Reviewerly/exHarmony huggingface dataset:
Asynchronous Blind Modulation Classification in the Presence of Non-Gaussian Noise

Blind modulation classification is an essential and fundamental step before signal detection in intelligent communication systems. However, in complicated electromagnetic environment, identifying asynchronous modulated signals remains a challenging task. In order to improve the performance of asynchronous modulation classification in non-Gaussian noise, this paper proposes a novel BMC method based on complex correntropy and Conv1D (one-dimensional convolution neural network), namely CC-Conv1D. First, complex correntropy is employed to extract discriminating features from asynchronous modulated signals, while non-Gaussian noise can be effectively suppressed by complex correntropy. Furthermore, theoretical analysis is conducted to demonstrate the effectiveness of complex correntropy in feature extraction and non-Gaussian noise suppression. Moreover, Conv1D is adopted to identify different modulation schemes due to its merits of recognizing the shape of extracted features with low computational complexity. Experimental implementation is conducted via USRP N210 and USRP 2901, and the results show that our solution can achieve at least 97.5% accuracy in practical wireless communications.
An Automated Approach to Detect Breast Cancer Tissue Using Ultrasound Images

Cancer is a kind of cell that develops out of control of the regular drives that normalizes growth. Breast cancer is one of the deadly forms of cancers for the human. Initial detection of cancerous tissues is likely to monitor the patient's strength to deal with the disease and live more. Electronic modalities are applied to diagnose breast cancers. Due to low cost and safety, ultrasound scanning is considered as one of the most frequently used electronic modalities for breast cancer detection. This research study proposed an automated approach for breast cancer detection using ultrasound gray-scale images. To this end, enhancement operation was used at the beginning to minimize the grayscale color shifts. Median filter was used to facilitate better segmentation removing maximum unwanted noises. Threshold-based segmentation (Threshold based OTSU) was used as the test images used in this study are gray-scale images. Finally, necessary feature information that is used in the pathology laboratory by pathology experts is used in this study to identify the ROI (Region of interests) to detect breast cancer tissues. This study proposed approach achieved an average accuracy rate of 93.11%. The depicted experimental outcomes of the proposed approach outperform other contemporary existing available approaches in terms of accuracy while maintaining the medical experts' acceptable accuracy rate.
Attack Agnostic Statistical Method for Adversarial Detection

Deep Learning based AI systems have shown great promise in various domains such as vision, audio, autonomous systems (vehicles, drones), etc. Recent research on neural networks has shown the susceptibility of deep networks to adversarial attacks - a technique of adding small perturbations to the inputs which can fool a deep network into misclassifying them. Developing defenses against such adversarial attacks is an active research area, with some approaches proposing robust models that are immune to such adversaries, while other techniques attempt to detect such adversarial inputs. In this paper, we present a novel statistical approach for adversarial detection in image classification. Our approach is based on constructing a per-class feature distribution and detecting adversaries based on comparison of features of a test image with the feature distribution of its class. For this purpose, we make use of various statistical distances such as ED (Energy Distance), MMD (Maximum Mean Discrepancy) for adversarial detection, and analyze the performance of each metric. We experimentally show that our approach achieves good adversarial detection performance on MNIST and CIFAR-10 datasets irrespective of the attack method, sample size and the degree of adversarial perturbation.
Intelligent sentence completion based on global context dependent recurrent neural network language model

This paper proposes a global context dependent recurrent neural network language model to obtain the global semantics of the target in the sentence completion task, in order to overcome the problem of the limited context in existing recurrent neural network language model. The model uses all the preceding and succeeding words of the target to construct the global context of the target, so as to capture the complete semantic information of the target without resorting to any external sentence information. This paper verifies the validity of the model on the data in Microsoft Sentence Completion Challenge. The experimental results show that the proposed model obtains higher completion accuracy than other language models. Moreover, it also obtains higher completion accuracy than the commonly used word similarity model, even better than RNNMEs and Skip-gram+RNNMEs combination models.
Sentence Classification with Deep Learning Method For Virtual Assistant Applications

