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This letter tries to effectively reduce the dimension of hyperspectral images (HSIs) by jointly considering both the spectral redundancy and spatial continuity through a multilinear transformation with graph embedding in core tensor space. The whole process is constructed in the framework of Tucker decomposition (TD). Since the distance between intraclass samples should be relatively smaller than that of the interclass samples, the reduced tensor cores should maintain this property. To achieve this goal, a graph is embedded to the core tensor space during TD. Moreover, considering the unstability of solution of the previous works, we constrain the projected matrices by orthogonality so that the results can be more stable and the extracted features can be more discriminative. We further analyze the effect of different constrains to TD methods for HSI dimensionality reduction. Finally, the experimental results show the superiority of this method to many other tensor methods.

In this article, we study the concomitants of m-generalized order statistics (m-GOS) from iterated Farlie–Gumbel–Morgenstern bivariate distributions, denoted by IFGM ( λ , η ) , as an extension of ...

A complex network is a network with non-trivial topological structures. It contains not just topological information but also attribute information available in the rich content of nodes. Concerning the task of cluster analysis in a complex network, model-based algorithms are preferred over distance-based ones, as they avoid designing specific distance measures. However, their models are only applicable to complex networks where the attribute information is composed of attributes in binary form. To overcome this disadvantage, we introduce a three-layer node-attribute-value hierarchical structure to describe the attribute information in a flexible and interpretable manner. Then, a new Bayesian model is proposed to simulate the generative process of a complex network. In this model, the attribute information is generated by following the hierarchical structure while the links between pairwise nodes are generated by a stochastic blockmodel. To solve the corresponding inference problem, we develop a variational Bayesian algorithm called TARA, which allows us to identify functionally meaningful clusters through an iterative procedure. Our extensive experiment results show that TARA can be an effective algorithm for cluster analysis in a complex network. Moreover, the parallelized version of TARA makes it possible to perform efficiently at its tasks when applied to large complex networks.

Generating text to have a consistent style with only a few observed highly-stylized text samples is a difficult task for image processing. The text style involving the typography, i.e., font, stroke, color, decoration, effects, etc., should be considered for transfer. In this paper, we propose a novel approach to stylize target text by decoding weighted deep features from only a few referenced samples. The deep features, including content and style features of each referenced text, are extracted from a Convolutional Neural Network (CNN) that is optimized for character recognition. Then, we calculate the similarity scores of the target text and the referenced samples by measuring the distance along the corresponding channels from the content features of the CNN when considering only the content, and assign them as the weights for aggregating the deep features. To enforce the stylized text to be realistic, a discriminative network with adversarial loss is employed. We demonstrate the effectiveness of our network by conducting experiments on three different datasets which have various styles, fonts, languages, etc. Additionally, the coefficients for character style transfer, including the character content, the effect of similarity matrix, the number of referenced characters, the similarity between characters, and performance evaluation by a new protocol are analyzed for better understanding our proposed framework.

In this paper, we present a novel double graph regularized double dictionary learning (DGRDDL) method for image classification. The proposed method jointly constructs a number of class-specific sub-dictionaries to capture the most discriminative features (class-specific information) of each class, and a class-shared dictionary to model the common patterns (class-shared information) shared by the images from different classes. A novel double graph regularization is proposed to correctly represent and differentiate these two types of information. Specifically, an intra-class similarity graph constraint is imposed on the representation coefficients over the class-specific dictionaries, and an inter-class similarity graph constraint is applied on the representation coefficients over the class-shared dictionary. In this way, the representations learned by the proposed DGRDDL method can correctly model the local similarity relationships of the class-specific and the class-shared information in images, respectively. Moreover, due to the differences between the intra-class and inter-class similarity graphs, the two types of information can be appropriately separated and captured by the learned dictionaries. We evaluate the performance of the proposed method on six public datasets and compared against those of seven benchmark methods. The experimental results demonstrate the effectiveness and superiority of the proposed method in image classification over the benchmark dictionary learning methods.

Android users are occasionally troubled by the slow UI responses and sudden application/OS crashes. These problems are mainly caused by software aging, a phenomenon of progressive degradation of performance and dependability typically observed in long-running software systems. A countermeasure to software aging is software rejuvenation, i.e., manual or scheduled restart at different levels, such as application, OS, and device. Various software aging and rejuvenation models have been proposed for different software systems. However, these traditional models cannot be applied in the context of mobile devices, as they seldom consider the patterns of usage behavior and user experience specific to mobile phones. We address this problem based on the observations that the usage time of mobile phones is typically fragmented in daily life, with frequent and periodical switches between active and sleep modes, and that the user experience on fluent operation in the active mode is a key concern for mobile users. These insights are exploited to model the usage behavior and aging process by individual Stochastic Petri-Nets, and then to compose them into a Continuous Time Markov Chain (CTMC). Furthermore, we propose proactive rejuvenation strategies based on such CTMCs to achieve the best user experience and the least user interference, such as restarting the device when it is in sleep mode and before it enters an aged state. To consider user experience - a key concern of mobile users which is still less prominent in traditional dependability measurements – we propose new related metrics: for fluency (i.e., the probability that a phone offers a fast UI response to the users), and for the degree of overall user experience. We demonstrate the effectiveness and advantages of the proposed models and metrics via simulations as well as an empirical study.

