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Computer Vision (CV) has become an essential field in Artificial Intelligence applications. Object detection and recognition (ODR) is one of the fundamental tasks of computer vision implementations. However, developing an efficient ODR model is still a significant problem. The model's execution time and speed are the most critical features during the inference or detection and recognition process, which need to be improved using the latest object detection architectures. In this paper, the handwritten detection and recognition (HDR) model is developed based on previously known algorithms with their efficiency, such as Faster R-CNN and YOLOv4 in the first hand. On the other hand, two new models capable of detecting and recognizing handwritten digits using the latest ODR algorithm are proposed, one based on the latest YOLO family architecture (YOLOv5-HDR) with high speed and accuracy and the other using the transformers architecture (DETR). To the best of our knowledge, this is the first study to achieve a details comparison between YOLOv5 and transformers-based models in handwritten digit detection. Finally, the detailed performance analysis achieved by the paper proves that the YOLOv4-based model achieved the testing inference 13% faster than Faster R-CNN. However, the proposed YOLOv5-based model outperformed the YOLOv4 and the transformers-based one as it increased the testing execution time 25% faster than the YOLOv4, three times faster than the DETR model. A further adversarial attack test has been conducted to ensure the robust performance of the proposed model. Furthermore, numerical experiment results and their analyses demonstrate the robustness and effectiveness of the proposed YOLOv5-based model being the most stable for handwritten digit detection and recognition tasks. [ABSTRACT FROM AUTHOR]

Computation and decision problems related to argumentation frameworks with higher-order attacks have not received a lot of attention so far. This paper is a step towards these issues. First, it provides a labelling counterpart for the structure semantics of Recursive Argumentation Frameworks (RAF). Second, it investigates the complexity of decision problems associated with RAF. This investigation shows that, for the higher expressiveness offered by these enriched systems, the complexity is the same as for classical argumentation frameworks. As a side contribution, a new semantics for RAF, the semi-stable semantics, and a new process for translating RAF into Argumentation Frameworks without higher-order attacks (AF), are introduced. Finally, new notions which are the counterparts of equivalent notions already existing for AF (among them, the Strongly Connected Components — SCC) are defined and investigated in order to involve them in the future development of algorithms for computing RAF labelling semantics. [ABSTRACT FROM AUTHOR]

In some circumstances, the performance of machine learning (ML) tasks are based on the quality of signal (data) that is processed in these tasks. Therefore, the pre-processing techniques, such as reconstruction and denoising methods, are important techniques in ML tasks. In reconstructed (estimated) method, the fused lasso algorithm with non-convex penalty function is an efficient method when the signal corrupted by additive white Gaussian noise (AWGN) is considered. Therefore, this paper proposes new shrinkage functions for non-convex penalty functions, modified arctangent and exponential models, in fused lasso formulation. A lot of works present the shrinkage function for arctangent penalty function. Unfortunately, there is no closed-form solution. The numerical solution is required for shrinkage function of this penalty function. However, the analytical solution is derived in this paper. Moreover, the shrinkage function of modified exponential penalty function is proposed. This shrinkage function obtains from simple iterative method, fixed-point algorithm. We demonstrate the proposed methods through simulations with standard one-dimensional signals contaminated by AWGN. The proposed techniques are compared with traditional estimation methods, such as total variation (TV) and wavelet denoising methods. In experimental results, our proposed methods outperform several exiting methods both visual quality and in terms of root mean square error (RMSE). In fact, the proposed methods can better preserve the feature of noise-free signal than the compared methods. The denoised signals produced by the proposed methods are less smooth than the denoised signals produced by the compared methods. [ABSTRACT FROM AUTHOR]

