifically, we introduce a criterion that allows to learn a cost function which tends to produce large distance values for elements belonging to different classes and small distance values for elements belonging to the same class. Under some additional constraints (one of them being the well-known Monge condition), we show that the optimization of this criterion writes as a linear programming problem. Experimental results on synthetic data show that the output optimal cost function provides good retrieval performances in the presence of two types of perturbations commonly found in histograms. When compared to a set of various commonly used cost functions, our optimal cost function performs as good as the best cost function of the set, which shows that it can adapt well to the task. Promising results are also obtained on real data for two-class image retrieval based on grayscale intensity histograms. [ABSTRACT FROM AUTHOR]

Situation awareness is an emerging concept in ubiquitous environments, particularly the learning ones. Situation identification techniques developed in literature aim to infer the user's situation from the detected context. Most of the studies to date in ubiquitous learning (u-learning) field give equal importance for detected context elements to describe learner's situation and therefore allow irrelevant context elements to have as much effect on situation description as relevant ones. Therefore, different weights need to be associated to context elements with reference to their importance to the inference process. In this paper, a new proposal for weighting u-learning context elements is detailed. The solution aims to merge different expert opinions about context elements weighting for situation description. Evidence theory is applied in order to handle uncertain and conflicting expert opinions. Experimental results are given to illustrate the applicability of the proposed solution in selecting characteristic context elements appropriate for each u-learning situation pattern and distinguishing them from others. [ABSTRACT FROM AUTHOR]

Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear programming (MILP) approaches, especially given the size of data centres. Hybrid solutions mixing CP and heuristic algorithms, such as, large neighbourhood search (CBLNS), also struggle to address the problem given its size and number of constraints. The multi-objective version of the Machine Reassignment Problem is even more challenging and it seems unlikely for CP, MILP or hybrid solutions to obtain good results in this context. As a result, the first approaches to address this problem have been based on other optimisation methods, including metaheuristics. In this paper we study three things: (i) under which conditions a mixed integer optimisation solver, such as IBM ILOG CPLEX, can be used for the Multi-objective Machine Reassignment Problem; (ii) how much of the search space can a well-known hybrid method such as CBLNS explore; and (iii) can we find a better hybrid approach combining MILP or CBLNS and an- other recent metaheuristic proposed for the problem (GeNePi). We show that MILP can handle only small or medium scale data centres, and with some relaxations, such as, an optimality tolerance gap and a limited number of directions explored in the search space. CBLNS on the other hand struggles with the problem in general but achieves reasonable performance for large instances of the problem. However, we show that our hybridisation improves both the quality of the set of solutions (CPLEX+GeNePi and CBLNS+GeNePi improve the solutions by +17.8% against CPLEX alone and +615% against CBLNS alone) and number of solutions (8.9 times more solutions than CPLEX alone and 56.76 times more solutions than CBLNS alone), while the processing time of CPLEX+GeNePi and CBLNS+GeNePi increases only by 6% and 16.4% respectively. Overall, the study shows that CPLEX+GeNePi is the best algorithm for small instances (CBLNS+GeNePi only gets 45.2% of CPLEX+GeNePi's hypervolume) while CBLNS+GeNePi is better than the others on large instances (that CPLEX+GeNePi cannot address). [ABSTRACT FROM AUTHOR]

The traditional vector-based classifiers, such as support vector machine (SVM) and twin support vector machine (TSVM), cannot handle tensor data directly and may not utilize the data informations effectively. In this paper, we propose a novel classifier based on tensor data, called twin bounded support tensor machine (TBSTM) which is an extension of twin bounded support vector machine (TBSVM). Similar to TBSVM, TBSTM gets two hyperplanes and obtains the solution by solving two quadratic programming problems (QPPs). The computational complexity of each QPPs is smaller than that of support tensor machine (STM). TBSTM not only retains the advantage of TBSVM, but also has its unique superior characteristics: (1) it makes full use of the structure information of data; (2) it has acceptable or better classification accuracy compared to STM, TBSVM and SVM; (3) the computational cost is basically less than STM; (4) it can deal with large data that TBSVM is not easy to achieve, especially for small-sample-size (S3) problems; (5) it adopts alternating successive over relaxation iteration (ASOR) method to solve optimization problems which accelerates the pace of training. Finally, we demonstrate the effectiveness and superiority by the experiments based on vector and tensor data. [ABSTRACT FROM AUTHOR]

This paper investigates a single-machine scheduling problem involving both the due-window assignment and position-dependent processing times under a group technology environment. By position-dependent processing times, we mean that the processing time of a job is dependent of its processing position in the job sequence within the group it belongs to. A setup time is incurred whenever the single machine transfers job processing from a group to another group. Each group is assigned an assignable common due-window. A job completed earlier (respectively, later) than the common due-window of the group it belongs to will incur an earliness (respectively, tardiness) penalty. The objective is to determine the optimal group sequence, the optimal job sequence, and the optimal due-window assignment so as to minimize the total cost including the earliness and tardiness (or weighted number of tardy jobs) penalties, black and the due-window starting time and due-window size costs. We show that both the problems can be solved in polynomial times. [ABSTRACT FROM AUTHOR]