Linear codes with a few weights have important applications in secret sharing, authentication codes, data storage system, association schemes, and strongly regular graphs. Hence, the construction of linear codes with a few weights is an important research topic in coding theory. In this paper, we construct two new classes of linear codes with two and three weights, and determine their complete weight enumerators. Our work generalizes the results of Wang et al. (Discret Math 340(3):467–480, 2017).

Image segmentation is among the most important techniques in image processing, and many methods have been developed to perform this task. This paper presents a new multi-objective metaheuristic based on a multi-verse optimization algorithm to segment grayscale images via multi-level thresholding. The proposed approach involves finding an approximate Pareto-optimal set by maximizing the Kapur and Otsu objective functions. Both Kapur’s and Otsu’s methods are highly used for image segmentation performed by means of bi-level and multi-level thresholding. However, each of them has certain characteristics and limitations. Several metaheuristic approaches have been proposed in the literature to separately optimize these objective functions in terms of accuracy, whereas only a few multi-objective approaches have explored the benefits of the joint use of Kapur and Otsu’s methods. However, the computational cost of Kapur and Otsu is high and their accuracy needs to be improved. The proposed method, called Multi-objective Multi-verse Optimization, avoids these limitations. It was tested using 11 natural grayscale images and its performance was compared against three of well-known multi-objective algorithms. The results were analyzed based on two sets of measures, one to assess the performance of the proposed method as a multi-objective algorithm, and the other to evaluate the accuracy of the segmented images. The results showed that the proposed method provides a better approximation to the optimal Pareto Front than the other algorithms in terms of hypervolume and spacing. Moreover, the quality of its segmented image is better than those of the other methods in terms of uniformity measures.

With the advent of social network services, Arabs’ opinions on the web have attracted many researchers in recent years toward detecting and classifying sentiments in Arabic tweets and reviews. However, the impact of word embeddings vectors (WEVs) initialization and dataset balance on Arabic sentiment classification using deep learning has not been thoroughly studied. In this article, a multi-channel embedding convolutional neural network (MCE-CNN) is proposed to improve Arabic sentiment classification by learning sentiment features from different text domains, word, and character n-grams levels. MCE-CNN encodes a combination of different pre-trained word embeddings into the embedding block at each embedding channel and trains these channels in parallel. Besides, a separate feature extraction module implemented in a CNN block is used to extract more relevant sentiment features. These channels and blocks help to start training on high-quality WEVs and fine-tuning them. The performance of MCE-CNN is evaluated on several standard balanced and imbalanced datasets to reflect real-world use cases. Experimental results show that MCE-CNN provides a high classification accuracy and benefits from the second embedding channel on both standard Arabic and dialectal Arabic text, which outperforms state-of-the-art methods.

This paper presents an alternative method for cloud task scheduling problem which aims to minimize makespan that required to schedule a number of tasks on different Virtual Machines (VMs). The proposed method is based on the improvement of the Moth Search Algorithm (MSA) using the Differential Evolution (DE). The MSA simulates the behavior of moths to fly towards the source of light in nature through using two concepts, the phototaxis and Levy flights that represent the exploration and exploitation ability respectively. However, the exploitation ability is still needed to be improved, therefore, the DE can be used as local search method. In order to evaluate the performance of the proposed MSDE algorithm, a set of three experimental series are performed. The first experiment aims to compare the traditional MSA and the proposed algorithm to solve a set of twenty global optimization problems. Meanwhile, in second and third experimental series the performance of the proposed algorithm to solve the cloud task scheduling problem is compared against other heuristic and meta-heuristic algorithms for synthetical and real trace data, respectively. The results of the two experimental series show that the proposed algorithm outperformed other algorithms according to the performance measures.

Automatic image annotation becomes a hot research area because of its efficiency on shrinking the semantic gap between images and their semantic meanings. We present a model referred as weight-KNN which firstly introduces the CNN feature to address the problem that traditional models only work well with well-designed manual feature representations. Additionally, in order to employ the simplicity and generality of the KNN-based model for annotation, the proposed model incorporates a multi-label linear discriminant approach to compute the weighting which improves the accuracy in the subsequent procedures of distance calculation. Moreover, we take the advantage of the KNN-based model to acquire the test image’s k-nearest neighbors in each label category and get the prediction of the image according to the contribution of its neighbors. At last, the experiments are performed on three typical image data sets, corel 5k, esp game and laprtc12, which verify the effectiveness of the proposed model.

In recent years, convolutional neural network (CNN) has attracted considerable attention since its impressive performance in various applications, such as Arabic sentence classification. However, building a powerful CNN for Arabic sentiment classification can be highly complicated and time consuming. In this paper, we address this problem by combining differential evolution (DE) algorithm and CNN, where DE algorithm is used to automatically search the optimal configuration including CNN architecture and network parameters. In order to achieve the goal, five CNN parameters are searched by the DE algorithm which include convolution filter sizes that control the CNN architecture, number of filters per convolution filter size (NFCS), number of neurons in fully connected (FC) layer, initialization mode, and dropout rate. In addition, the effect of the mutation and crossover operators in DE algorithm were investigated. The performance of the proposed framework DE-CNN is evaluated on five Arabic sentiment datasets. Experiments’ results show that DE-CNN has higher accuracy and is less time consuming than the state-of-the-art algorithms.