Time series change point detection can identify the locations of abrupt points in many dynamic processes. It can help us to find anomaly data in an early stage. At the same time, detecting change points for long, periodic, and multiple input series data has received a lot of attention recently, and is universally applicable in many fields including power, environment, finance, and medicine. However, the performance of classical methods typically scales poorly for such time series. In this paper, we propose CPMAN, a novel prediction-based change point detection approach via multi-stage attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ the multi-stage attention-based networks and integrate the multi-series fusion mechanism. This module can adaptively extract features from the relevant input series and capture the long-term temporal dependencies. Secondly, in the change point detection module, we use the wavelet analysis-based algorithm to detect change points efficiently and identify the change points and outliers. Extensive experiments are conducted on various real-world datasets and synthetic datasets, proving the superiority and effectiveness of CPMAN. Our approach outperforms the state-of-the-art methods by up to 12.1% on the F1 Score. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algorithm for image inpainting using low dimensional feature space. In our method, projecting a low dimensional space from the original space is accomplished firstly using SVD, which is named low rank component, and then the missing pixels are filled in the new space. Finally, the original image is inpainted so that adaptive patch size is considered by quad-tree based on the previous step. In our algorithm, the missing pixels in the target region are estimated twice, one in low dimension feature space and another in the original space. It is noticeable that both processes estimate the unknown pixels using patch-based idea and rank lowering concept. Experimental results of this algorithm show better consistency in comparison with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Due to the inability of the accuracy-driven methods to address the challenging problem of learning from imbalanced data, several alternative measures have been proposed in the literature, like the Area Under the ROC Curve (AUC), the Average Precision (AP), the F-measure, the G-Mean, etc. However, these latter measures are neither smooth, convex nor separable, making their direct optimization hard in practice. In this paper, we tackle the challenging problem of imbalanced learning from a nearest-neighbor (NN) classification perspective, where the minority examples typically belong to the class of interest. Based on simple geometrical ideas, we introduce an algorithm that rescales the distance between a query sample and any positive training example. This leads to a modification of the Voronoi regions and thus of the decision boundaries of the NN classifier. We provide a theoretical justification about this scaling scheme which inherently aims at reducing the False Negative rate while controlling the number of False Positives. We further formally establish a link between the proposed method and cost-sensitive learning. An extensive experimental study is conducted on many public imbalanced datasets showing that our method is very effective with respect to popular Nearest-Neighbor algorithms, comparable to state-of-the-art sampling methods and even yields the best performance when combined with them. [ABSTRACT FROM AUTHOR]

In both practical applications and theoretical analysis, there are many fuzzy chance-constrained optimization problems. Currently, there is short of real-time algorithms for solving such problems. Therefore, in this paper, a continuous-time neurodynamic approach is proposed for solving a class of fuzzy chance-constrained optimization problems. Firstly, an equivalent deterministic problem with inequality constraint is discussed, and then a continuous-time neurodynamic approach is proposed. Secondly, a sufficient and necessary optimality condition of the considered optimization problem is obtained. Thirdly, the boundedness, global existence and Lyapunov stability of the state solution to the proposed approach are proved. Moreover, the convergence to the optimal solution of considered problem is studied. Finally, several experiments are provided to show the performance of proposed approach. [ABSTRACT FROM AUTHOR]

In the last decade a new variety of nature inspired optimization algorithms has been appeared. After the swarm based models, researchers turned their inspiration in nature phenomena and laws of science. In this way a new category of algorithms was born, equally effective or even sometimes superior to known algorithms for optimization problems, like genetic algorithms and swarm intelligence schemes. The present survey depicts the evolution of research on nature inspired optimization algorithms related to physical phenomena and laws of science and discusses the possibilities of using the presented approaches in a number of different applications. An attempt has been made to draw conclusions on what algorithm could be used in which different problem areas, for those approaches that this information could be extracted from the related papers studied. The paper also underlines the usage of this kind of nature inspired algorithms in industrial research problems, due to their better confrontation with optimization problems represented with nodes and edges. [ABSTRACT FROM AUTHOR]

The 3D measurement system based on line-structured light uses a camera to capture laser stripes due to changing in the shape of an object, and uses the acquired pixel coordinates for 3D reconstruction. System calibration is an important step in 3D measurement. The current camera calibration algorithm research mainly focuses on improving the algorithm itself, and there is less research on the influence of external factors. This paper proposes a coplanar hybrid calibration algorithm based on the error screening model by combining the error screening model, mathematical model and neural network model. It is mainly divided into two steps. The first step is to use the radial array constraint calibration algorithm based on the error screening model to solve the camera's internal and external parameters. The second step uses the camera internal and external parameters obtained in the first step to convert the pixel coordinates into real three-dimensional coordinates, and compares the calculated three-dimensional coordinates with the actual coordinates. Using machine learning to establish a compensation network, get a compensation function, and use the resulting 3D world coordinates to perform point cloud stitching. Experiments show that compared with the traditional calibration algorithm, the calibration algorithm has a small error and reduces the calibration error by about 6.5%. [ABSTRACT FROM AUTHOR]

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method. [ABSTRACT FROM AUTHOR]

A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions. [ABSTRACT FROM AUTHOR]

As a relaxation of clique in graph theory, k-plex is a powerful tool for analyzing social networks and identifying cohesive structures in graphs. Recently, more and more researchers have concentrated on the algorithms for the maximum k-plex problem. Among those algorithms, a branch-and-bound algorithm proposed very recently shows a good performance on solving large sparse graphs, but does not work well on social networks. In this paper, we propose two novel vertex selection heuristic strategies for branching. The first one employs historical information of vertex reduction, and the second one is a combination of the first heuristic and the degree-based approach. Intensive experiments on Facebook benchmark show that the algorithm combining our heuristics outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Recent trends in educational technology focus on designing systems that can support students while learning complex psychomotor skills, such as those required when practicing sports and martial arts, dancing or playing a musical instrument. In this context, artificial intelligence can be key to personalize the development of these psychomotor skills by enabling the provision of effective feedback when the instructor is not present, or scaling up to a larger pool of students the feedback that an instructor would typically provide one-on-one. This paper presents the modeling of human motion gathered with inertial sensors aimed to offer a personalized support to students when learning complex psychomotor skills. In particular, when comparing learner data with those of an expert during the psychomotor learning process, artificial intelligence algorithms can allow to: (i) recognize specific motion learning units and (ii) assess learning performance in a motion unit. However, it seems that this field is still emerging, since when reviewed systematically, search results hardly included the motion modeling with artificial intelligence techniques of complex human activities measured with inertial sensors. [ABSTRACT FROM AUTHOR]

