Reviewerly/exHarmony huggingface dataset:
Genetic Programming for Instance Transfer Learning in Symbolic Regression

Transfer learning has attracted more attention in the machine-learning community recently. It aims to improve the learning performance on the domain of interest with the help of the knowledge acquired from a similar domain(s). However, there is only a limited number of research on tackling transfer learning in genetic programming for symbolic regression. This article attempts to fill this gap by proposing a new instance weighting framework for transfer learning in genetic programming-based symbolic regression. In the new framework, differential evolution is employed to search for optimal weights for source-domain instances, which helps genetic programming to identify more useful source-domain instances and learn from them. Meanwhile, a density estimation method is used to provide good starting points to help the search for the optimal weights while discarding some irrelevant or less important source-domain instances before learning regression models. The experimental results show that compared with genetic programming and support vector regression that learn only from the target instances, and learning from a mixture of instances from the source and target domains without any transfer learning component, the proposed method can evolve regression models which not only achieve notably better cross-domain generalization performance in stability but also reduce the trend of overfitting effectively. Meanwhile, these models are generally much simpler than those generated by the other GP methods.
A hybridized parallel bats algorithm for combinatorial problem of traveling salesman

Metaheuristic algorithms have been applied widely for real-world problems in many fields, e.g., engineering, financial, healthcare. Bats algorithm (BA) is a recent metaheuristic algorithm with considering as a robust optimization method that can outperform existing algorithms. However, when dealing with complicated combinatorial problems such as traveling salesman problems (TSP), the BA can be fallen in a local optimum. This paper proposes a new hybridizing Parallel BA (HPBA) with a mutation in local-search to escape such its drawback scenario for TSP. A graph theory mutation method is used to embed for hybridizing BA with exploiting similarities among individuals. The proposed method is extensively evaluated in TSP with series instances of the benchmark from TSPLIB to test its performance. The compared experimental result with the previous method and the best-known solutions (B.K.S) in the literature shows that the proposed approach offers competitive results.
Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network

Abstract The precise detection and segmentation of pectoral muscle areas in mediolateral oblique (MLO) views is an essential step in the development of a computer‐aided diagnosis system to access breast malignant lesions or parenchyma. The goal of this article is to develop a robust and fully automatic algorithm for pectoral muscle segmentation from mammography images. This paper presents an image enhancement approach that improves the quality of mammogram scans and a convolutional neural network‐based fully convolutional network architecture enhanced with residual connections for automatic segmentation of the pectoral muscle from the MLO views of a digital mammogram. For this purpose, the model is tested and trained on three different mammogram datasets named MIAS, INBREAST, and DDSM. The ground truth labels of the pectoral muscle were identified under the supervision of experienced radiologists. For training and testing, 10‐fold cross‐validation was used. The proposed model was compared with baseline U‐Net‐based architecture. Finally, we used a postprocessing step to find the actual boundary of the pectoral muscle. Our presented architecture generated a mean Intersection over Union (IoU) of 97%, dice similarity coefficient (DSC) of 96% and 98% accuracy on testing data. The proposed architecture for pectoral muscle segmentation from the MLO views of mammogram images with high accuracy and dice score can be quickly merged with the breast tumor segmentation problem.
Transfer Correlation Between Textual Content to Images for Sentiment Analysis

In social media, images and texts are used to convey individuals' attitudes and feelings; thus, social media has become an indispensable part of people's lives.To understand social behavior and provide better recommendations, sentiment analysis on social media is helpful.One sentiment analysis task is polarity prediction.Although current research on visual or textual sentiment analysis has achieved quite good progress, multimodal and cross-modal analysis combining visual and textual correlation is still in the exploration stage.To capture a semantic connection between images and captions, this paper proposes a cross-modal approach that considers both images and captions in classifying image sentiment polarity.This method transfers the correlation between textual content to images.First, the image and its corresponding caption are sent into an inner-class mapping model, where they are transformed into vectors in Hilbert space to obtain their labels by calculating the inner-class maximum mean discrepancy (MMD).Then, a class-aware sentence representation (CASR) model assigns the distributed representation to the labels with a class-aware attention-based gated recurrent unit (GRU).Finally, an inner-class dependency LSTM (IDLSTM) classifies the sentiment polarity.Experiments carried out on the Getty Images dataset and Twitter 1269 dataset demonstrate the effectiveness of our approach.Moreover, extensive experimental results show that our model outperforms baseline solutions.
Application of hyperbolic partial differential equations in global optimal scheduling of UAV