To prevent the same known vulnerabilities from affecting different firmware, searching known vulnerabilities in binary firmware across different architectures is crucial. Because the accuracy of existing cross-architecture vulnerability search methods is not high, we propose a staged approach based on support vector machine (SVM) and attributed control flow graph (ACFG) at the function level to improve the accuracy using prior knowledge. Furthermore, for efficiency, we utilize the k-nearest neighbor (kNN) algorithm to prune and SVM to refine in the function prefilter stage. Although the accuracy of the proposed method using kNN-SVM approach is slightly lower than the accuracy of the method using only SVM, its efficiency is significantly enhanced. We have implemented our approach CVSkSA to search several vulnerabilities in real-world firmware images. The experimental results show that the accuracy of the proposed method using kNN-SVM approach is close to the accuracy of the method using only SVM in most cases, while the former is approximately four times faster than the latter.

We study the limit distributions of upper and lower record values of a stationary Gaussian sequence (SGS) under an equi-correlated set up, when the random sample size is assumed to converge...

In this paper, a multiscale contour steered region integral (MCSRI) method is proposed to classify highly similar shapes with flexible interior connection architectures. A component distance map (CDM) is developed to robustly characterize the flexible interior connection structure, shape of the exterior contour, and their inter-relationship in a shape image. A novel multiscale region transform (MReT) is proposed to perform region integral over different contour-steered strips at all possible scales to effectively integrate patch features, and thus enables a better description of the shape image in a coarse-to-fine manner. It is applied to solve a challenging problem of classifying cultivars from leaf images, which is a new attempt in both biology and computer vision research communities. A soybean cultivar leaf vein database (SoyCultivarVein), which is the first cultivar leaf vein database, is created and presented for performance evaluation. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art methods in similar shape classification and the possibility of cultivar recognition via leaf pattern analysis, which may lead to a new research interest towards fine-level shape analysis on cultivar classification.

Out-of-vocabulary (OOV) words are the most challenging problem in automatic speech recognition (ASR), especially for morphologically rich languages. Most end-to-end speech recognition systems are performed at word and character levels of a language. Amharic is a poorly resourced but morphologically rich language. This paper proposes hybrid connectionist temporal classification with attention end-to-end architecture and a syllabification algorithm for Amharic automatic speech recognition system (AASR) using its phoneme-based subword units. This algorithm helps to insert the epithetic vowel እ[ɨ], which is not included in our Grapheme-to-Phoneme (G2P) conversion algorithm developed using consonant–vowel (CV) representations of Amharic graphemes. The proposed end-to-end model was trained in various Amharic subwords, namely characters, phonemes, character-based subwords, and phoneme-based subwords generated by the byte-pair-encoding (BPE) segmentation algorithm. Experimental results showed that context-dependent phoneme-based subwords tend to result in more accurate speech recognition systems than the character-based, phoneme-based, and character-based subword counterparts. Further improvement was also obtained in proposed phoneme-based subwords with the syllabification algorithm and SpecAugment data augmentation technique. The word error rate (WER) reduction was 18.38% compared to character-based acoustic modeling with the word-based recurrent neural network language modeling (RNNLM) baseline. These phoneme-based subword models are also useful to improve machine and speech translation tasks.

Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal network (AS-RPN) using effective selected anchors instead of dense anchors to extract text proposals. The center, scales, aspect ratios and orientations of anchors are learnable instead of fixing, which leads to high recall and greatly reduced numbers of anchors. By replacing the anchor-based RPN in Faster RCNN, the AS-RPN-based Faster RCNN can achieve comparable performance with previous state-of-the-art text detecting approaches on standard benchmarks, including COCO-Text, ICDAR2013, ICDAR2015 and MSRA-TD500 when using single-scale and single model (ResNet50) testing only.

Face anti-spoofing is becoming increasingly indispensable for face recognition systems, which are vulnerable to various spoofing attacks performed using fake photos and videos. In this paper, a novel “LDN-TOP representation followed by ProCRC classification” pipeline for face anti-spoofing is proposed. We use local directional number pattern (LDN) with the derivative-Gaussian mask to capture detailed appearance information resisting illumination variations and noises, which can influence the texture pattern distribution. To further capture motion information, we extend LDN to a spatial-temporal variant named local directional number pattern from three orthogonal planes (LDN- TOP). The multi-scale LDN- TOP capturing complete information is extracted from color images to generate the feature vector with powerful representation capacity. Finally, the feature vector is fed into the probabilistic collaborative representation based classifier (ProCRC) for face anti-spoofing. Our method is evaluated on three challenging public datasets, namely CASIA FASD, Replay-Attack database, and UVAD database using sequence-based evaluation protocol. The experimental results show that our method can achieve promising performance with 0.37% EER on CASIA and 5.73% HTER on UVAD. The performance on Replay-Attack database is also competitive.

As a cross modality retrieval task, RGB-infrared person re-identification(Re-ID) is an important and challenging task, because of its important role in video surveillance applications and large cross-modality variations between visible and infrared images. Most previous works addressed the problem of cross-modality gap with feature alignment by original feature representation learning straightly. In this paper, different from existing works, we propose a novel network(CE2L) to tackle the cross-modality gap with feature alignment. CE2L mainly focuses on adding discriminative information and learning robust features by converting modality between visible and infrared images. Its merits are highlighted in two aspects: 1)Using CycleGAN to convert infrared images into color images can not only increase the recognition characteristics of images, but also allow the network to better learn the two modal image features; 2)Our novel method can serve as data augmentation. Specifically, it can increase data diversity and total data against over-fitting by converting labeled training images to another modal images. Extensive experimental results on two datasets demonstrate superior performance compared to the baseline and the state-of-the-art methods.