Due to the increasing growth of data, many methods are proposed to extract useful data and remove noisy data. Instance selection is one of these methods which selects some instances of a data set and removes others. This paper proposes a new instance selection algorithm based on ReliefF, which is a feature selection algorithm. In the proposed algorithm, based on the Jaccard index, the nearest instances of each class are found for each instance. Then, based on the nearest neighbor's set, the weight of each instance is calculated. Finally, only instances with more weights are selected. This algorithm can reduce data at a specified rate and have the ability to run parallel on the instances. It can work on a variety of data sets with nominal and numeric data with missing values and is also suitable for working with imbalanced data sets. The proposed algorithm tests on three data sets. Results show that the proposed algorithm can reduce the volume of data, without a significant change in classification accuracy of these datasets. [ABSTRACT FROM AUTHOR]

The potential applications and challenges of uncertain data mining have recently attracted interests from researchers. Most uncertain data mining algorithms consider aleatory (random) uncertainty of data, i.e. these algorithms require that exact probability distributions or confidence values are attached to uncertain data. However, knowledge about uncertainty may be incomplete in the case of epistemic (incomplete) uncertainty of data, i.e. probabilities of uncertain data may be imprecise, coarse, or missing in some applications. The paper focuses on uncertain data which miss probabilities, specially, value-uncertain discrete objects which miss probabilities (for short uncertain objects). On the other hand, classification is one of the most important tasks in data mining. But, to the best of our knowledge, there is no method to learn Naïve Bayesian classifier from uncertain objects. So the paper studies Naïve Bayesian classification of uncertain objects. Firstly, the paper defines interval probabilities of uncertain objects from probabilistic cardinality point of view, and bridges the gap between uncertain objects and the theory of interval probability by proving that interval probabilities are F-probabilities. Secondly, based on the theory of interval probability, the paper defines conditional interval probabilities including the intuitive concept and the canonical concept, and the conditional independence of the intuitive concept. Further, the paper gives a formula to effectively compute the intuitive concept. Thirdly, the paper presents a Naïve Bayesian classifier with interval probability parameters which can handle both uncertain objects and certain objects. Finally, experiments with uncertain objects based on UCI data show satisfactory performances. [ABSTRACT FROM AUTHOR]

This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time. [ABSTRACT FROM AUTHOR]

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm. [ABSTRACT FROM AUTHOR]

Distributed Constraint Optimization Problem (DCOP) is a powerful paradigm to model multi-agent systems through enabling multiple agents to coordinate with each other to solve a problem. These agents are often assumed to be cooperative, that is, they communicate with other agents in order to optimize a global objective. However, the communication times between all pairs of agents are assumed to be identical in the evaluation of most DCOP algorithms. This assumption is impractical in almost all real-world applications. In this paper, we study the impact of empirically evaluating a DCOP algorithm under the assumption that communication times between pairs of agents can vary. In addition, we evaluate a DCOP algorithm using ns-2, a discrete-event simulator that is widely used in the computer networking community, to simulate the communication times, as opposed to the standard DCOP simulators that are used to evaluate DCOP algorithms in the AI community. Furthermore, we propose heuristics that exploit the non-uniform communication times to speed up DCOP algorithms that operate on pseudo-trees. Our empirical results demonstrate that the proposed heuristics improve the runtime of those algorithms up to 20%. These heuristics are evaluated on different benchmarks such as scale-free graphs, random graphs, and an instance of the smart grid, Customer-Driven Microgrid (CDMG) application. [ABSTRACT FROM AUTHOR]

Several propositional satisfiability (SAT) based encodings have been proposed to deal with various data mining problems including itemset and sequence mining problems. This research issue allows to model data mining problems in a declarative way, while exploiting efficient SAT solving techniques. In this paper, we overview our contributions on the application of propositional satisfiability (SAT) to model and solve itemset mining tasks. We first present a SAT based encoding of frequent closed itemset mining task as a propositional formula whose models corresponds to the patterns to be mined. Secondly, we show that some data mining constraints can be avoided by reformulation. We illustrate this issue by reformulating the closeness constraint using the notion of minimal models. Finally, we also addressed the scalability issue, one of the most important challenge of these nice declarative framework. To this end, we proposed a complete partition based approach whose aim is to avoid encoding the whole database as a single formula. Using a partition on the set of items, our new approach leads to several propositional formulas of reasonable size. The experimental evaluation on several known datasets shows huge improvements in comparison to the direct approach without partitioning, while reducing significantly the performance gap with respect to specialized algorithms. [ABSTRACT FROM AUTHOR]