This paper investigates three different deep learning method performance for virtual assistant applications about sentence classification. The classification is based in Turkish texts. For three different model we demonstrate the performance of each model. We investigate Convolutional Neural Network (CNN), Region Convolutional Neural Network (RCNN) and Long Short Term Memory (LSTM) deep learning methods and compare the accuracy results of the related models. Furthermore, we aim to select the best classification model for our dataset.We have researched effect of the hyper parameters to model accuracy and we used best hyper parameters for each methods and we aimed to gain best performance for our dataset.This research helps applications like virtual assistant with classification of the sentence and giving the output of the class. The output of classification could be a text, image or document. Benefit of this comparison of the methods we realized that instance number increases the model accuracy. The best method for our dataset was the Convolutional Neural Networks (CNN) with the %87.3 accuracy.
Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.
Example‐based image recoloring in an indoor environment

Abstract Color structure of a home scene image closely relates to the material properties of its local regions. Existing color migration methods typically fail to fully infer the correlation between the coloring of local home scene regions, leading to a local blur problem. In this paper, we propose a color migration framework for home scene images. It picks the coloring from a template image and transforms such coloring to a home scene image through a simple interaction. Our framework comprises three main parts. First, we carry out an interactive segmentation to divide an image into local regions and extract their corresponding colors. Second, we generate a matching color table by sampling the template image according to the color structure of the original home scene image. Finally, we transform colors from the matching color table to the target home scene image with the boundary transition maintained. Experimental results show that our method can effectively transform the coloring of a scene matching with the color composition of a given natural or interior scenery.
Real time prediction of irregular periodic time series data

By means of a novel time‐dependent cumulated variation penalty function, a new class of real‐time prediction methods is developed to improve the prediction accuracy of time series exhibiting irregular periodic patterns: in particular, the breathing motion data of the patients during robotic radiation therapy. It is illustrated that for both simulated and empirical data involving changes in mean, trend, and amplitude, the proposed methods outperform existing forecasting methods based on support vector machines and artificial neural network in terms of prediction accuracy. Moreover, the proposed methods are designed so that real‐time updates can be done efficiently with O (1) computational complexity upon the arrival of a new signal without scanning the old data repeatedly.
High Accuracy Conveyor-Based Object Counting Algorithm

This paper proposes Redundancy Count (RC) method for accurately counting moving objects on a moving plane or a conveyor system. The method works on top of an image processing algorithm that detects the positions of objects. The image processing fault can also be corrected by redundancy evaluation of the method. The experimental results show an improvement of 7-8 times less counting error than a frame-based counting method.
Block Toeplitz matrices for burst-correcting convolutional codes

In this paper we study a problem in the area of coding theory. In particular, we focus on a class of error-correcting codes called convolutional codes. We characterize convolutional codes that can correct bursts of erasures with the lowest possible delay. This characterization is given in terms of a block Toeplitz matrix with entries in a finite field that is built upon a given generator matrix of the convolutional code. This result allows us to provide a concrete construction of a generator matrix of a convolutional code with entries being only zeros or ones that can recover bursts of erasures with low delay. This construction admits a very simple decoding algorithm and, therefore, simplifies the existing schemes proposed recently in the literature.
Expert System Diagnosing Disease of Honey Guava Using Bayes Method

Abstract Honey Guava Fruit (Green Guava Deli) is one of the fruit that are very popular, liked, and consumed by the people. The pProblems that cause a decline in the quality of guava due to honey guava plants can also be attacked by disease. The limited access information about honey guava disease is one of the obstacles, while the number of agricultural experts is still insufficient. In this study a media system will built with an expert system approach. The application development phase begins with the system analysis stage, namely data analysis and system requirements description, building a knowledge base, Data Flow Diagram, Entity Relationship Diagram, and creating a table structure, table design, and interface menu design. After the design phase is complete, it is continued to the implementation and testing phase of the application. This application uses Visual basic. Net as a programming language and Sql Server as a database. The research was conducted to make Honey Guava Disease Diagnosis System Expert System software that can work like an agricultural expert. The system is able to diagnose as many as 3 diseases using the Bayes Theorem method.
A P2P Surplus Energy Trade Among Neighbors Based on Hyperledger Fabric Blockchain

We propose a Hyperledger Fabric blockchain based P2P surplus energy trade system in which participating prosumers and consumers trade surplus energy with high reliability and scalability. Previous POW (Proof-of-Work)-based blockchain such as public Ethereum may cause low throughput and long latency due to their probabilistic consensus algorithm which does not guarantee real-time. We choose Hyperledger Fabric blockchain because its consensus mechanism may be divided into individual phases (Endorsement, Ordering and Validation), which is eligible for speed up through parallel processing. We illustrate how to build an energy trade system using Hyperledger Fabric with set up of endorsing peers, orderers and validating peers. We also show how to protect the system from possible attackers by set up of CA (certificate authority) and MSP (membership service provider) in IBM Blockchain Platform.
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos

Object detection has been vigorously investigated for years but fast accurate detection for real-world scenes remains a very challenging problem. Overcoming drawbacks of single-stage detectors, we take aim at precisely detecting objects for static and temporal scenes in real time. Firstly, as a dual refinement mechanism, a novel anchor-offset detection is designed, which includes an anchor refinement, a feature location refinement, and a deformable detection head. This new detection mode is able to simultaneously perform two-step regression and capture accurate object features. Based on the anchor-offset detection, a dual refinement network (DRNet) is developed for high-performance static detection, where a multi-deformable head is further designed to leverage contextual information for describing objects. As for temporal detection in videos, temporal refinement networks (TRNet) and temporal dual refinement networks (TDRNet) are developed by propagating the refinement information across time. We also propose a soft refinement strategy to temporally match object motion with the previous refinement. Our proposed methods are evaluated on PASCAL VOC, COCO, and ImageNet VID datasets. Extensive comparisons on static and temporal detection verify the superiority of DRNet, TRNet, and TDRNet. Consequently, our developed approaches run in a fairly fast speed, and in the meantime achieve a significantly enhanced detection accuracy, i.e., 84.4% mAP on VOC 2007, 83.6% mAP on VOC 2012, 69.4% mAP on VID 2017, and 42.4% AP on COCO. Ultimately, producing encouraging results, our methods are applied to online underwater object detection and grasping with an autonomous system. Codes are publicly available at this https URL.
Image compression based on periodic principal components

In the present paper, the almost periodicity of the first principal components is studied, with the aim of being able to use less information in order to obtain acceptable reconstructions of compressed images. The results of this study show that by working with the periodic principal components of images under analysis, it is possible to obtain an additional reduction to that obtained by using the original principal components. Specifically, it is shown that if the principal components that are considered periodic are replaced by their period plus a trend, it can be said that the reconstruction achieved using these periodic principal components is very close to the reconstruction achieved using the original principal components.
Unsteady Flow Visualization via Physics Based Pathline Exploration

This work proposes to analyze the time-dependent characteristics of the physical attributes measured along pathlines derived from unsteady flows, which can be represented as a series of time activity curves (TAC). A new TAC-based unsteady flow visualization and analysis framework is proposed. The center of this framework is a new event-based distance metric (EDM) that compares the similarity of two TACs, from which a new spatio-temporal, hierarchical clustering of pathlines based on their physical attributes and an attribute-based pathline exploration are proposed. These techniques are integrated into a visual analytics system, which has been applied to a number of unsteady flow in 2D and 3D to demonstrate its utility.
Variance Reduction With Sparse Gradients

Variance reduction methods which use a mixture of large and small batch gradients, such as SVRG (Johnson & Zhang, 2013) and SpiderBoost (Wang et al., 2018), require significantly more computational resources per update than SGD (Robbins & Monro, 1951). We reduce the computational cost per update of variance reduction methods by introducing a sparse gradient operator blending the top-K operator (Stich et al., 2018; Aji & Heafield, 2017) and the randomized coordinate descent operator. While the computational cost of computing the derivative of a model parameter is constant, we make the observation that the gains in variance reduction are proportional to the magnitude of the derivative. In this paper, we show that a sparse gradient based on the magnitude of past gradients reduces the computational cost of model updates without a significant loss in variance reduction. Theoretically, our algorithm is at least as good as the best available algorithm (e.g. SpiderBoost) under appropriate settings of parameters and can be much more efficient if our algorithm succeeds in capturing the sparsity of the gradients. Empirically, our algorithm consistently outperforms SpiderBoost using various models to solve various image classification tasks. We also provide empirical evidence to support the intuition behind our algorithm via a simple gradient entropy computation, which serves to quantify gradient sparsity at every iteration.
Attack Detection in Recommender Systems Using Subspace Outlier Detection Algorithm

