Reviewerly/exHarmony huggingface dataset:
Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images

The main aim of this paper is to design and develop an approach for kidney disease detection and segmentation using a combination of clustering and classification approach. Nowadays, kidney stone detection and segmentation is one of the crucial procedures in surgical and treatment planning for ultrasound images. However, at present, kidney stone segmentation in ultrasound images is mostly performed manually in clinical practice. Apart from being time-consuming, manual stone delineation is difficult and depends on the individual operator. Therefore, in this work, we proposed a kidney stone detection using artificial neural network and segmentation using multi-kernel k-means clustering algorithm. Normally, the system comprises of four modules like (i) preprocessing, (ii) feature extraction, (iii) classification and (iv) segmentation. Primarily, we eliminate the noise present in the input image using median filter. Then, we extract the important GLCM features from the image. After that, we classify the image as normal or abnormal using neural network classifier. Finally, the abnormal images are given to the segmentation stage to segment the stone and tumor part separately using multi. Kernel K-means clustering algorithm. The experimentation results show that the proposed system as linear + quadratic based segmentation achieves the maximum accuracy of 99.61%, compare with all other methods.
Low-rank local tangent space embedding for subspace clustering

Subspace techniques have gained much attention for their remarkable efficiency in representing high-dimensional data, in which sparse subspace clustering (SSC) and low-rank representation (LRR) are two commonly used prototypes in the fields of pattern recognition, computer vision and signal processing. Both of them aim at constructing a block sparse matrix via linearly representing data to make them be embedded into linear subspaces. However, few datasets satisfy the linear subspace assumption in the real world. In this paper, data are peered from viewpoint of manifold architecture under the framework of sparse representation. A globally low-rank representation with the Frobenius norm minimization is constructed under the constraint of local manifold embedding and a novel low-rank local embedding representation (LRLER) model for subspace clustering of datasets is proposed. In this model, the local as well as global manifold structures of a dataset are concerned. Clusters of a dataset are considered as sub-manifolds embedded in low-dimensional subspaces. To represent and segment samples with hybrid neighbors or interlaced manifold structures, the local tangent space analysis strategy is introduced to characterize the local structure of neighborhood of samples. The coefficients of locally linear embedding are rectified according to the relationship between local tangent spaces of samples and their neighbors. A local tangent space based low-rank local embedding representation model (LRLTSER) is built to deal with data with neighborhood aliasing distortion. Extensive experiments on synthetic datasets and real-world datasets are implemented and experimental results show superior performance of the proposed methods for subspace clustering compared to the state-of-the-art techniques.
A deeper graph neural network for recommender systems

Interaction data in recommender systems are usually represented by a bipartite user–item graph whose edges represent interaction behavior between users and items. The data sparsity problem, which is common in recommender systems, is the result of insufficient interaction data in the link prediction on graphs. The data sparsity problem can be alleviated by extracting more interaction behavior from the bipartite graph, however, stacking multiple layers will lead to over-smoothing, in which case, all nodes will converge to the same value. To address this issue, we propose a deeper graph neural network in this paper that can predict links on a bipartite user–item graph using information propagation. An attention mechanism is introduced to our method to address the problem that variable size inputs for each node on a bipartite graph. Our experimental results demonstrate that our proposed method outperforms five baselines, suggesting that the interactions extracted help to alleviate the data sparsity problem and improve recommendation accuracy.
Star-Based Reachability Analysis of Deep Neural Networks

This paper proposes novel reachability algorithms for both exact (sound and complete) and over-approximation (sound) analysis of deep neural networks (DNNs). The approach uses star sets as a symbolic representation of sets of states, which are known in short as stars and provide an effective representation of high-dimensional polytopes. Our star-based reachability algorithms can be applied to several problems in analyzing the robustness of machine learning methods, such as safety and robustness verification of DNNs. The star-based reachability algorithms are implemented in a software prototype called the neural network verification (NNV) tool that is publicly available for evaluation and comparison. Our experiments show that when verifying ACAS Xu neural networks on a multi-core platform, our exact reachability algorithm is on average about 19 times faster than Reluplex, a satisfiability modulo theory (SMT)-based approach. Furthermore, our approach can visualize the precise behavior of DNNs because the reachable states are computed in the method. Notably, in the case that a DNN violates a safety property, the exact reachability algorithm can construct a complete set of counterexamples. Our star-based over-approximate reachability algorithm is on average 118 times faster than Reluplex on the verification of properties for ACAS Xu networks, even without exploiting the parallelism that comes naturally in our method. Additionally, our over-approximate reachability is much less conservative than DeepZ and DeepPoly, recent approaches utilizing zonotopes and other abstract domains that fail to verify many properties of ACAS Xu networks due to their conservativeness. Moreover, our star-based over-approximate reachability algorithm obtains better robustness bounds in comparison with DeepZ and DeepPoly when verifying the robustness of image classification DNNs.
Deep Convolutional Neural Network-Based Approaches for Face Recognition