In recent years, a new research area known as privacy preserving data mining (PPDM) has emerged and captured the attention of many researchers interested in preventing the privacy violations that may occur during data mining. In this paper, we provide a review of studies on PPDM in the context of association rules (PPARM). This paper systematically defines the scope of this survey and determines the PPARM models. The problems of each model are formally described, and we discuss the relevant approaches, techniques and algorithms that have been proposed in the literature. A profile of each model and the accompanying algorithms are provided with a comparison of the PPARM models. [ABSTRACT FROM AUTHOR]

At present many algorithms for mining association rules have been proposed, but most of them are only suitable for discovering specific frequent itemsets from characteristic data sets on the appointed environments, namely, these algorithms are not general enough when mining association rules. In this paper, a general framework based on composite granules for mining association rules is proposed, which is a general data mining model without appointed restriction from frequent itemsets, data sets or mining environments and so on. An iterative method is efficiently applied to the general mining framework for discovering frequent itemsets, which adopts repartitioning frequent attributes to iteratively reconstruct the mixed radix information system for reducing a relational database. In order that the framework for discovering frequent itemsets has a generality, in discussing and establishing the general mining framework, this paper introduces a novel conception and data model, namely, a mixed radix information system is applied to describe a relational database, and a composite granules is used to build a specific relationship between an information system and a mixed radix information system, which can hold the same extension and simultaneously exist in two different information systems. The mixed radix information system can help the general framework to reduce information data and improve the performance of the framework for generating frequent itemsets. The composite granules model can create a relationship between an information granule and a digital information granule, and help the framework for computing the support to avoid reading the database repeatedly or using the complex data structure. Finally, a new taxonomy is presented to verify the generality and the high efficiency of the mining framework and all the experiments based on the taxonomy indicate that the general mining framework has the required generality, and the performance of the framework is better than these classical mining frameworks. [ABSTRACT FROM AUTHOR]

There are several reasons to expect that recognising word order errors in a text will be a difficult problem, and recognition rates reported in the literature are in fact low. Although grammatical rules constructed by computational linguists improve the performance of a grammar checker in word order diagnosis, the repairing task is still very difficult. This paper describes a method to repair any sentence with wrong word order using a statistical language model (LM). A good indicator of whether a person really knows a language is the ability to use the appropriate words in a sentence in correct word order. The "scrambled" words in a sentence produce a meaningless sentence. Most languages have a fairly fixed word order. This paper introduces a method, which is language independent, for repairing word order errors in sentences using the probabilities of most typical trigrams and bigrams extracted from a large text corpus such as the British National Corpus (BNC). [ABSTRACT FROM AUTHOR]

This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the evolutionary feedforward neural networks seemed to have better of equal accuracy than the original backpropagation feedforward neural network. We found few differences in the accuracy of the networks solved by applying the EAs, but found ample differences in the execution time. The results suggest that the evolutionary feedforward neural network with random search algorithm might be the best algorithm on the data sets we tested. [ABSTRACT FROM AUTHOR]

In the pickup and delivery problem with time windows (PDPTW), vehicles have to transport loads from origins to destinations respecting capacity and time constraints. In this paper, we present a two-phase method to solve the PDPTW. In the first phase, we apply a novel construction heuristics to generate an initial solution. In the second phase, a tabu search method is proposed to improve the solution. Another contribution of this paper is a strategy to generate good problem instances and benchmarking solutions for PDPTW, based on Solomon' s benchmark test cases for VRPTW. Experimental results show that our approach yields very good solutions when compared with the benchmarking solutions. [ABSTRACT FROM AUTHOR]

Discovering different groups, or called classes, is useful for pattern recognition, data preprocessing, association analysis, query optimization, etc. To make every object satisfied as much as possible, the groups are generated by the associations or behaviors among participating objects other than the attributes owned by themselves. By mainly considering the mutual associations among the given objects and based on the game theory, in this paper we study the multi-objective oriented categorization. Based on the idea of Shapley value in the coalitional game, we first propose the concept of priority groups and give the efficient algorithm for computing the satisfaction degree of players in a group. Based on the idea of strategic games and Nash equilibrium, we then give the algorithm for computing an approximate equilibrium to solve the conflicts between the strategies of players, and consequently achieve the ultimate multi-objective oriented groups. Preliminary experiments and performance studies verify the efficiency and effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Multi-core and multi-processor architectures have predominated the domain of embedded systems permitting easy mapping of concurrent applications to such architectures. The programs, in general, are subjected to significant optimizing and parallelizing transformations, automated and also human guided, before being mapped to an architecture. Modelling parallel behaviour and formally verifying that their functionality is preserved during synthesis are challenging tasks. Untimed PRES+ models are found to be suitable for the specification of parallel behaviour. Path cover oriented equivalence checking methods have been found to be quite effective for sequential behaviour. Path construction for parallel behaviour, however, is significantly more complex than that for sequential behaviour due to all possible interleavings of the parallel operations. Identification of the path covers depends upon choosing appropriate cut-points. In this paper, the need for introducing cut-points dynamically has been underlined and a mechanism to achieve this task is proposed. Details on how to construct a path cover using dynamic cut-points is presented. [ABSTRACT FROM AUTHOR]