The global optimal scheduling of UAV (unmanned aerial vehicle) navigation channel is studied. Firstly, a multi-channel optimal scheduling mathematical model based on the HPDEs (hyperbolic partial differential equations) wave combination optimization is proposed. Secondly, the pseudo arc length method is applied to solve the HPDEs, and the form of the spatial transformation of the global pseudo arc length method and its mesh adaptive properties are given. Finally, the results show that the mathematical model is used to achieve better convergence of the UAV's channel scheduling, and the scheduling delay error is lower, which has better reliability and robustness.
Echo state network‐based feature extraction for efficient color image segmentation

Summary Image segmentation plays a crucial role in many image processing and understanding applications. Despite the huge number of proposed image segmentation techniques, accurate segmentation remains a significant challenge in image analysis. This article investigates the viability of using echo state network (ESN), a biologically inspired recurrent neural network, as features extractor for efficient color image segmentation. First, an ensemble of initial pixel features is extracted from the original images and injected into the ESN reservoir. Second, the internal activations of the reservoir neurons are used as new pixel features. Third, the new features are classified using a feed forward neural network as a readout layer for the ESN. The quality of the pixel features produced by the ESN is evaluated through extensive series of experiments conducted on real world image datasets. The optimal operating range of different ESN setup parameters for producing competitive quality features is identified. The performance of the proposed ESN‐based framework is also evaluated on a domain‐specific application, namely, blood vessel segmentation in retinal images where experiments are conducted on the widely used digital retinal images for vessel extraction (DRIVE) dataset. The obtained results demonstrate that the proposed method outperforms state‐of‐the‐art general segmentation techniques in terms of performance with an F ‐score of 0.92 ± 0.003 on the segmentation evaluation dataset. In addition, the proposed method achieves a comparable segmentation accuracy (0.9470) comparing with reported techniques of segmentation of blood vessels in images of retina and outperform them in terms of processing time. The average time required by our technique to segment one retinal image from DRIVE dataset is 8 seconds. Furthermore, empirically derived guidelines are proposed for adequately setting the ESN parameters for effective color image segmentation.
A parallel constrained efficient global optimization algorithm for expensive constrained optimization problems

The Constrained Expected Improvement (CEI) criterion used in the so-called Constrained Efficient Global Optimization (C-EGO) algorithm is one of the most famous infill criteria for expensive constrained optimization problems. However, the standard CEI criterion selects only one point to evaluate in each cycle, which is time consuming when parallel computing architecture is available. This work proposes a new Parallel Constrained EGO (PC-EGO) algorithm to extend the C-EGO algorithm to parallel computing. The proposed PC-EGO algorithm is tested on sixteen analytical problems as well as one real-world engineering problem. The experiment results show that the proposed PC-EGO algorithm converges significantly faster and finds better solutions on the test problems compared to the standard C-EGO algorithm. Moreover, when compared to another state-of-the-art parallel constrained EGO algorithm, the proposed PC-EGO algorithm shows more efficient and robust performance.
An Improved Task Allocation Strategy in Cloud using Modified K-means Clustering Technique

In the present era, cloud computing has earned much popularity, mainly because of its utilities and relevance with the current technological trends. It is an arrangement which is highly customizable and encapsulated for providing better computational services to its clients worldwide. In cloud computing, scheduling plays a pivotal role in the optimal utilization of resources. Prevalent priority based job scheduling strategies are silent in deciding scheduling scheme for tasks with the same priority and strive hard in appropriately allocating jobs to virtual machines. In the recent years, despite of much research in this field, these scheduling algorithms are unable to provide optimal solution and are lacking in one way or the other in their performance and efficiency. Work pertaining to the use of four criteria/credits for deciding priority, with modified K-means clustering technique is scant. Therefore, to eliminate the drawbacks of the prevalent or existing system and to enhance the performance and efficiency of cloud computing, a new credits based scheduling algorithm has been rendered. The proposed system considers four real time parameters/factors namely Task-Length, Task-Priority, Deadline and Cost, as credits and uses Modified K-means Clustering technique for categorizing the cloudlets and virtual machines (VMs). Results indicate that the suggested scheduling algorithm has excelled existing priority-based scheduling strategy and it has been empirically proven with experimental/simulated results in this paper. CloudSim 3.0.3, a Cloud Simulation Tool has been used to implement and test the proposed algorithm.
Improved Table Retrieval Using Multiple Context Embeddings for Attributes