Face recognition (FR) is defined as the process through which people are identified using facial images. This technology is applied broadly in biometrics, security information, accessing controlled areas, keeping of the law by different enforcement bodies, smart cards, and surveillance technology. The facial recognition system is built using two steps. The first step is a process through which the facial features are picked up or extracted, and the second step is pattern classification. Deep learning, specifically the convolutional neural network (CNN), has recently made commendable progress in FR technology. This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification. The study considers CNN architecture, which has so far recorded the best outcome in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the past years, more specifically, AlexNet and ResNet-50. In order to determine performance optimization of the CNN algorithm, recognition accuracy was used as a determinant. Improved classification rates were seen in the comprehensive experiments that were completed on the various datasets of ORL, GTAV face, Georgia Tech face, labelled faces in the wild (LFW), frontalized labeled faces in the wild (F_LFW), YouTube face, and FEI faces. The result showed that our model achieved a higher accuracy compared to most of the state-of-the-art models. An accuracy range of 94% to 100% for models with all databases was obtained. Also, this was obtained with an improvement in recognition accuracy up to 39%.
Learning Rich Features at High-Speed for Single-Shot Object Detection

Single-stage object detection methods have received significant attention recently due to their characteristic realtime capabilities and high detection accuracies. Generally, most existing single-stage detectors follow two common practices: they employ a network backbone that is pretrained on ImageNet for the classification task and use a top-down feature pyramid representation for handling scale variations. Contrary to common pre-training strategy, recent works have demonstrated the benefits of training from scratch to reduce the task gap between classification and localization, especially at high overlap thresholds. However, detection models trained from scratch require significantly longer training time compared to their typical finetuning based counterparts. We introduce a single-stage detection framework that combines the advantages of both fine-tuning pretrained models and training from scratch. Our framework constitutes a standard network that uses a pre-trained backbone and a parallel light-weight auxiliary network trained from scratch. Further, we argue that the commonly used top-down pyramid representation only focuses on passing high-level semantics from the top layers to bottom layers. We introduce a bi-directional network that efficiently circulates both low-/mid-level and high-level semantic information in the detection framework. Experiments are performed on MS COCO and UAVDT datasets. Compared to the baseline, our detector achieives an absolute gain of 7.4% and 4.2% in average precision (AP) on MS COCO and UAVDT datasets, respectively using VGG backbone. For a 300×300 input on the MS COCO test set, our detector with ResNet backbone surpasses existing single-stage detection methods for single-scale inference achieving 34.3 AP, while operating at an inference time of 19 milliseconds on a single Titan X GPU. Code is avail- able at https://github.com/vaesl/LRF-Net.
DADNet

Most existing CNN-based methods for crowd counting always suffer from large scale variation in objects of interest, leading to density maps of low quality. In this paper, we propose a novel deep model called Dilated-Attention-Deformable ConvNet (DADNet), which consists of two schemes: multi-scale dilated attention and deformable convolutional DME (Density Map Estimation). The proposed model explores a scale-aware attention fusion with various dilation rates to capture different visual granularities of crowd regions of interest, and utilizes deformable convolutions to generate a high-quality density map. There are two merits as follows: (1) varying dilation rates can effectively identify discriminative regions by enlarging the receptive fields of convolutional kernels upon surrounding region cues, and (2) deformable CNN operations promote the accuracy of object localization in the density map by augmenting the spatial object location sampling with adaptive offsets and scalars. DADNet not only excels at capturing rich spatial context of salient and tiny regions of interest simultaneously, but also keeps a robustness to background noises, such as partially occluded objects. Extensive experiments on benchmark datasets verify that DADNet achieves the state-of-the-art performance. Visualization results of the multi-scale attention maps further validate the remarkable interpretability achieved by our solution.
Twitter Sentiment Analysis Based on Ordinal Regression