In this paper, we consider optimal scheduling algorithms for scientific workows with two typical structures, fork&join and tree, on a set of provisioned (virtual) machines under budget and deadline constraints in cloud computing. First, given a total budget B, by leveraging a bi-step dynamic programming technique, we propose optimal algorithms in pseudo-polynomial time for both workows with minimum scheduling length as a goal. Our algorithms are efficient if the total budget B is polynomially bounded by the number of jobs in respective workows, which is usually the common case in practice. Second, we consider the dual of this optimization problem to minimize the cost when the deadline of the computation D is fixed. We change this problem into the standard multiple-choice knapsack problem via a parallel transformation. [ABSTRACT FROM AUTHOR]

Differing from the conventional back-propagation (BP) neural networks, a novel multi-input Euler polynomial neural network, in short, MIEPNN (specifically, 4-input Euler polynomial neural network, 4IEPNN) is established and investigated in this paper. In order to achieve satisfactory performance of the established MIEPNN, a weights and structure determination (WASD) algorithm with pruning-while-growing (PWG) and twice-pruning (TP) techniques is built up for the established MIEPNN. By employing the weights direct determination (WDD) method, the WASD algorithm not only determines the optimal connecting weights between hidden layer and output layer directly, but also obtains the optimal number of hidden-layer neurons. Specifically, a sub-optimal structure is obtained via the PWG technique, then the redundant hidden-layer neurons are further pruned via the TP technique. Consequently, the optimal structure of the MIEPNN is obtained. To provide a reasonable choice in practice, several different MATLAB computing routines related to the WDD method are studied. Comparative numerical-experiment results of the 4IEPNN using these different MATLAB computing routines and the standard multi-layer perceptron (MLP) neural network further verify the superior performance and efficacy of the proposed MIEPNN equipped with the WASD algorithm including PWG and TP techniques in terms of training, testing and predicting. [ABSTRACT FROM AUTHOR]

Cluster validation is an essential technique in all cluster applications. Several validation methods measure the accuracy of cluster structure. Typical methods are geometric, where only distance and membership form the core of validation. Yao's decision theory is a novel approach for cluster validation, which evolved loss calculations and probabilistic based measure for determining the cluster quality. Conventional rough set algorithms have utilized this validity measure. This paper propagates decision theory, an unprecedented validation scheme for Rough-Fuzzy clustering by resolving loss and probability calculations to predict the risk measure in clustering techniques. Experiments with synthetic and UCI datasets have been performed, proven to deduce the optimal number of clusters overcoming the downsides of traditional validation frameworks. The proposed index can also be applied to other clustering algorithms and extends the usefulness in business oriented data mining. [ABSTRACT FROM AUTHOR]

This paper presents a new method to estimate reflectance properties of non-Lambertian surface by the least-mean-square (LMS) algorithm. In this paper, hybrid reflectance of an object is represented by the Torrance-Sparrow model. We determine reflectance parameters which minimize the sum squared difference of the intensity distribution between the image of a sample sphere and the calculated image. The estimated reflectance parameters provide the range data with intensity distributions. Therefore, we generate three reference images of a range sphere, which has the same diameter as that of the sample, from the same viewpoint with different light directions. Direct matching of the object images to the references can precisely reconstruct the shape of the object. This paper uses a plate diffuse illumination to alleviate the effects of specular spike and highlights. The simulation results show that the proposed method can estimate reflectance properties of the hybrid surface, and also recover the object shape. [ABSTRACT FROM AUTHOR]

Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFCM) algorithm is the hybridization of fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms which overcomes the problem of noise in the FCM algorithm and coincident clusters problem in the PCM algorithm. A major challenge posed in the PFCM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm for Atanassov's intuitionistic fuzzy sets (A-IFS) which includes the advantages of the PCM, FCM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFCM algorithm. The experimental results demonstrate that the proposed algorithm yields better result. [ABSTRACT FROM AUTHOR]