Table retrieval is the task of extracting the most relevant tables to answer a user's query. Table retrieval is an important task because many domains have tables that contain useful information in a structured form. Given a user's query, the goal is to obtain a relevance ranking for query-table pairs, such that higher ranked tables should be more relevant to the query. In this paper, we present a context-aware table retrieval method that is based on a novel embedding for attribute tokens. We find that differentiated types of contexts are useful in building word embeddings. We also find that including a specialized representation of numerical cell values in our model improves table retrieval performance. We use the trained model to predict different contexts of every table. We show that the predicted contexts are useful in ranking tables against a query using a multi-field ranking approach. We evaluate our approach using public WikiTables data, and we demonstrate improvements in terms of NDCG over unsupervised baseline methods in the table retrieval task.
Multilingual Bottleneck Features for Query by Example Spoken Term Detection

State of the art solutions to query by example spoken term detection (QbE-STD) rely on bottleneck feature representation of the query and audio document. Here, we present a study on QbE-STD performance using several monolingual as well as multilingual bottleneck features extracted from feed forward networks. In contrast to previous works, we use multitask learning to train the multilingual networks which perform significantly better than the concatenated monolingual features. Additionally, we propose to employ residual networks (ResNet) to estimate the bottleneck features and show significant improvements over the corresponding feed forward network based features. The neural networks are trained on GlobalPhone corpus and QbE-STD experiments are performed on a very challenging QUESST 2014 database
Siamese Deformable Cross-Correlation Network for Real-Time Visual Tracking

In recent years, SiamFC-based trackers have received much attention because of their great potentials in balancing tracking accuracy and speed. However, the robustness of most such trackers is greatly affected by the large deformations of targets. We argue that in the cross-correlation operation which is widely used by modern SiamFC-based trackers, the static correlation between the template kernel and the feature maps of test sample is difficult to adapt to the large deformation of the target object. In this paper, we propose a Siamese deformable cross-correlation network (SiamDCN), which introduces the deformable cross-correlation operation into SiamFC in an online self-adaptive way, for robust visual tracking. Compared to the previous SiamFC-based trackers, our SiamDCN is more robust to the large deformations of targets through dynamically and adaptively adjusting the location of correlation calculation for each element of the template kernel in the cross-correlation operation. Moreover, we build a twofold Siamese network, named SiamDCN+, which consists of a SiamDCN branch and a SiamFC branch, for accurate and real-time visual tracking after observing that the features learned in SiamFC are static and discriminative, whereas those in SiamDCN are dynamic and robust, and they complement each other. Extensive experiments on three public benchmarks, OTB2015, VOT2016, and VOT2017, show that the proposed SiamDCN achieves superior localization accuracy than its baseline tracker SiamFC and the proposed SiamDCN+ achieves competitive performance compared to state-of-the-art real-time trackers, while running beyond 40 FPS.
Fake Review Detection Based on PU Learning and Behavior Density

Today, app stores offer ranking lists to help users to find quality apps that meet their needs. In order to prevent people from spreading fake reviews which can be used to defame certain apps or manipulate the ranking lists of the app store, we propose a method based on Positive and Unlabeled (PU) learning and behavior density to detect fake reviews. To identify the trusted negative samples, the classifier is trained by the Biased-SVM algorithm. Then, the preliminary screening results of the classifier are combined with user behavior density to identify fake reviews. The traditional fully supervised detection method relies on manually labeled data, the quality of which directly affects the trained classifier. Our proposed method can overcome such a deficiency, and achieve effective learning when there are only a small number of positive samples and a large number of unlabeled samples. Through experiments and case analysis, we demonstrate that our method has high detection accuracy.
Triple loss for hard face detection