Few-shot learning aims to train a classifier which can recognize a new class from a few examples like a human. Recently, some works have leveraged auxiliary information in few-shot learning, such as textual data, unlabeled visual data. But these data are positive data, they are close related to the training data. Different from such experimental settings, people can also get knowledge from negative data to better recognize a new class. Inspired by this, we exposure a few unlabeled outliers in each few-shot learning tasks to assist the learning of the classifier. To the best of our knowledge, we are the first ones who propose to utilize outliers to improve few-shot learning. We propose a novel method based on meta-learning paradigms to utilize unlabeled outliers. We not only utilize unlabeled outliers to optimize the meta-embedding network but also adaptively leverage them to enhance the class prototypes. Experiments show that our outlier exposure network can improve few-shot learning performance with a few unlabeled outliers exposure.

Facial makeup transfer aims to render a non-makeup face image in an arbitrary given makeup one while preserving face identity. The most advanced method separates makeup style information from face images to realize makeup transfer. However, makeup style includes several semantic clear local styles which are still entangled together. In this paper, we propose a novel unified adversarial disentangling network to further decompose face images into four independent components, i.e., personal identity, lips makeup style, eyes makeup style and face makeup style. Owing to the disentangled makeup representation, our method can not only flexible control the degree of local makeup styles, but also can transfer local makeup styles from different images into the final result, which any other approaches fail to handle. For makeup removal, different from other methods which regard makeup removal as the reverse process of makeup transfer, we integrate the makeup transfer with the makeup removal into one uniform framework and obtain multiple makeup removal results. Extensive experiments have demonstrated that our approach can produce visually pleasant and accurate makeup transfer results compared to the state-of-the-art methods.

Wheat is one of the main food crops in the world today. In order to increase wheat yield, breeding experts are committed to discovering the connection between its genotype and phenotype. Existing phenotype extraction methods mostly rely on manual methods, and the amount of collected data is limited and inefficient. 3D Computed Tomography (CT) imaging has the advantages of fine imaging, high dynamic range, and nondestructive detection of internal structures, which can help to extract the high-throughput phenotype of wheat non-destructively and finely. However, the 3D images scanned by CT have the characteristics of large amount of data and highly sparse content, which brings great challenges to extract phenotype from them. We propose an adaptive supervoxel algorithm for the high sparsity of 3D CT images, which is used for the pre-segmentation. Then, an octree convolutional network is used to optimize the sparse structure storage, and further improve the efficiency of fine segmentation. At last, phenotype is extracted automatically from the finely segmented wheat grain.

Vehicular communication networking (VCN) has been widely recognized as a promising solution to support safety-related applications in urban transportation systems. In VCN, efficient message dissemination can let vehicles be better aware of the potential risks and traffic anomalies, which is critical to road safety and traffic efficiency. Substantial studies have focused on the design of inter-vehicle message dissemination protocols. Nonetheless, most existing designs only consider the rapid end-to-end transmission, few of which take into account the broadcast coverage. In this paper, we propose a new message dissemination scheme in the urban traffic scenario by considering both the time constraint and the spatial distribution of data dissemination. Specifically, based on the temporal and spatial correlation of vehicle trajectory, relay vehicles are selected to construct a temporary warning network (TWN) for a rapid safety message dissemination in the regions of interest (ROI). Finally, we conduct extensive numerical experiments to validate the effectiveness of our method in various traffic scenarios.

This paper proposes a novel approach for pose transfer, which aims at transferring the pose of a given person to a target pose. Unlike previous works directly simulating the target pose, we emphasize the pose geometric constraint in our approach to tackle this task. To capture the geometric constraints among the condition pose and target pose, we generate a sequence of different intermediate pose skeleton. Moreover, we introduced the 3D convolution into the generator of our Generative Adversarial Network (GAN) in order to learn spatiotemporal features from the generated pose skeleton sequence. The final target pose will be estimated and refined by a pose transfer block and a series of residual blocks based on the spatiotemporal features. Compared with the existing works, our generated person pose images achieved better performance in terms of Inception Score and Structure Similarity. Extensive experiments on dataset Market-1501 demonstrate the effectiveness of the pose skeleton sequence on pose transfer.

Vehicular ad hoc networks (VANETs) have been widely recognized as a promising solution to improve traffic safety and efficiency for the ability to provide situation awareness even though the potential dangers and traffic anomalies are out of the visual range. In VANETs, time-division multiple access (TDMA) based overlay protocols can prevent transmission collisions, and play an important role in providing an efficient communication channel. However, due to high vehicle mobility and time-varying traffic flow, the existing TDMA-based slot allocation approaches cannot fully utilize the channel resources, which may result in high transmission delay and packet collision. To overcome these shortcomings, we propose a traffic-aware TDMA-based MAC (TA-MAC) protocol which utilizes the capability of mobile edge computing (MEC) in this paper. Specifically, based on MEC and vehicle-to-road-side-units (V2R) communications, a traffic-aware mechanism is first proposed to estimate the traffic condition on the road segment. Then, we propose a new slot assignment method that aims at guaranteeing the high channel utilization and low delay of safety message under dynamic traffic conditions. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed protocol.