In this paper, we employ multiple Single-hidden Layer Feedforward Neural Networks for multi-view action recognition. We propose an extension of the Extreme Learning Machine algorithm that is able to exploit multiple action representations and scatter information in the corresponding ELM spaces for the calculation of the networks' parameters and the determination of optimized network combination weights. The proposed algorithm is evaluated by using two state-of-the-art action video representation approaches on five publicly available action recognition databases designed for different application scenarios. Experimental comparison of the proposed approach with three commonly used video representation combination approaches and relating classification schemes illustrates that ELM networks employing a supervised view combination scheme generally outperform those exploiting unsupervised combination approaches, as well as that the exploitation of scatter information in ELM-based neural network training enhances the network's performance. [ABSTRACT FROM AUTHOR]

Computational performance associated with high dimensional data is a common challenge for real-world action classification systems. Subspace learning, and manifold learning in particular, have received considerable attention as means of finding efficient low-dimensional representations that lead to better classification and efficient processing. A Grassmann manifold is a space that promotes smooth surfaces where points represent subspaces. In this paper, Grassmannian Spectral Regression (GRASP) is presented as a Grassmann inspired subspace learning algorithm that combines the benefits of Grassmann manifolds and spectral regression for fast and accurate classification. GRASP involves embedding high dimensional action subspaces as individual points onto a Grassmann manifold, kernelizing the embeddings onto a projection space, and then applying Spectral Regression for fast and accurate action classification. Furthermore, spatiotemporal action descriptions called Motion History Surfaces and Motion Depth Surfaces are utilized. The effectiveness of GRASP is illustrated for computationally intensive, multi-view and 3D action classification datasets. [ABSTRACT FROM AUTHOR]

In this paper, we proposed an edge weight method for performing a community detection on mixed scale-free networks.We use the phrase 'mixed scale-free networks' for networks where some communities have node degree that follows a power law similar to scale-free networks, while some have node degree that follows normal distribution. In this type of network, community detection algorithms that are designed for scale-free networks will have reduced accuracy because some communities do not have scale-free properties. On the other hand, algorithms that are not designed for scale-free networks will also have reduced accuracy because some communities have scale-free properties. To solve this problem, our algorithm consists of two community detection steps; one is aimed at extracting communities whose node degree follows power law distribution (scale-free), while the other one is aimed at extracting communities whose node degree follows normal distribution (non scale-free). To evaluate our method, we use NMI - Normalized Mutual Information - to measure our results on both synthetic and real-world datasets comparing with both scale-free and non scale-free community detection methods. The results show that our method outperforms all other based line methods on mixed scale-free networks. [ABSTRACT FROM AUTHOR]

Linking named entities to structured knowledge sources paves the way for state-of-the-art Web intelligence applications which assign sentiment to the correct entities, identify trends, and reveal relations between organizations, persons and products. For this purpose this paper introduces Recognyze, a named entity linking component that uses background knowledge obtained from linked data repositories, and outlines the process of transforming heterogeneous data silos within an organization into a linked enterprise data repository which draws upon popular linked open data vocabularies to foster interoperability with public data sets. The presented examples use comprehensive real-world data sets from Orell Füssli Business Information, Switzerland's largest business information provider. The linked data repository created from these data sets comprises more than nine million triples on companies, the companies' contact information, key people, products and brands. We identify the major challenges of tapping into such sources for named entity linking, and describe required data pre-processing techniques to use and integrate such data sets, with a special focus on disambiguation and ranking algorithms. Finally, we conduct a comprehensive evaluation based on business news from the New Journal of Zurich and AWP Financial News to illustrate how these techniques improve the performance of the Recognyze named entity linking component. [ABSTRACT FROM AUTHOR]

Over the last decade a vast number of businesses have developed online e-shops in the web. These online stores are supported by sophisticated systems that manage the products and record the activity of customers. There exist many research works that strive to answer the question 'what items are the customers going to like' given their historical profiles. However, most of these works do not take into account the time dimension and cannot respond efficiently when data are huge. In this paper, we study the problem of recommendations in the context of multi-relational stream mining. Our algorithm 'xStreams' first separates customers based on their historical data into clusters. It then employs collaborative filtering (CF) to recommend new items to the customers based on their group similarity. To evaluate the working of xStreams, we use a multi-relational data generator for streams. We evaluate xStreams on real and synthetic datasets. [ABSTRACT FROM AUTHOR]

A framework for constructing robust classification models is proposed in the paper. An assumption about importance of one of the classes in comparison with other classes is incorporated into the models. It often takes place in the real application, for example, in reliability, in medical diagnostic, etc. A main idea underlying the models is to consider a set of probability distributions on training examples produced by the imprecise probability models such as linear-vacuous mixture and constant odd-ratio contaminated models. Extreme points of the sets of probability distributions are a main tool for constructing the robust classifiers. It is shown that algorithms for computing optimal classification parameters are reduced to a finite number of weighted support vector machines with weights determined by the extreme points. Experimental results with synthetic and real data illustrate the proposed models. [ABSTRACT FROM AUTHOR]

