Reviewerly/exHarmony huggingface dataset:
New brain tumor classification method based on an improved version of whale optimization algorithm

Brain tumor is an abnormal growth of cells in the brain that its diagnosis in the early stages can help us to prevent the dangers of the next stage. In this paper, a new meta-heuristic based methodology is presented for the early diagnosis of the brain tumor to prevent this objection. The proposed method includes three main phases including background removing, feature extraction, and classification based on multilayer perceptron neural network. Here, an improved model of the whale optimization algorithm based on the chaos theory and logistic mapping technique is employed to the optimal selection of the features and the classification stages. The performance analysis of the presented method is compared with some existing methods. Final results showed that based on analyzing CDR, FAR, and FRR as testifying indices, the proposed method has better results than the other similar methods.
Trust in Blockchain Cryptocurrency Ecosystem

The recent growth in blockchain-based cryptocurrency ecosystem has been attracting researchers, developers, investors, regulators, and speculators to develop new economic and business models for trade, investment, and taxation. Currently, the cryptocurrency ecosystem is immature with multifaceted trust issues at all levels from technology providers to users and governments. In this article, we present a detailed analysis of trust issues in the cryptocurrency ecosystem, including a detailed taxonomic discussion of the key trust aspects including price manipulation, price volatility, insider trading, parallel economy, shadow economy, reputation systems, transparency, centrality, token economy, governance, regulations, design, usability, privacy, and security. We also present a comparative analysis of the top 10 cryptocurrencies that are holding about 85% of the total market capital. Finally, we present a detailed summary of the key trust issues and their potential immediate, short-term, and long-term solutions. This article reveals that significant effort is required to develop a fully trustworthy cryptocurrency ecosystem.
Deep learning modeling using normal mammograms for predicting breast cancer risk

Purpose To investigate two deep learning‐based modeling schemes for predicting short‐term risk of developing breast cancer using prior normal screening digital mammograms in a case‐control setting. Methods We conducted a retrospective Institutional Review Board‐approved study on a case‐control cohort of 226 patients (including 113 women diagnosed with breast cancer and 113 controls) who underwent general population breast cancer screening. For each patient, a prior normal (i.e., with negative or benign findings) digital mammogram examination [including mediolateral oblique (MLO) view and craniocaudal (CC) view two images] was collected. Thus, a total of 452 normal images (226 MLO view images and 226 CC view images) of this case‐control cohort were analyzed to predict the outcome, i.e., developing breast cancer (cancer cases) or remaining breast cancer‐free (controls) within the follow‐up period. We implemented an end‐to‐end deep learning model and a GoogLeNet‐LDA model and compared their effects in several experimental settings using two mammographic view images and inputting two different subregions of the images to the models. The proposed models were also compared to logistic regression modeling of mammographic breast density. Area under the receiver operating characteristic curve (AUC) was used as the model performance metric. Results The highest AUC was 0.73 [95% Confidence Interval (CI): 0.68–0.78; GoogLeNet‐LDA model on CC view] when using the whole‐breast and was 0.72 (95% CI: 0.67–0.76; GoogLeNet‐LDA model on MLO + CC view) when using the dense tissue, respectively, as the model input. The GoogleNet‐LDA model significantly (all P &lt; 0.05) outperformed the end‐to‐end GoogLeNet model in all experiments. CC view was consistently more predictive than MLO view in both deep learning models, regardless of the input subregions. Both models exhibited superior performance than the percent breast density (AUC = 0.54; 95% CI: 0.49–0.59). Conclusions The proposed deep learning modeling approach can predict short‐term breast cancer risk using normal screening mammogram images. Larger studies are needed to further reveal the promise of deep learning in enhancing imaging‐based breast cancer risk assessment.
Improvement of Bagging performance for classification of imbalanced datasets using evolutionary multi-objective optimization