Text categorization or Textual document is a method that becomes more significant in tagging a textual document to their most relevant label. However, not all languages have parallel textual growth; without free and absences of a dataset, text categorization becomes interesting for Tigrigna language, i.e., low-resourced language. Our aim to identify the given document to its categories based on its linguistic features. To achieve our goal, we have constructed a new dataset from different Tigrigna news sources. The dataset has six main categories: Agriculture, Sports, Health, Education, Religion, and Politics. Each collected is article preprocessed from Latin characters, punctuations, and stop words. We deployed a collection of different classical machine learning classifiers to investigate its effectiveness in our datasets. Namely, 7 popular classifiers were used, Logistic Regression, Nearest Centroid, Decision Tree (DT), Support Vector Machines (SVM), K-nearest neighbors (KNN), Random Forest Classifier, and Multi-Layer Perceptron (MLP). Ensemble models also implemented to get the best accuracy by combining the best classifiers based on their majority-voting classifiers. Our experimental results showed reliable performance with a minimum F1-score of 89.1% achieved by Nearest centroid and top performance of 96 % achieved by SVM. The experimental results presented in terms of precision, Recall, and F1-scores.

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

This paper proposed a modified version of Spider Monkey Optimization (SMO) algorithm for solving global optimization problems. The traditional SMO consists of seven phases where each phase has its characteristics and tasks to be performed. However, the local leader phase (LLP), that is the second phase of the SMO has the most significant effect on the performance of the algorithm. In which if it does not has good exploration and exploitation capability then the SMO might stick at a local point. Therefore, the proposed modified version of SMO (that called SMONM) used the transformations of the Nelder–Mead (NM) method to improve the ability of LLP. The proposed SMONM algorithm contains the same number of phases of the traditional SMO except the LLP that has modified through using the reflection, expansion, and contraction transformations of the NM. These transformations of NM worked if there is no improvement in the fitness function value after the solution is updating using the original LLP. The performance of the proposed algorithm has compared with other four algorithms namely, original SMO, Artificial Bee Colony optimization, Biography Based Optimization and Particle Swarm Optimization. A set of experimental series is performed to evaluate the performance of the proposed algorithm using 23 standard benchmark functions, 15 composite functions, and three classical engineering problems. The preliminary results show that the modified version of SMO has excellent ability to avoid the limitations of the tradition SMO algorithm, as well as, it provides better results than the other comparative algorithms regarding performance measures.

The routing optimization problem of grain emergency vehicle scheduling with three objectives is studied in this paper. The objectives are: maximizing satisfaction of the needs at the emergency grain demand points, minimizing total cost of grain distribution and minimizing the distribution time. A hybrid algorithm is present to solve the proposed problem based on combining artificial immune and ant colony optimization (ACO) algorithms. This hybrid algorithm calculates the degree of crowding and conducts non-dominated sorting of the population in the ant colony optimization algorithm by applying a Pareto optimization model. A better solution set is quickly generated by making use of the fast global convergence and randomness of the improved immune algorithm together with the distributed search ability and positive feedback of the ACO algorithm. A better solution set obtained as the initial pheromone distribution, is solved further by using ACO until the approximate optimal solution set is obtained. A comparison of the proposed algorithm with several common optimization algorithms on the Solomon benchmark dataset demonstrates that this method obtains better performance in shorter time, and is an efficient way to solve the vehicle routing problem in emergency grain distribution scenarios.

Exterior contour and interior structure are both vital features for classifying objects. However, most of the existing methods consider exterior contour feature and internal structure feature separately, and thus fail to function when classifying patchy image structures that have similar contours and flexible structures. To address above limitations, this paper proposes a novel Multi-Orientation Region Transform (MORT), which can effectively characterize both contour and structure features simultaneously, for patchy image structure classification. MORT is performed over multiple orientation regions at multiple scales to effectively integrate patchy features, and thus enables a better description of the shape in a coarse-to-fine manner. Moreover, the proposed MORT can be extended to combine with the deep convolutional neural network techniques, for further enhancement of classification accuracy. Very encouraging experimental results on the challenging ultra-fine-grained cultivar recognition task, insect wing recognition task, and large variation butterfly recognition task are obtained, which demonstrate the effectiveness and superiority of the proposed MORT over the state-of-the-art methods in classifying patchy image structures. Our code and three patchy image structure datasets are available at: https://github.com/XiaohanYu-GU/MReT2019., Accepted by AAAI 2020

Nowadays, privacy protection has become an important issue in data mining. k-means algorithm is one of the most classical data mining algorithms, and it has been widely studied in the past decade. Negative database (NDB) is a new type of data representation which can protect privacy while supporting distance estimation, so it is promising to apply NDBs to privacy-preserving k-means clustering. Existing privacy-preserving k-means clustering algorithms based on NDBs could effectively protect data privacy, but their clustering performance has a non-negligible degradation. In this paper, we propose a new NDB generation algorithm (named QK-hidden algorithm), and based on this algorithm, we propose a privacy-preserving k-means algorithm. The proposed algorithm can control the accuracy of distance estimation in a fine-grained manner, and thus it can control the clustering results granularly. Experimental results demonstrate the proposed algorithm has better clustering performance than existing privacy-preserving k-means algorithms based on NDBs.

Biometrics have been widely studied in recent years, and they are increasingly employed in real-world applications. Meanwhile, a number of potential threats to the privacy of biometric data arise. Iris template protection demands that the privacy of iris data should be protected when performing iris recognition. According to the international standard ISO/IEC 24745, iris template protection should satisfy the irreversibility, revocability, and unlinkability. However, existing works about iris template protection demonstrate that it is difficult to satisfy the three privacy requirements simultaneously while supporting effective iris recognition. In this paper, we propose an iris template protection method based on local ranking. Specifically, the iris data are first XORed (Exclusive OR operation) with an application-specific string; next, we divide the results into blocks and then partition the blocks into groups. The blocks in each group are ranked according to their decimal values, and original blocks are transformed to their rank values for storage. We also extend the basic method to support the shifting strategy and masking strategy, which are two important strategies for iris recognition. We demonstrate that the proposed method satisfies the irreversibility, revocability, and unlinkability. Experimental results on typical iris datasets (i.e., CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, UBIRIS-V1-S1, and MMU-V1) show that the proposed method could maintain the recognition performance while protecting the privacy of iris data.