Problems of motion coordination of multiple entities are addressed in this paper. These problems are dealt on the abstract level where they can be viewed as tasks of constructing a spatial-temporal plan for a set of identical mobile entities. The entities reside in a certain environment where they can move. Each entity need to reach a given goal position supposed it is starting from some initial position. The most abstract formal representations of coordinated motion problems are known as 'pebble motion on a graph' and 'multi-robot path planning'. The existent algorithms for pebble motion and multi-robot problems were suspected of generating solutions containing redundancies. This hypothesis eventually confirmed in this work. We present several techniques for identifying and eliminating redundancies from solutions generated by existent algorithms. An extensive experimental evaluation was performed and it showed that the quality of generated solutions can be improved up to the order of magnitude. We also identify parameters characterizing instances of problems where a significant improvement is expectable. [ABSTRACT FROM AUTHOR]

A strong assumption to derive generalization guarantees in the standard PAC framework is that training (or source) data and test (or target) data are drawn according to the same distribution. Because of the presence of possibly outdated data in the training set, or the use of biased collections, this assumption is often violated in real-world applications leading to different source and target distributions. To go around this problem, a new research area known as Domain Adaptation (DA) has recently been introduced giving rise to many adaptation algorithms and theoretical results in the form of generalization bounds. This paper deals with self-labeling DA whose goal is to iteratively incorporate semi-labeled target data in the learning set to progressively adapt the classifier from the source to the target domain. The contribution of this work is three-fold: First, we provide the minimum and necessary theoretical conditions for a self-labeling DA algorithm to perform an actual domain adaptation. Second, following these theoretical recommendations, we design a new iterative DA algorithm, called GESIDA, able to deal with structured data. This algorithm makes use of the new theory of learning with (ε,γ,τ)-good similarity functions introduced by Balcan et al., which does not require the use of a valid kernel to learn well and allows us to induce sparse models. Finally, we apply our algorithm on a structured image classification task and show that self-labeling domain adaptation is a new original way to deal with scaling and rotation problems. [ABSTRACT FROM AUTHOR]

In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement. [ABSTRACT FROM AUTHOR]

Effective feature selection methods are important for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. Extensive research has been done to improve the performance of individual feature selection methods. However, it is always a challenge to come up with an individual feature selection method which would outperform other methods in most cases. In this paper, we explore the possibility of improving the overall performance by combining multiple individual feature selection methods. In particular, we propose a method of combining multiple feature selection methods by using an information fusion paradigm, called Combinatorial Fusion Analysis (CFA). A rank-score function and its associated graph, called rank-score graph, are adopted to measure the diversity of different feature selection methods. Our experimental results demonstrated that a combination of multiple feature selection methods can outperform a single method only if each individual feature selection method has unique scoring behavior and relatively high performance. Moreover, it is shown that the rank-score function and rank-score graph are useful for the selection of a combination of feature selection methods. [ABSTRACT FROM AUTHOR]

Due to the infeasibility of randomized controlled experiments, the existence of unobserved variables and the fact that equivalent direct acyclic graphs obtained generally can not be distinguished, it is difficult to learn the true causal relations of original graph. This paper presents an algorithm called BSPC based on adjacent nodes to learn the structure of Causal Bayesian Networks with unobserved variables by using observational data. It does not have to adjust the structure as the existing algorithms FCI and MBCS*, while it can guarantee to obtain the true adjacent nodes. More important is that algorithm BSPC reduces computational complexity and improves reliability of conditional independence tests. Theoretical results show that the new algorithm is correct. In addition, the advantages of BSPC in terms of the number of conditional independence tests and the number of orientation errors are illustrated with simulation experiments from which we can see that it is more suitable in order to learn the structure of Causal Bayesian Networks with latent variables. Moreover a better latent structure representation is returned. [ABSTRACT FROM AUTHOR]

Many decision tree algorithms were proposed over the last few decades. A lack of publishing standards for decision tree algorithm software produced a large time gap between algorithm proposals and their wider application in practice. Non-existence of common repository for storing algorithms and their parts led to a need to re-implement these algorithms from a scratch when they had to be implemented on a different platform. This makes the comparison between algorithms and their partial improvements vague. In addition, combinations and interactions between different algorithm parts haven't been analyzed thoroughly. Reusable component design of decision tree algorithms has been recently suggested as a potential solution to these problems. In this paper we describe an architecture for component-based (white-box) decision tree algorithm design, and we present an open-source framework which enables design and fair testing of decision tree algorithms and their parts. This architecture and developed platform can provide the research community with a common codebase for storing, designing, and evaluating decision tree algorithms (traditional, multivariate and hybrid) and their partial improvements. It is intended for data mining practitioners, algorithm and software developers, and as well for students, as a technology enhanced learning tool. [ABSTRACT FROM AUTHOR]