Today, classification of imbalanced datasets, in which the samples belonging to one class is more than the samples pertaining to other classes, has been paid much attention owing to its vast application in real-world problems. Bagging ensemble method, as one of the most favorite ensemble learning algorithms can provide better performance in solving imbalanced problems when is incorporated with undersampling methods. In Bagging method, diversity of classifiers, performance of classifiers, appropriate number of bags (classifiers) and balanced training sets to train the classifiers are important factors in successfulness of Bagging so as to deal with imbalanced problems. In this paper, through inspiring of evolutionary undersampling (the new undersampling method for seeking the subsets of majority class samples) and taking the mentioned factors into account, i.e., diversity, performance of classifiers, number of classifiers and balanced training set, a multi-objective optimization undersampling is proposed. The proposed method uses multi-objective evolutionary to produce set of diverse, well-performing and (near) balanced bags. Accordingly, the proposed method provides the possibility of generating diverse and well-performing classifiers and determining the number of classifiers in Bagging algorithm. Moreover, two different strategies are employed in the proposed method so as to improve the diversity. In order to confirm the proposed method’s efficiency, its performance is measured over 33 imbalanced datasets using AUC and then compared with 6 well-known ensemble learning algorithms. Investigating the obtained results of such comparisons using non-parametric statistical analysis demonstrate the dominancy of the proposed method compared to other employed techniques, as well.
Disentangling Propagation and Generation for Video Prediction

A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated. Prior approaches to video prediction typically learn either to warp or to hallucinate future pixels, but not both. In this paper, we describe a computational model for high-fidelity video prediction which disentangles motion-specific propagation from motion-agnostic generation. We introduce a confidence-aware warping operator which gates the output of pixel predictions from a flow predictor for non-occluded regions and from a context encoder for occluded regions. Moreover, in contrast to prior works where confidence is jointly learned with flow and appearance using a single network, we compute confidence after a warping step, and employ a separate network to inpaint exposed regions. Empirical results on both synthetic and real datasets show that our disentangling approach provides better occlusion maps and produces both sharper and more realistic predictions compared to strong baselines.
Deep learning-based smart task assistance in wearable augmented reality

Wearable augmented reality (AR) smart glasses have been utilized in various applications such as training, maintenance, and collaboration. However, most previous research on wearable AR technology did not effectively supported situation-aware task assistance because of AR marker-based static visualization and registration. In this study, a smart and user-centric task assistance method is proposed, which combines deep learning-based object detection and instance segmentation with wearable AR technology to provide more effective visual guidance with less cognitive load. In particular, instance segmentation using the Mask R-CNN and markerless AR are combined to overlay the 3D spatial mapping of an actual object onto its surrounding real environment. In addition, 3D spatial information with instance segmentation is used to provide 3D task guidance and navigation, which helps the user to more easily identify and understand physical objects while moving around in the physical environment. Furthermore, 2.5D or 3D replicas support the 3D annotation and collaboration between different workers without predefined 3D models. Therefore, the user can perform more realistic manufacturing tasks in dynamic environments. To verify the usability and usefulness of the proposed method, we performed quantitative and qualitative analyses by conducting two user studies: 1) matching a virtual object to a real object in a real environment, and 2) performing a realistic task, that is, the maintenance and inspection of a 3D printer. We also implemented several viable applications supporting task assistance using the proposed deep learning-based task assistance in wearable AR.
Dealing with class imbalance in classifier chains via random undersampling

Class imbalance is an intrinsic characteristic of multi-label data. Most of the labels in multi-label data sets are associated with a small number of training examples, much smaller compared to the size of the data set. Class imbalance poses a key challenge that plagues most multi-label learning methods. Ensemble of Classifier Chains (ECC), one of the most prominent multi-label learning methods, is no exception to this rule, as each of the binary models it builds is trained from all positive and negative examples of a label. To make ECC resilient to class imbalance, we first couple it with random undersampling. We then present two extensions of this basic approach, where we build a varying number of binary models per label and construct chains of different sizes, in order to improve the exploitation of majority examples with approximately the same computational budget. Experimental results on 16 multi-label datasets demonstrate the effectiveness of the proposed approaches in a variety of evaluation metrics.
A Posterior-Neighborhood-Regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction

Neighborhood regularization is highly important for a latent factor (LF)-based Quality-of-Service (QoS)-predictor since similar users usually experience similar QoS when invoking similar services. Current neighborhood-regularized LF models rely prior information on neighborhood obtained from common raw QoS data or geographical information. The former suffers from low prediction accuracy due to the difficulty of constructing the neighborhood based on incomplete QoS data, while the latter requires additional geographical information that is usually difficult to collect considering information security, identity privacy, and commercial interests in real-world scenarios. To address the above issues, this work proposes a posterior-neighborhood-regularized LF (PLF) model for QoS prediction. The main idea is to decompose the LF analysis process into three phases: a) primal LF extraction, where the LFs are extracted to represent involved users/services based on known QoS data, b) posterior-neighborhood construction, where the neighborhood of each user/service is achieved based on similarities between their primal LF vectors, and c) posterior-neighborhood-regularized LF analysis, where the objective function is regularized by both the posterior-neighborhood of users/services and <inline-formula><tex-math notation="LaTeX">$L_{2}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="luo-ieq1-2961895.gif"/></alternatives></inline-formula>-norm of desired LFs. Experimental results from large scale QoS datasets demonstrate that PLF outperforms state-of-the-art models in terms of both accuracy and efficiency.
A Comprehensive Study and Comparison of Core Technologies for MPEG 3-D Point Cloud Compression

Point cloud based 3D visual representation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way. However, under a limited network bandwidth, it is very challenging to communicate this kind of media due to its huge data volume. Therefore, the MPEG have launched the standardization for point cloud compression (PCC), and proposed three model categories, i.e., TMC1, TMC2, and TMC3. Because the 3D geometry compression methods of TMC1 and TMC3 are similar, TMC1 and TMC3 are further merged into a new platform namely TMC13. In this paper, we first introduce some basic technologies that are usually used in 3D point cloud compression, then review the encoder architectures of these test models in detail, and finally analyze their rate distortion performance as well as complexity quantitatively for different cases (i.e., lossless geometry and lossless color, lossless geometry and lossy color, lossy geometry and lossy color) by using 16 benchmark 3D point clouds that are recommended by MPEG. Experimental results demonstrate that the coding efficiency of TMC2 is the best on average (especially for lossy geometry and lossy color compression) for dense point clouds while TMC13 achieves the optimal coding performance for sparse and noisy point clouds with lower time complexity.
Deep Adaptive Fusion Network for High Performance RGBT Tracking

Due to the complementarity of RGB and thermal data, RGBT tracking has received more and more attention in recent years because it can effectively solve the degradation of tracking performance in dark environments and bad weather conditions. How to effectively fuse the information from RGB and thermal modality is the key to give full play to their complementarities for effective RGBT tracking. In this paper, we propose a high performance RGBT tracking framework based on a novel deep adaptive fusion network, named DAFNet. Our DAFNet consists of a recursive fusion chain that could adaptively integrate all layer features in an end-to-end manner. Due to simple yet effective operations in DAFNet, our tracker is able to reach the near-real-time speed. Comparing with the state-of-the-art trackers on two public datasets, our DAFNet tracker achieves the outstanding performance and yields a new state-of-the-art in RGBT tracking.
Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images

Pixel-level classification for very high resolution (VHR) images is a crucial but challenging task in remote sensing. However, since the diverse ways of satellite image acquisition and the distinct structures of various regions, the distributions of the same semantic classes among different data sets are dissimilar. Therefore, the classification model trained on one data set (source domain) may collapse, when it is directly applied to another one (target domain). To solve this problem, many adversarial-based domain adaptation methods have been proposed. However, these methods only consider the source and the target domains independently in the adversarial training, where only the target domain is explicitly contributed to narrow the gap between the distributions of both domains. Unlike previous methods, we propose a triplet adversarial domain adaptation (TriADA) method that jointly considers both domains to learn a domain-invariant classifier by a novel domain similarity discriminator. Specifically, the discriminator takes a triplet of segmentation maps as input, where two segmentation maps from the same domain are to be distinguished from the two maps from the different domains during the adversarial learning. Consequently, it explicitly considers both domains' information to narrow the distribution gap across domains. To enhance the discriminability of the classifier on the target domain, a class-aware self-training strategy, which depends on the output of the discriminator, is proposed to assign pseudo-labels with high adapted confidence on target data to retrain the classifier. Extensive experiments on several VHR pixel-level classification benchmarks demonstrate the effectiveness of our method as well as its superiority to the-state of the art.
HyperML

This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by exploring Mobius gyrovector spaces where the formalism of the spaces could be utilized to generalize the most common Euclidean vector operations. Overall, this work aims to bridge the gap between Euclidean and hyperbolic geometry in recommender systems through metric learning approach. We propose HyperML (Hyperbolic Metric Learning), a conceptually simple but highly effective model for boosting the performance. Via a series of extensive experiments, we show that our proposed HyperML not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in hyperbolic geometry.
GAN-Based Day-to-Night Image Style Transfer for Nighttime Vehicle Detection