In the process of selecting a machine learning algorithm to solve a problem, questions like the following commonly arise: (1) Are some algorithms basically the same, or are they fundamentally different? (2) How different? (3) How do we measure that difference? (4) If we want to combine algorithms, what algorithms and combinators should be tried? This research proposes COD (Classifier Output Difference) distance as a diversity metric. COD separates difference from accuracy, COD goes beyond accuracy to consider differences in output behavior as the basis for comparison. The paper extends earlier on COD by giving a basic comparison to other diversity metrics, and by giving an example of using COD data as a predictive model from which to select algorithms to include in an ensemble. COD may fill a niche in metalearning as a predictive aid to selecting algorithms for ensembles and hybrid systems by providing a simple, straightforward, computationally reasonable alternative to other approaches. [ABSTRACT FROM AUTHOR]

It is commonly understood that machine learning algorithms discover and extract knowledge based on data at hand. However, a huge amount of knowledge is available which is in machine-readable format and ready for inclusion in machine learning algorithms and models. In this paper, we propose a framework that integrates domain knowledge in form of ontologies/hierarchies into logistic regression using stacked generalization. Namely, relations from ontology/hierarchy are used in stacking manner in order to obtain higher, more abstract concepts. Obtained concepts are further used for prediction. The problem we solved is unplanned 30-days hospital readmission, which is considered as one of the major problems in healthcare. Proposed framework yields better results compared to Ridge, Lasso, and Tree Lasso Logistic Regression. Results suggest that the proposed framework improves AUC by up to 9.5% on pediatric datasets and up to 4% on morbidly obese patients' datasets and also improves AUPRC by up to 5.7% on pediatric datasets and up to 2.6% on morbidly obese patients' datasets on average. This indicates that the inclusion of domain knowledge improves the predictive performance of Logistic Regression. [ABSTRACT FROM AUTHOR]

One of the serious challenges in machine learning and pattern recognition is to transfer knowledge from related but different domains to a new unlabeled domain. Feature selection with maximum mean discrepancy (f-MMD) is a novel and effective approach to transfer knowledge from source domain (training set) into target domain (test set) where training and test sets are drawn from different distributions. However, f-MMD has serious challenges in facing datasets with large number of samples and features. Moreover, f-MMD ignores the feature-label relation in finding the reduced representation of dataset. In this paper, we exploit jointly transfer learning and class discrimination to cope with domain shift problem on which the distribution difference is considerably large. We therefore put forward a novel transfer learning and class discrimination approach, referred to as RandOm k-samplesets feature Weighting Approach (ROWA). Specifically, ROWA reduces the distribution difference across domains in an unsupervised manner where no label is available in the test set. Moreover, ROWA exploits feature-label relation to separate various classes alongside the domain transfer, and augments the relation of selected features and source domain labels. In this work, we employ disjoint/overlapping small-sized samplesets to iteratively converge to final solution. Employment of local sets along with a novel optimization problem constructs a robust and effective reduced representation for adaptation across domains. Extensive experiments on real and synthetic datasets verify that ROWA can significantly outperform state-of-the-art transfer learning approaches. [ABSTRACT FROM AUTHOR]

The -job and -machine permutation flow shop scheduling problem with a proportional deterioration is considered in which all machines process the jobs in the same order, i.e., a permutation schedule. A proportional deterioration means that the job deterioration as an increasing function that is proportional to a linear function of time. The objective is to minimize the makespan, i.e., the maximum completion time. When some dominant relationships between machines can be satisfied, we show that some special cases of the problem can be polynomial solvable. For the general case, we also propose a heuristic algorithm and give the computational experiments. [ABSTRACT FROM AUTHOR]

The stereo correspondence problem is one of the most pre-eminent problems in a stereo vision system. With the right correspondence, a stereo vision system can help cap over diverse problems, while on the other hand, a wrong correspondence can be costly. While the performance of a feature-based correspondence approach is exceptional, the method can still produce wrong correspondences. This work presents an amalgam of feature-based and correlation-based correspondence, where the local pixels around a feature pair are compared using Structural SIMilarity index (SSIM), enhancing the correspondences, and a semantic-based filtering module, which further filters the obtained corresponding features using semantic data whenever detected in both the stereo image pair. While approaches in the literature are focused towards finding better features and their representation, the proposed approach advocates that correlation-based verification of the features can filter out bad correspondences, and in addition, aided by semantic-level filtering. These two modules establish the novelty of the work. The proposed correspondence matching algorithm is used to solve the problem of Visual Odometry to let a low-cost robot compute its pose in a novel environment. The experimental results show adequate filtering of wrong feature correspondence wherein, different environments with different lighting conditions were also considered. The proposed approach outperformed numerous state-of-the-art approaches available in the literature. The visual odometry algorithm using the proposed correspondence matching is compared against classical methods and a deep learning method, and it is observed that the proposed approach delivers lower trajectory error values in most scenarios on the KITTI dataset sequences. [ABSTRACT FROM AUTHOR]