In recent years, research on Twitter sentiment analysis, which analyzes Twitter data (tweets) to extract user sentiments about a topic, has grown rapidly. Many researchers prefer the use of machine learning algorithms for such analysis. This study aims to perform a detailed sentiment analysis of tweets based on ordinal regression using machine learning techniques. The proposed approach consists of first pre-processing tweets and using a feature extraction method that creates an efficient feature. Then, under several classes, these features scoring and balancing. Multinomial logistic regression (SoftMax), Support Vector Regression (SVR), Decision Trees (DTs), and Random Forest (RF) algorithms are used for sentiment analysis classification in the proposed framework. For the actual implementation of this system, a twitter dataset publicly made available by the NLTK corpora resources is used. Experimental findings reveal that the proposed approach can detect ordinal regression using machine learning methods with good accuracy. Moreover, results indicate that Decision Trees obtains the best results outperforming all the other algorithms.
Data science applications to string theory

We first introduce various algorithms and techniques for machine learning and data science. While there is a strong focus on neural network applications in unsupervised, supervised and reinforcement learning, other machine learning techniques are discussed as well. These include various clustering and anomaly detection algorithms, support vector machines, and decision trees. In addition, we review data science techniques such as genetic algorithms and topological data analysis. This first part of the review makes some reference to concepts in physics, but the explanations and examples do not assume any knowledge of string theory and should therefore be accessible to a wide variety of readers with a physics background. After that, we illustrate applications to string theory. We give an overview of existing string theory data sets and describe how they can be studied using data science techniques. We also explain the computational complexity involved in the investigation of string vacua. Example codes that illustrate the techniques introduced in this review are available from Fabian Ruehle (0000).
Adversarial Cross-Spectral Face Completion for NIR-VIS Face Recognition

Near infrared-visible (NIR-VIS) heterogeneous face recognition refers to the process of matching NIR to VIS face images. Current heterogeneous methods try to extend VIS face recognition methods to the NIR spectrum by synthesizing VIS images from NIR images. However, due to the self-occlusion and sensing gap, NIR face images lose some visible lighting contents so that they are always incomplete compared to VIS face images. This paper models high-resolution heterogeneous face synthesis as a complementary combination of two components: a texture inpainting component and a pose correction component. The inpainting component synthesizes and inpaints VIS image textures from NIR image textures. The correction component maps any pose in NIR images to a frontal pose in VIS images, resulting in paired NIR and VIS textures. A warping procedure is developed to integrate the two components into an end-to-end deep network. A fine-grained discriminator and a wavelet-based discriminator are designed to improve visual quality. A novel 3D-based pose correction loss, two adversarial losses, and a pixel loss are imposed to ensure synthesis results. We demonstrate that by attaching the correction component, we can simplify heterogeneous face synthesis from one-to-many unpaired image translation to one-to-one paired image translation, and minimize the spectral and pose discrepancy during heterogeneous recognition. Extensive experimental results show that our network not only generates high-resolution VIS face images but also facilitates the accuracy improvement of heterogeneous face recognition.
HMS-Net: Hierarchical Multi-Scale Sparsity-Invariant Network for Sparse Depth Completion

Dense depth cues are important and have wide applications in various computer vision tasks. In autonomous driving, LIDAR sensors are adopted to acquire depth measurements around the vehicle to perceive the surrounding environments. However, depth maps obtained by LIDAR are generally sparse because of its hardware limitation. The task of depth completion attracts increasing attention, which aims at generating a dense depth map from an input sparse depth map. To effectively utilize multi-scale features, we propose three novel sparsity-invariant operations, based on which, a sparsity-invariant multi-scale encoder-decoder network (HMS-Net) for handling sparse inputs and sparse feature maps is also proposed. Additional RGB features could be incorporated to further improve the depth completion performance. Our extensive experiments and component analysis on two public benchmarks, KITTI depth completion benchmark and NYU-depth-v2 dataset, demonstrate the effectiveness of the proposed approach. As of Aug. 12th, 2018, on KITTI depth completion leaderboard, our proposed model without RGB guidance ranks 1st among all peer-reviewed methods without using RGB information, and our model with RGB guidance ranks 2nd among all RGB-guided methods.
Dynamic Resource Provisioning With Fault Tolerance for Data-Intensive Meteorological Workflows in Cloud

Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, the meteorological services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes for hosting the meteorological workflows fail, all sorts of consequences (e.g., data loss, makespan enlargement, performance degradation, etc.) could arise. Thus recovering the failed tasks as well as optimizing the makespan and the load balance of the computing nodes is still a critical challenge. To address this challenge, a dynamic resource provisioning method (DRPM) with fault tolerance for the data-intensive meteorological workflows is proposed in this article. Technically, the Virtual Layer 2 (VL2) network topology is exploited to build meteorological cloud infrastructure. Then, the nondominated sorting genetic algorithm II (NSGA-II) is employed to minimize the makespan and improve the load balance. Finally, comprehensive experimental analysis of DRPM are proceeded.
DynamoNet: Dynamic Action and Motion Network