Data augmentation plays a crucial role in training a CNN-based detector. Most previous approaches were based on using a combination of general image-processing operations and could only produce limited plausible image variations. Recently, GAN (Generative Adversarial Network) based methods have shown compelling visual results. However, they are prone to fail at preserving image-objects and maintaining translation consistency when faced with large and complex domain shifts, such as day-to-night. In this paper, we propose AugGAN, a GAN-based data augmenter which could transform on-road driving images to a desired domain while image-objects would be well-preserved. The contribution of this work is three-fold: (1) we design a structure-aware unpaired image-to-image translation network which learns the latent data transformation across different domains while artifacts in the transformed images are greatly reduced; (2) we quantitatively prove that the domain adaptation capability of a vehicle detector is not limited by its training data; (3) our object-preserving network provides significant performance gain in the difficult day-to-night case in terms of vehicle detection. AugGAN could generate more visually plausible images compared to competing methods on different on-road image translation tasks across domains. In addition, we quantitatively evaluate different methods by training Faster R-CNN and YOLO with datasets generated from the transformed results and demonstrate significant improvement on the object detection accuracies by using the proposed AugGAN model.
BERT Representations for Video Question Answering

Visual question answering (VQA) aims at answering questions about the visual content of an image or a video. Currently, most work on VQA is focused on image-based question answering, and less attention has been paid into answering questions about videos. However, VQA in video presents some unique challenges that are worth studying: it not only requires to model a sequence of visual features over time, but often it also needs to reason about associated subtitles. In this work, we propose to use BERT, a sequential modelling technique based on Transformers, to encode the complex semantics from video clips. Our proposed model jointly captures the visual and language information of a video scene by encoding not only the subtitles but also a sequence of visual concepts with a pretrained language-based Transformer. In our experiments, we exhaustively study the performance of our model by taking different input arrangements, showing outstanding improvements when compared against previous work on two well-known video VQA datasets: TVQA and Pororo.
Improving TCP Performance Over WiFi for Internet of Vehicles: A Federated Learning Approach

We propose a novel communication efficient and privacy preserving federated learning framework for enhancing the performance of Internet of Vehicles (IoV), wherein on-vehicle learning models are trained by exchanging inputs, outputs and their learning parameters locally. Moreover, we use analytic modeling as a tool for reasoning and developing the required IoV scenario and stabilize their data flow dynamics by considering TCP CUBIC streams over WiFi networks to prove our idea.
Smart healthcare and quality of service in IoT using grey filter convolutional based cyber physical system

The relationship between technology and healthcare society rises due to the intelligent Internet of Things (IoT) with endless networking capabilities for medical data analysis. Deep Neural Networks and the swift public embracement of medical wearable have been productively metamorphosed in the recent few years. Deep Neural Network-powered IoT allowed innovative developments for medical society and distinctive probabilities to the medical data analysis in the healthcare industry (Yin, Yang, Zhang, & Oki, 2016). Despite this progress, several issues still required to be handled while concerning the quality of service. The key to flourishing in the shift from client-oriented to patient-oriented medical data analysis for healthcare society is applying deep networks to provide a high level of quality in key attributes such as end-to-end response time, overhead and accuracy. In this paper, we propose a holistic Deep Neural Network-driven IoT smart health care method called, Grey Filter Bayesian Convolution Neural Network (GFB-CNN) based on real-time analytics. In this paper, we propose a holistic AI-driven IoT eHealth architecture based on the Grey Filter Bayesian Convolution Neural Network in which the key quality of service parameters like, time and overhead is reduced with a higher rate of accuracy. The feasibility of the method is investigated using a comprehensive Mobile HEALTH (MHEALTH) dataset. This illustrative example discusses and addresses all important aspects of the proposed method from design suggestions such as corresponding overheads, time, accuracy compared to state-of-the-art methods. By simulation, the performance of GFB-CNN method is compared to the state-of-the-art methods with various synthetically generated scenarios. Results show that with minimal time and overhead incurred for sensing and data collection, our method accurately evaluates medical data analysis for heart signals by efficient differentiation between healthy and unhealthy heart signals.
A survey of Behavior Trees in robotics and AI

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.