Recommender system plays an important role in today’s online business. At the time of choosing right product or service from Internet, recommender system helps customers by providing a personalized recommendation. Collaborative filtering algorithms generate recommendations by considering the opinions of the similar-minded customers where similarity between customers is calculated by taking into account the previous purchase information of customers. Recommender systems are vulnerable to shilling attacks where fake customer profiles are inserted to promote or demote a product. In this paper, we use outlier detection algorithm to detect such types of attacks. Due to high dimensionality of the data that recommender systems deal with, we have applied subspace outlier detection algorithm in this context.
Evolution of functional size measures through ICONIX process phases

Abstract Functional size measures are used in several software development processes, because they can be applied as soon as the functional user requirements of a software application have been specified. Quite often, functional user requirements are used as the main input of effort and cost estimation. Since effort and cost estimates are fundamental for decision making, project managers would like to get reasonably accurate estimates even before user requirements have been specified completely and at the proper level of detail. To this end, early size estimation methods have been proposed. In this paper, we consider a specific situation; namely, we consider processes in which the requirements elicitation and modeling phases are carried out according to the ICONIX process. The ICONIX process involves a few stages, each one tackling an aspect of requirements and delivering a specific artifact, in the form of a UML model. We explore the possibility of deriving from ICONIX artifacts the information needed to estimate the final size of the application. To this end, we analyzed the requirements of 21 software applications and derived statistical models that correlate the information extracted from ICONIX artifacts to the size, expressed in function points, of the application. The analysis of the achieved estimates shows that the approach is viable, in that it provides early estimates with good accuracy.
Med-Match: An Interactive Game to Learn Medications for Clinical Practice

Knowing medications and their use in patient care is a component of safe practice. The Med-Match game transforms learning medications from a solitary task to an interactive activity. Educational games create real-life scenarios in a safe, context-rich environment without real-world consequences. Gaming is a form of active learning; learning occurs as a result of student-to-student interaction, peer feedback, and faculty debriefing. Although this activity was initially developed for nursing students, it can also be used with new RNs being oriented to a clinical setting or as an annual review for experienced nurses.
Assessment of Optical See-Through Head Mounted Display Calibration for Interactive Augmented Reality

Interaction in Augmented Reality environments requires the precise alignment of virtual elements added to the real scene. This can be achieved if the egocentric perception of the augmented scene is coherent in both the virtual and the real reference frames. To this aim, a proper calibration of the complete system, composed of the Augmented Reality device, the user and the environment, should be performed. Over the years, several calibration techniques have been proposed, and objectively evaluating their performance has shown to be troublesome. Since only the user can assess the hologram alignment fidelity, most researchers quantify the calibration error with subjective data from user studies. This paper describes the calibration process of an optical see-through device, based on a visual alignment method, and proposes a technique to objectively quantify the residual misalignment error.
Comparative Assessment of Zero-Inflated Models with Application to HIV Exposed Infants Data

In a typical Kenyan HIV clinical setting, there is a likelihood of registering many zeros during the routine monthly data collection of new HIV infections among HIV exposed infants (HEI).This is attributed to the implementation of the prevention of mother to child transmission (PMTCT) policies.However, even though the PMTCT policy is implemented uniformly across all public health facilities, implementation naturally differs from every facility due to differential health systems and infrastructure.This leads to structured zero among reported positive HEI (where PMTCT implementation is optimum) and non-structured zero among reported positive HEI (where PMTCT implementation is not optimum).Hence the classical zero-inflated and hurdle models that do not account for the abundance of structured and non-structured zeros in the data can give misleading results.The purpose of this study is to systematically compare performance of the various zero-inflated models with an application to HIV Exposed Infants (HEI) in the context of structured and unstructured zeros.We revisit zero-inflated, hurdle models, Poisson and negative binomial count models and conduct the simulations by varying sample size and levels of abundance zeros.Results from simulation study and real data analysis of exposed infant diagnosis show the negative binomial emerging as the best performing model when fitting data with both structured and non-structured zeros under various settings.
Intelligent Speed Control of DC Motor using ANFIS D Controller

The primary objective of this paper is to present the speed controlling method of a separately excited DC motor with Adaptive Neuro Fuzzy Inference System (ANFIS) based derivative controller. Efficient control of speed of DC motor has been achieved using by reducing undershoot, overshoot, peak-peak ripple and s-s (Steady-State) error by the proposed approach. A derivative controller has also been used to validate the performance improvement by comparison between the two methods. From the assessment, it clearly shows that, the suggested ANFIS D controller provides better performance including overshoot, undershoot, peak-peak ripple and s-s error reduction and hence improving the speed regulation technique of separately excited DC motor.