In this paper, we are interested in self-supervised learning the motion cues in videos using dynamic motion filters for a better motion representation to finally boost human action recognition in particular. Thus far, the vision community has focused on spatio-temporal approaches using standard filters, rather we here propose dynamic filters that adaptively learn the video-specific internal motion representation by predicting the short-term future frames. We name this new motion representation, as dynamic motion representation (DMR) and is embedded inside of 3D convolutional network as a new layer, which captures the visual appearance and motion dynamics throughout entire video clip via end-to-end network learning. Simultaneously, we utilize these motion representation to enrich video classification. We have designed the frame prediction task as an auxiliary task to empower the classification problem. With these overall objectives, to this end, we introduce a novel unified spatio-temporal 3D-CNN architecture (DynamoNet) that jointly optimizes the video classification and learning motion representation by predicting future frames as a multi-task learning problem. We conduct experiments on challenging human action datasets: Kinetics 400, UCF101, HMDB51. The experiments using the proposed DynamoNet show promising results on all the datasets.
EML-NET: An Expandable Multi-Layer NETwork for saliency prediction

Existing advanced saliency systems have been proposed using Convolutional Neural Networks (CNNs), the prior knowledge of objectness is crucial for saliency detection. However, we show that the use of objectness may also limit the power of CNNs on the images which do not contain a salient object. Besides, one previous study has shown applying a deeper CNN model may not improve the performance, the reason behind could be due to the limited training data. In this work, we aim at investigating the effect of prior knowledge from other domains and proposing a multi-modality system. Our work shows the depth of the model still plays an important role in saliency detection when the training data is large enough. To do this in a sophisticated manner can be complex, and also result in unwieldy networks or produce competing objectives that are hard to balance. For the scalability, our multi-modality system is trained in an almost end-to-end piece-wise fashion. The encoder and decoder components are separately trained to deal with complexity tied to the computational paradigm and required space. Therefore, our system can be easily extended to include a variety of prior knowledge for saliency detection. Besides, we also propose a combined saliency loss based on modifications of Pearson correlation and normalized scanpath saliency. Our experiment shows the combined loss can train a CNN model more comprehensively for saliency detection. We denote our expandable multi-layer network as EML-NET and our method achieves the state-of-the-art results on the public saliency benchmarks, SALICON, MIT300 and CAT2000.
Two-Dimensional Modular Chaotification System for Improving Chaos Complexity

Chaotic systems are widely studied in various research areas such as signal processing and secure communication. Existing chaotic systems may have drawbacks such as discontinuous chaotic ranges and incomplete output distributions. These drawbacks may lead to the defects of some chaos-based applications. To accommodate these challenges, this paper proposes a two-dimensional (2D) modular chaotification system (2D-MCS) to improve the chaos complexity of any 2D chaotic map. Because the modular operation is a bounded transform, the improved chaotic maps by 2D-MCS can generate chaotic behaviors in wide parameter ranges while existing chaotic maps cannot. Three improved chaotic maps are presented as typical examples to verify the effectiveness of 2D-MCS. The chaos properties of one example of 2D-MCS are mathematically analyzed using the definition of Lyapunov exponent. Performance evaluations demonstrate that these improved chaotic maps have continuous and large chaotic ranges, and their outputs are distributed more uniformly than the outputs of existing 2D chaotic maps. To show the application of 2D-MCS, we apply the improved chaotic maps of 2D-MCS to secure communication. The simulation results show that these improved chaotic maps exhibit better performance than several existing and newly developed chaotic maps in terms of resisting different channel noise.
A systematic review of the bubble dynamics of cryptocurrency prices

This paper surveys the academic literature concerning the formation of pricing bubbles in digital currency markets. Studies indicate that several bubble phases have taken place in Bitcoin prices, mostly during the years 2013 and 2017. Other digital currencies of primary importance, such as Ethereum and Litecoin, also exhibit several bubble phases. The Augmented Dickey Fuller (ADF) as well as the Log-Periodic Power Law (LPPL) methodology are the most frequently employed techniques for bubble detection and measurement. Based on much academic research, Bitcoin appears to have been in a bubble-phase since June 2015, while Ethereum, NEM, Stellar, Ripple, Litecoin and Dash have been denoted as possessing bubble-like characteristics since September 2015. However, this latter group possess little academic evidence supporting the presence of bubbles since early 2018. An overall perspective is provided based on a robust bibliography based on large deviations of market quotes from fundamental values that can serve as a guide to policymakers, academics and investors.