In cognitive radio ad hoc networks (CRAHNs), the secondary users' links are interrupted by the arrival of primary users, which leads to a significant increase of the number of transmissions per packet. Since network coding and opportunistic routing can reduce the number of transmissions over unreliable wireless links, they are more suitable for CRAHNs. Network coding-based opportunistic routing has been well studied in previous works for traditional ad hoc networks. However, existing approaches have limitations to handle CRAHNs. First, the routing decisions of these methods aim to find one optimal forwarding set for the whole path from the source to the destination. If the channel conditions change dynamically, the pre-selected forwarding set may become unavailable or non-optimal. Second, their coding schemes are not efficient enough to cope with the dynamic spectrum variation in CRAHNs. In this paper, we attempt to overcome these limitations and propose a network-coding-based geographic segmented opportunistic routing scheme for CRAHNs, by fully embracing its characteristics. In our scheme, the whole path from the source to the destination is cut down into several smaller opportunistic route segments, where the packets are transmitted through multiple segments based on a step-by-step forwarding procedure until all the packets are delivered to the destination. Since our scheme utilizes only local spectrum opportunities, topology information, and geometric conditions to compute the forwarding set for each short-term opportunistic route segment, it can better adapt to dynamic spectrum environments and changing network topologies in CRAHNs. Furthermore, we construct a coding graph to show that our coding problem is a reduction from the maximum clique problem, and we propose an efficient network coding strategy to solve it. Simulation results show that our scheme can achieve a considerable performance, compared with the latest opportunistic routing protocols designed for CRAHNs.

In this paper, we propose a novel low-rank double dictionary learning (LRD2L) method for robust image classification tasks, in which the training and testing samples are both corrupted. Unlike traditional dictionary learning methods, LRD2L simultaneously learns three components from corrupted training data: 1) a low-rank class-specific sub-dictionary for each class to capture the most discriminative class-specific features of each class, 2) a low-rank class-shared dictionary which models the common patterns shared in the data of different classes, and 3) a sparse error term to model the noise in data. Through low-rank class-shared dictionary and noise term, the proposed method can effectively separate the corruptions and noise in training samples from creating low-rank class-specific sub-dictionaries, which are employed for correctly reconstructing and classifying testing images. Comparative experiments are conducted on three public available databases. Experimental results are encouraging, demonstrating the effectiveness of the proposed method and its superiority in performance over the state-of-the-art dictionary learning methods.

A new method to solve global optimization and engineering problems called OBSCA.The proposed method improves the SCA by using opposite-based learning.We apply the OBSCA over mathematical benchmark functions.We test OBSCA in engineering optimization problems.Comparisons support the improvement on the performance of OBCSA. Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces.

Ultra-fine-grained visual categorization (ultra-FGVC) categorizes objects with more similar patterns between classes than those in fine-grained visual categorization (FGVC), e.g., where the spectrum of granularity significantly moves down from classifying species to classifying cultivars within the same species. It is considered as an open research problem mainly due to the following challenges. First, the inter-class differences among images are much smaller by level of orders (e.g., cultivars in the same species) than those in current FGVC tasks (e.g., species). Second, there is only a few samples per category, which is beyond the ability of most large training data favored convolutional neural network methods. To address these problems, we propose a novel random mask covariance network (MaskCOV), which integrates an auxiliary self-supervised learning module with a powerful in-image data augmentation scheme for the ultra-FGVC. Specifically, we first uniformly partition input images into patches and then augment data by randomly shuffling and masking these patches. On top of that, we introduce an auxiliary self-supervised learning module of predicting the spatial covariance context of these patches to increase discriminability of our network for classification. Very encouraging experimental results of the proposed method in comparison with the state-of-the-art benchmarks demonstrate its superiority and potential of MaskCOV concept, which pushes research boundary forward from the fine-grained to the ultra-fine-grained visual categorization.

Driving performance evaluations can contribute to fleet management and lead to safer and more economical driving conditions for manned or driverless fleet vehicles. One approach to driving performance evaluation involves quantitative mapping or categorical labeling of skill levels and categorizing of driving patterns from extraordinarily mild to the most aggressive. This paper presents a big data system for driving performance evaluations of drivers and trips using a probabilistic framework. The proposed framework combines a feature mixture model for scoring driving performance through defined objective comparison criteria and a latent style mixture model for classifying drivers by the main driving styles they exhibit. To demonstrate the effectiveness of the proposed models, we perform both quantitative and qualitative experiments. The results show that the former produces an interpretable and normal scorecard model, while the latter helps build an improved clustering model that represents enhanced driver behavior.