Code clone refers to two or more identical or similar source code fragments. Research on code clone detection has lasted for decades. Investigation and evaluation of existing clone detection techniques indicate that they are resilient to function-level clone detection. Still, there may be room for further research in block-level clone detection. Particularly, type-3 clones that include large gaps, are ongoing challenges. To solve these problems, we propose a clone detection method based on multiple code features. It aims to improve the recall rate of code block clone detection and overcome large-gap and hard-to-detect type-3 clones. This method first splits the source code files based on the program's structural features and context features to obtain code blocks. The collection of code blocks obtained in this way is complete, and the large gaps in clone pairs will also be removed. In addition, we only need to compute the similarity between code blocks with the same structural features, which can also significantly save time and resources. The similarity is obtained by calculating the proportion of the same tokens between two code blocks. Moreover, since different types of tokens have different weights in similarity calculation, we use supervised learning to obtain a classifier model between token features and code clone. We divide the tokens into 13 types and train the machine learning model with the manually confirmed clone or non-clone pair. Finally, we develop a prototype system and compare our tools with existing tools under the Mutation Framework and in several actual C projects. The experimental results also demonstrate the advancement and practicality of our prototype. [ABSTRACT FROM AUTHOR]

The rapid growth of communication networking, ubiquitous sensing, and signal processing, has promoted the development of the Internet of Things (IoT). However, the IoT is essentially dynamic and has no clearly defined network boundary, unauthorized access and data leakage may be much easier. Attribute-based access control (ABAC) can solve the problem of fine-grained access control and large-scale user dynamic expansion in complex information systems, and provides an ideal access control solution for an open network environment, which is more suitable for the dynamic access environment of IoT. However, the dynamic nature of IoT brings new challenges to access control. On the one hand, as new devices and services are continuously deployed, administrators need to manually formulate new rules, which is time-consuming and error-prone. On the other hand, as the IoT environment is continuously changing, the access policy easily becomes unsuitable for the current environment. In order to solve the above two problems, we propose a new scheme named Policy Maintenance-based machine learning (PMML), which includes two modules named Policy Generalization (PG) and Policy Evaluation (PE). After the access control model is deployed, automated PG and PE are carried out to maintain the rule set. In the PG module, we define a novel measure, resource similarity, and integrate it into policy mining so that policies could generalize among related resources. In the PE module, we introduce a quantitative method to assess rules and prune rules of low-quality. We conduct our experiments on real-world enterprise access logs from Amazon, and thoroughly analyzed the effects of different hyper-parameters on the experimental results. The experimental results have qualitatively and quantitatively shown the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

Person re-identification (Re-ID) is a research hot spot in the field of intelligent video analysis, and it is also a challenging task. As the number of samples grows larger, traditional metric and feature learning methods fall into bottleneck, while it just meets the needs of deep learning algorithm, which perform very well in person re-identification. Although they have achieved good results in the field of supervised learning, their application in real-world scenarios is not very satisfactory. This is mainly because in the real world, a huge number of labeled images are hard to obtain, and even if they are obtained, the cost is expensive. Meanwhile, the performance of deep learning in unsupervised metrics is not ideal. For solving the problem, we propose a new method based on unsupervised domain adaptation (UDA) and re-ranking, and name it UDA +. As for this method, we first train a camera-aware style transfer model to gain camstyle images. Then we further reduce the difference between the domain of the target and source by using invariant feature, and further improve their commonality. In addition, re-ranking is also introduced to optimize the matching results. This method can not only reduce the cost of obtaining labeled data, but also improve the accuracy. Experimental results show that our method can outperform the most advanced method by 4% on Rank-1 and 14% on mAP. The results also better confirm the effectiveness of Re-ranking module and provide a new idea for domain adaptation by unsupervised methods in the future. [ABSTRACT FROM AUTHOR]

A change and maintenance method is proposed based on the change propagation model and the procedure model information for solving data maintenance problem of 3D machining procedure model change and to help improve the flexibility of 3D machining procedure model and the reliability of the change result. Design Structure Matrix (DSM) is established by analyzing the relationships between machining features in the machining process route to obtain all possible propagation paths. On the basis of obtained paths, machining features that may be affected and machining procedure models related to machining features are stored by the structured method. Algorithms to solve the problems of adding, deleting and modifying machining features are proposed to realize the change and maintenance of 3D machining procedure model by combining machining procedure model's information in the machining process route. In the end, some numerical examples are given to explain both rationality and feasibility of the proposed approaches. [ABSTRACT FROM AUTHOR]