Although face detection has been well addressed in the last decades, despite the achievements in recent years, effective detection of small, blurred and partially occluded faces in the wild remains a challenging task. Meanwhile, the trade-off between computational cost and accuracy is also an open research problem in this context. To tackle these challenges, in this paper, a novel context enhanced approach is proposed with structural optimization and loss function optimization. For loss function optimization, we introduce a hierarchical loss, referring to ``triple loss'' in this paper, to optimize the feature pyramid network (FPN) (Lin et al., 2017) based face detector. Additional layers are only applied during the training process. As a result, the computational cost is the same as FPN during inference. For structural optimization, we propose a context sensitive structure to increase the capacity of the prediction network to improve the accuracy of the output. In details, a three-branch inception subnet (Szegedy et al., 2015) based feature fusion module is employed to refine the original FPN without increasing the computational cost significantly, further improving low-level semantic information, which is originally extracted from a single convolutional layer in the backward pathway of FPN. The proposed approach is evaluated on two publicly available face detection benchmarks, FDDB and WIDER FACE. By using a VGG-16 based detector, experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost of face detection.
Predict or draw blood: An integrated method to reduce lab tests

Serial laboratory testing is common, especially in Intensive Care Units (ICU). Such repeated testing is expensive and may even harm patients. However, identifying specific tests that can be omitted is challenging. The search space of different lab tests is large and the optimal reduction is hard to determine without modeling the time trajectory of decisions, which is a nontrivial optimization problem. In this paper, we propose a novel deep-learning method with a very concise architecture to jointly predict future lab test events to be omitted and the values of the omitted events based on observed testing values. Using our method, we were able to omit 15% of lab tests with <5% prediction accuracy loss. Although the application is specific to repeated lab tests, our proposed framework is highly generalizable and can be used to tackle a family of similar business decision making problems.
Quantitative Ultrasound Monitoring of Breast Tumour Response to Neoadjuvant Chemotherapy: Comparison of Results Among Clinical Scanners

<h2>Abstract</h2> Quantitative ultrasound (QUS) techniques have been demonstrated to detect cell death <i>in vitro</i> and <i>in vivo</i>. Recently, multi-feature classification models have been incorporated into QUS texture-feature analysis methods to increase further the sensitivity and specificity of detecting treatment response in locally advanced breast cancer patients. To effectively incorporate these analytic methods into clinical applications, QUS and texture-feature estimations should be independent of data acquisition systems. The study here investigated the consistencies of QUS and texture-feature estimation techniques relative to several factors. These included the ultrasound system properties, the effects of tissue heterogeneity and the effects of these factors on the monitoring of response to neoadjuvant chemotherapy. Specifically, tumour-response–detection performance based on QUS and texture parameters using two clinical ultrasound systems was compared. Observed variations in data between the systems were small and the results exhibited good agreement in tumour response predictions obtained from both ultrasound systems. The results obtained in this study suggest that tissue heterogeneity was a dominant feature in the parameters measured with the two different ultrasound systems; whereas differences in ultrasound system beam properties only exhibited a minor impact on texture features. The McNemar statistical test performed on tumour response prediction results from the two systems did not reveal significant differences. Overall, the results in this study demonstrate the potential to achieve reliable and consistent QUS and texture-based analyses across different ultrasound imaging platforms.
Privacy in IoT Blockchains: with Big Data comes Big Responsibility

This paper reviews current progress for privacy support in IoT blockchains. It starts by reviewing motivations for IoT architectures to incorporate blockchain capabilities and address privacy concerns. Currently available technological tools for enhancing and measuring privacy need enhancement for consumer use. Privacy concerns have moved beyond public relations angst and are emerging as regulatory imperatives. Regulations, privacy principles and impact statements move beyond traditional legal classifications of privacy violations to provide some guidance in the context of operating IoT blockchains. Legal and technical innovations have enabled new data ownership and control options. Operating IoT blockchains need adequate privacy patterns across the scope of privacy threats and the life cycles of the IoT blockchain.
Bayesian Automatic Model Compression

Model compression has drawn great attention in deep learning community. A core problem in model compression is to determine the layer-wise optimal compression policy, e.g., the layer-wise bit-width in network quantization. Conventional hand-crafted heuristics rely on human experts and are usually sub-optimal, while recent reinforcement learning based approaches can be inefficient during the exploration of the policy space. In this article, we propose Bayesian automatic model compression (BAMC), which leverages non-parametric Bayesian methods to learn the optimal quantization bit-width for each layer of the network. BAMC is trained in a one-shot manner, avoiding the back and forth (re)-training in reinforcement learning based approaches. Experimental results on various datasets validate that our proposed methods can find reasonable quantization policies efficiently with little accuracy drop for the quantized network.