Person retrieval is an important vision task, aiming at matching the images of the same person under various camera views. The key challenge of person retrieval lies in the large intra-class variations among the person images. Therefore, how to learn discriminative feature representations becomes the core problem. In this paper, we propose a deep part-aware representation learning method for person retrieval. First, an improved triplet loss is introduced such that the global feature representations from the same identity are closely clustered. Meanwhile, a localization branch is proposed to automatically localize those discriminative person-wise parts or regions, only using identity labels in a weakly supervised manner. Via the learning simultaneously guided by the global branch and the localization branch, the proposed method can further improve the performance for person retrieval. Through an extensive set of ablation studies, we verify that the localization branch and the improved triplet loss each contributes to the performance boosts of the proposed method. Our model obtains superior (or comparable) performance compared to state-of-the-art methods for person retrieval on the four public person retrieval datasets. On the CUHK03-labeled dataset, for instance, the performance increases from 73.0% mAP and 77.9% rank-1 accuracy to 80.8% (+7.8%) mAP and 83.9% (+6.0%) rank-1 accuracy.

Classification algorithms have shown exceptional prediction results in the supervised learning area. These classification algorithms are not always efficient when it comes to real-life datasets due to class distributions. As a result, datasets for real-life applications are generally imbalanced. Several methods have been proposed to solve the problem of class imbalance. In this paper, we propose a hybrid method combining the preprocessing techniques and those of ensemble learning. The original training set is undersampled by evaluating the samples by stochastic measurement (SM) and then training these samples selected by Multilayer Perceptron to return a balanced training set. The MLPUS (Multilayer perceptron undersampling) balanced training set is aggregated using the bagging ensemble method. We applied our method to the real-life Niger_Rice dataset and forty-four other imbalanced datasets from the KEEL repository in this study. We also compared our method with six other existing methods in the literature, such as the MLP classifier on the original imbalance dataset, MLPUS, UnderBagging (combining random under-sampling and bagging), RUSBoost, SMOTEBagging (Synthetic Minority Oversampling Technique and bagging), SMOTEBoost. The results show that our method is competitive compared to other methods. The Niger_Rice real-life dataset results are 75.6, 0.73, 0.76, and 0.86, respectively, for accuracy, F-measure, G-mean, and ROC with our proposed method. In contrast, the MLP classifier on the original imbalance Niger_Rice dataset gives results 72.44, 0.82, 0.59, and 0.76 respectively for accuracy, F-measure, G-mean, and ROC.

Purpose This work aims to report the effect of different inlet air velocity settings, and the position of the inlet areas under fire-induced conditions in stairwells. Design/methodology/approach The results are evaluated both experimentally and numerically. In sequence, the study also describes the results of three tests under different ventilation conditions, which were performed by simulating a fire of 0.9 m diameter in a stairwell access door. Detailed transient measurements of air temperature in the walls, air velocity settings at the ventilation outlet, smoke temperature through the fans and pressure through the exhaust fans were recorded for the study. Findings These data could be used as benchmark for future numerical validation studies. Based on computational fluid dynamics, the tests were performed using fire dynamics simulator codes, to compare the results of tests and simulations. Originality/value The numerical study was performed in confined floors close to the test areas of a typical stairwell, with different vents and a source of fire. The results show that the lack of symmetry in the air vents settings has greater influence on the plume than the total area of the air inlet.

Software development is a knowledge intensive and a collaborative activity, which mainly depends on knowledge and experience of the software developers. Knowledge management in software industries aims to create an environment for continuous knowledge sharing and creation to remain competitive. This study investigates the current status of knowledge management in three software industries in Sri Lanka on the basis that they claimed to apply knowledge management practices within their organizations using both quantitative and qualitative methods. The knowledge synthesis theory is used in this study to synthesize a variety of knowledge, unseen problems, obstructions, and the corresponding underlying reasons in implementing the knowledge management practices. Findings of this study emphasized that while knowledge management practices were considered to be important, organizational learning and culture, attitudes and behaviors of employees, information technology infrastructure, and rewards systems were inadequate to address an effective management of knowledge in these three software industries. One of the findings showed that, software developers believe both software engineering knowledge and information communication technology are considered to be an enterprise’s strategic asset and a main source to create competitive advantage. Findings also presented a compelling evidence of the soundness for the need of knowledge management practices to manage software engineering knowledge.

Pedestrian navigation at night should differ from daytime navigation due to the psychological safety needs of pedestrians. For example, pedestrians may prefer better-illuminated walking environments, shorter travel distances, and greater numbers of pedestrian companions. Route selection at night is therefore a multi-objective optimization problem. However, multi-objective optimization problems are commonly solved by combining multiple objectives into a single weighted-sum objective function. This study extends the artificial bee colony (ABC) algorithm by modifying several strategies, including the representation of the solutions, the limited neighborhood search, and the Pareto front approximation method. The extended algorithm can be used to generate an optimal route set for pedestrians at night that considers travel distance, the illumination of the walking environment, and the number of pedestrian companions. We compare the proposed algorithm with the well-known Dijkstra shortest-path algorithm ...

Emergency evacuation is an important task while dealing with a sudden accident. Aiming at emergency evacuation in campus, this paper proposed a personalized route planning model for pedestrian–vehicle mixed evacuation. Considering the congestion of pedestrian–vehicle mixed evacuation and the actual factors of students, this model found the optimal user equilibrium close to the system equilibrium. By the help of the Wardrop Equilibrium model, the server calculated the optimized evacuation route planning and guided evacuees by mobile intelligent terminals. Finally this paper applied this model to the evacuation of Wuhan University of Technology. By simulating the process of emergency evacuation, the results showed the feasibility of the algorithm, which proved this model a scientific basis for guiding the real evacuation process.