Oft-decried information overload is a serious problem that negatively impacts the comprehension of information in the digital age. Text summarization is a helpful process that can be used to alleviate this problem. With the aim of seeking a novel method to enhance the performance of multi-document summarization, this study proposes a novel approach to analyze the problem of multi-document summarization based on a mixture model, consisting of a contextual topic model from a Bayesian hierarchical topic modeling family for selecting candidate summary sentences, and a regression model in machine learning for generating the summary. By investigating hierarchical topics and their correlations with respect to the lexical co-occurrences of words, the proposed contextual topic model can determine the relevance of sentences more effectively, recognize latent topics, and arrange them hierarchically. The quantitative evaluation results from a practical application demonstrates that a system implementing this model can significantly improve the performance of summarization and make it comparable to state-of-the-art summarization systems. [ABSTRACT FROM AUTHOR]

Extreme Learning Machine (ELM) is a recently proposed algorithm, efficient and fast for learning the parameters of single layer neural structures. One of the main problems of this algorithm is to choose the optimal architecture for a given problem solution. To solve this limitation several solutions have been proposed in the literature, including the regularization of the structure. However, to the best of our knowledge, there are no works where such adjustment is applied to classification problems in the presence of a non-linearity in the output; all published works tackle modelling or regression problems. Our proposal has been applied to a series of standard databases for the evaluation of machine learning techniques. Results obtained in terms of classification success rate and training time, are compared to the original ELM, to the well known Least Square Support Vector Machine (LS-SVM) algorithm and with two other methods based on the ELM regularization: Optimally Pruned Extreme Learning Machine (OP-ELM) and Bayesian Extreme Learning Machine (BELM). The obtained results clearly demonstrate the usefulness of the proposed method and its superiority over a classical approach. [ABSTRACT FROM AUTHOR]

A new method for reinforcement fuzzy controllers is presented by this article. The method uses Artificial Bee Colony algorithm based on Q-Value to control reinforcement fuzzy system; the algorithm is called Artificial Bee Colony-Fuzzy Q learning (ABC-FQ). In fuzzy inference system, precondition part of rules is generated by prior knowledge, but ABC-FQ algorithm is responsible to achieve the best combination of actions for the consequence part of the rules. In ABC-FQ algorithm, each combination of actions is considered a food source for consequence part of the rules and the fitness level of this food source is determined by Q-Value. ABC-FQ Algorithm selects the best food resource, which is the best combination of actions for fuzzy system, using Q criterion. This algorithm tries to generate the best reinforcement fuzzy system to control the agent. ABC-FQ algorithm is used to solve the problem of Truck Backer-Upper Control, a reinforcement fuzzy control. The results have indicated that this method arrives to a result with higher speed and fewer trials in comparison to previous methods. [ABSTRACT FROM AUTHOR]

In this study, we consider a scheduling problem with truncated exponential sum-of-logarithm-processing-times based and position-based learning effects on a single machine. We prove that the shortest processing time (SPT) rule is optimal for the makespan minimization problem, the sum of the θth power of job completion times minimization problem, and the total lateness minimization problem, respectively. For the total weighted completion time minimization problem, the discounted total weighted completion time minimization problem, the maximum lateness minimization problem, we present heuristic algorithms (the worst-case bound of these heuristic algorithms are also given) according to the corresponding single machine scheduling problems without learning considerations. It also shows that the problems of minimizing the total tardiness, the total weighted completion time and the discounted total weighted completion time are polynomially solvable under some agreeable conditions on the problem parameters. [ABSTRACT FROM AUTHOR]

The note deals with machine scheduling problems with a more general learning effect model, i.e., the actual job processing time is a function of the sum of the function of the processing times of the jobs already processed and job position. We show that some single machine scheduling problems are still polynomially solvable under the proposed model. We also show that some special cases of the flow shop scheduling problems can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

This study proposed a pornography classifier using multi-agent learning as a combination of the Bayesian method using color features extracted from skin detection based on the YCbCr color space and the back-propagation neural network method using shape features also extracted from skin detection. The classification of pornographic images was made more robust to the variation of images despite size engineering problems. Previous studies failed to achieve such robustness. Findings showed that the proposed multi-agent learning-based pornography classifier has produced significant TP and TN average rates (i.e., 96% and 97.33%, respectively). In addition, the proposed classifier has achieved a significantly low average rate of FN and FP (i.e., only 4% and 2.67%, respectively). The implementation of this algorithm is crucial and significant not only in identifying pornography but also in blocking Web sites that covertly promote pornography. [ABSTRACT FROM AUTHOR]