This paper investigates and compares the performance of local descriptors for race classification from face images. Two powerful types of local descriptors have been considered in this study: Local Binary Patterns (LBP) and Weber Local Descriptors (WLD). First, we investigate the performance of LBP and WLD separately and experiment with different parameter values to optimize race classification. Second, we apply the Kruskal-Wallis feature selection algorithm to select a subset of more "discriminative" bins from the LBP and WLD histograms. Finally, we fuse LBP and WLD, both at the feature and score levels, to further improve race classification accuracy. For classification, we have considered the minimum distance classifier and experimented with three distance measures: City-block, Euclidean, and Chi-square. We have performed extensive experiments and comparisons using five race groups from the FERET database. Our experimental results indicate that (i) using the Kruskal-Wallis feature selection, (ii) fusing LBP with WLD at the feature level, and (iii) using the City-block distance for classification, outperforms LBP and WLD alone as well as methods based on holistic features such as Principal Component Analysis (PCA) and LBP or WLD (i.e., applied globally). [ABSTRACT FROM AUTHOR]

The prediction of gene function from genome sequences is one of the main issues in Bioinformatics. Most computational approaches are based on the similarity between sequences to infer gene function. However, the availability of several fully sequenced genomes has enabled alternative approaches, such as phylogenetic profiles. Phylogenetic profiles are vectors which indicate the presence or absence of a gene in other genomes. The main concept of phylogenetic profiles is that proteins participating in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion. In this paper, a multi-level clustering algorithm of phylogenetic profiles is presented, which aims to detect inter- and intra-genome gene clusters. [ABSTRACT FROM AUTHOR]

Multi-class classification is one of the most important tasks in machine learning. In this paper we consider two online multi-class classification problems: classification by a linear model and by a kernelized model. The quality of predictions is measured by the Brier loss function. We obtain two computationally efficient algorithms for these problems by applying the Aggregating Algorithms to certain pools of experts and prove theoretical guarantees on the losses of these algorithms. We kernelize one of the algorithms and prove theoretical guarantees on its loss. We perform experiments and compare our algorithms with logistic regression. [ABSTRACT FROM AUTHOR]

Conjugate gradient methods constitute excellent neural network training methods which are characterized by their simplicity and their very low memory requirements. In this paper, we propose a new spectral conjugate gradient method which guarantees sufficient descent using any line search, avoiding thereby the usually inefficient restarts. Moreover, we establish the global convergence of our proposed method under some assumptions. Experimental results provide evidence that our proposed method is preferable and in general superior to the classical conjugate gradient methods in terms of efficiency and robustness. [ABSTRACT FROM AUTHOR]

Naïve Bayes is a simple and efficient classification algorithm which performs well on text classification, which is also known as text categorization. Many researches have been done to improve the performance of the naïve Bayes classifier by weighting the correlated terms, in order to relax the strong assumption of independence between terms. In this paper, we first introduce a new χ2 statistical data, denoted by Rw,c, which can measure positive term-class dependency accurately, and then propose a new weighted naïve Bayes classifier using Rw,c at the training phase. Experimental results with real data sets show that our weighted naïve Bayes classifier has much better performance than the basic naïve Bayes classifier in most cases. [ABSTRACT FROM AUTHOR]

Many approaches are proposed to improve naive Bayes by weakening its conditional independence assumption. In this paper, we work on the approach of instance weighting and propose an improved naive Bayes algorithm by discriminative instance weighting. We called it Discriminatively Weighted Naive Bayes. In each iteration of it, different training instances are discriminatively assigned different weights according to the estimated conditional probability loss. The experimental results based on a large number of UCI data sets validate its effectiveness in terms of the classification accuracy and AUC. Besides, the experimental results on the running time show that our Discriminatively Weighted Naive Bayes performs almost as efficiently as the state-of-the-art Discriminative Frequency Estimate learning method, and significantly more efficient than Boosted Naive Bayes. At last, we apply the idea of discriminatively weighted learning in our algorithm to some state-of-the-art naive Bayes text classifiers, such as multinomial naive Bayes, complement naive Bayes and the one-versus-all-but-one model, and have achieved remarkable improvements. [ABSTRACT FROM AUTHOR]

Resources like ontologies are used in a number of applications, including natural language processing, information retrieval(especially from the Internet). Different methods have been proposed to build such resources. This paper proposes a new method to extract information from the Web to build a taxonomy of terms and Web resources for a given domain. Firstly, a (CHIR) method is used to identify candidat terms. Then a similarity (SIM) measure is introduced to select relevant concepts to build the ontology. Our new algorithm, called (CHIRSIM), is easy to implement and can be efficiently integrated into an information retrieval system to help improve the retrieval performance. Experimental results show that the proposed approach can effectively and efficiently construct a cancer domain ontology from unstructured text documents. [ABSTRACT FROM AUTHOR]