The automatic congestion detection of campus traffic presents a significant challenge to the traffic congestion research community. Typically, campus road users can be classified into four types including pedestrian, bike, vehicle and motorbike, which enhances the complexity of traffic condition. Thus, existing descriptors of traffic congestion for highway traffic are not valid when describing the traffic congestion in campus. In this paper, we propose a novel descriptor, road occupancy rate, for measuring campus traffic congestion level, which is statistically proved to be the most effective descriptor among other descriptors (including speed of pedestrian, vehicle, motorbike and bike). Two existing models Markov model and back propagation neural network (BPNN) are introduced in this paper to detect the campus traffic congestion combined with the proposed descriptors. And three phases are defined based on three-phase traffic theory to describe the campus traffic congestion levels. Experimental results indicate that the proposed detecting methods are both capable of detecting campus traffic congestion, while the BPNN-based method achieves higher accuracy and more stable performance.

In this paper, we evaluate the impact of climate parameters on the yield of rice production at the Office du Niger (ON) in Mali using four classifiers. Three machine learning classification algorithms, namely k-Nearest Neighbor (KNN), Support Vector Classifier (SVC), and Logistic Regression (LR) and Multilayer Perceptron (MLP) classifier are utilized. Our experience reveals that the three classification algorithms of machine learning and Multilayer perceptron classifiers give poor results on the original dataset due to the problem of unbalanced data. However, oversampling techniques are applied to the dataset to improve these results. The technique we used is an oversampling technique step by step. Therefore, 100% SMOTE (Synthetic Minority Over-Sampling Technique) is applied to the minority class at each stage, i.e., the minority class is doubled at each step. We found out that the proposed technique gives better results for all these classification algorithms rather than applying SMOTE directly to balance the dataset and other balanced techniques such as class balance and resample. However, MLP gives better results compared to other machine learning classification algorithms using our technique.

This paper presents a novel shape descriptor to effectively and efficiently characterize the local image statistics. The proposed descriptor, termed contour covariance (CC), characterizes covariance features driven by a moving point on the shape contour at multiple scales. To calculate the covariance matrices, three basic features including texture, intensity and distance map, are extracted from the object image. Based on coefficients of the obtained covariance matrices, the proposed CC descriptor is compact yet informative, as well as invariant to rotation, translation and scale. The experimental results on two databases demonstrate the superiority and efficiency of the proposed method among the state-of-the-art methods for shape classification.

Existing studies have uncovered that there exist significant Raw Bit Error Rates (RBERs) variations among different layers of 3D flash memories due to manufacture process variation. These RBER variations would cause significantly diversed read latencies when reading data with traditional Low-Density Parity-Check (LDPC) codes designed for planar flash memories, which induces sub-optimal read performance of flash-based Solid-State Drives (SSDs). To investigate the latency diversity, this paper first performs a preliminary experiment and observes that LDPC read levels proportional to latencies increase in diverse speeds along with data retention. Then, by exploiting the observation results, a Multi-Granularity LDPC (MG-LDPC) read method is proposed to adapt level increase speed for each layer. Five LDPC engines with varied increase granularity are designed to adapt RBER speed requirements. Finally, two implementations for MG-LDPC are applied to assign LDPC engines for each flash layer in a fixed way or dynamically according to prior read levels. Experimental results show that the proposed two implementations can reduce SSD read response time by 21% and 47% on average, respectively.CCS CONCEPTS• Hardware→Temperature optimization; Network on chip; 3D integrated circuits.

The data driven speech enhancement approaches using regression-based deep neural network usually result in enormous number of model parameters, which increase the computational load and the difficulty of model training. In order to improve the model efficiency, we propose a densely connected network with time-frequency (T-F) dilated convolution for speech enhancement. The T-F dilated convolution block is designed to enlarge the receptive field and capture the contextual information in both temporal and frequency domains. Considering the computational efficiency, the 1-D convolution with the bottleneck structure is exploited in the T-F convolution block. Each T-F convolution block is then densely connected to ensure maximum information flow between layers and alleviate the vanishing gradient problem of the network. The experimental results reveal that the proposed scheme not only improves the computational efficiency significantly but also produces satisfactory enhancement performance comparing the competing methods.

This paper proposes a novel multi-scale descriptor for shape recognition. The contour of shape is represented by a sequence of sample points with uniform spacing. Straight lines connected between two moving contour points are used to cut the shape. The lengths of the contour segments between the two sampled contour points determine the levels of scales. Then the geometric features of the cut contour and the interior texture features around the straight lines are extracted at each scale. This method not only has the powerful discriminability to describe a shape from coarse to fine, but also is invariant to scale, rotation, translation and mirror transformations. Experiments conducted on five image datasets (COIL-20, Flavia, Swedish, Leaf100 and ETH-80) demonstrate that the proposed method significantly outperforms the state-of-the-art methods.

Data set of high quality is the cornerstone of the current data-driven machine learning models, and plays an important role in promoting the development of various application areas. At present, image analysis and processing techniques have intensively involved into the tasks of inheriting and protecting culture resources. However, currently there are few effective image data sets about the traditional Chinese Tibetan culture. In this work, we provided a small data set referred as CYTKv1(Chomo Yarlung Tibet version 1) which includes 1700 + Thangka images (an important and representative carrier of Chinese Tibetan culture), and the main objects in each image are manually labeled and bounding-boxed with semantic words. In addition, we shared a list of tasks of processing and analyzing the Thangkas to enlighten researchers about the challenges and potential applications on this data set. At last, we tested several famous deep learning models for the purpose of validating the annotation task on the new data set and presented the results of them, and finally selected the best one as the baseline for the annotation task.