Reviewerly/exHarmony huggingface dataset:
Color Image Steganography Scheme Based on Convolutional Neural Network

Image steganography has always an important part in the field of information security. In this paper, we propose a new color image steganography scheme base on convolutional neural network, which can hide the secret image into the cover image of the same size. The steganography scheme consists of two parts, the hiding network and the reveal network. Hiding network uses the skip connection, the low-level features of the image can be transmitted to each subsequent layer, which helps to hide the details of the secret image. Experimental results show that this scheme can not only achieve the steganography of color images, but also maintain a high embedding ability. Compared with other methods, the PSNR and SSIM mean values of our steganography image reach 43.6 dB and 0.987, the secret images extracted through the extracting network have also achieved good results.
Blockchain and the Future of Digital Marketing

Blockchain is a unique new technology affecting the way facts are stored and shared electronically. Blockchain in digital advertising reflects clarity, security, and access to waft revenue and streamflow. It can promote digital advertising to consumers, which is profitable in spending big money on digital advertising campaigns. As per new technology, consumers can share and improve their statistics simultaneously with advertisers and advertisers. Blockchain technology can be considered to restore data control to statistical owners focused on digital advertising. Today's society has grown into a very digital one where local technology plays a significant role in everyone's lives. Moreover, society is advancing rapidly at an alarming rate with innovation in every corner and other business made online. The authors of the concept chose to find out how blockchain works could affect significant facts in digital advertising. The idea is to select challenging issues and beneficial opportunities when applying blockchain to digital marketing content.
XSepConv: extremely separated convolution for efficient deep networks with large kernels

Due to the increasing demand for deploying CNNs on resource-constrained platforms, efficient neural networks are becoming more and more popular, in which depthwise convolution plays an indispensable role. Recently, larger kernel sizes (&ge;5) have been applied to depthwise convolution, but with significantly increased computational cost and parameter size. In this paper, we propose a novel extremely separated convolutional block (XSepConv), which fuses spatially separable convolutions into depthwise convolution to substantially reduce both the computational cost and the parameter size induced by large kernels. We also propose an extra 2&times;2 depthwise convolution coupled with a new and improved symmetric padding strategy to compensate for the side effect brought by spatially separable convolutions. XSepConv is a more efficient alternative to vanilla depthwise convolution with large kernel sizes; moreover, extensive experiments on popular image classification and object detection benchmarks demonstrate that XSepConv can strike a better trade-off between accuracy and efficiency, and the improvement is more significant for larger kernels.
Multimodal Signals Subject Authentication System

The ever increasing number of e-services provided to users necessitates the efficient and accurate verification of user identity. In this work, we present a user authentication model based on multimodal signals. More specifically, we investigate the extraction of statistical features using various segment lengths of photoplethysmogram (PPG), electrocardiogram (ECG) and capnogram (CO2) signals and generate a feature vector using different fusion approaches. In addition, adding to the feature vector additional features extracted from the 1st derivative of the corresponding signal is explored. Finally, a classifier is used for identity authentication. The proposed authentication system achieved an average authentication accuracy of 99.76% using a 1 second segment length and a random forest classifier.
Troubling Care: Four Orientations for Wickedness in Design

Tensions in designing for care are often positioned as conflicts to be resolved. We draw upon queer theories to investigate caring for loved ones as not "in-line" with normative expectations of care as positive and fulfilling. Through the critique of two autobiographical design projects designed for informal, everyday care of our families, we describe four troubling orientations of care: willful detours, selfish shortcuts, naughty invasions, and unhappy departures. From these, we argue that tensions in care may not always be designed against, but can also be desired and generative. We conclude by discussing a "wickedness" in caring for loved ones that problematizes in-home technologies as attractively naughty and potentially violent, and the four orientations as resources for interaction designers to spatially navigate tensions of domestic care.
Aspect Extraction in Sentiment Analysis Based on Emotional Affect Using Supervised Approach

Aspect based sentiment analysis (ABSA) is regards as the most demanding area of research in the field of text mining and natural language processing. In the last decade, people tend to be more interested in consulting online media outlets to make decisions concerning products or events. However, the existing ABSA approaches alone are not sufficient in providing comprehensive information required to make an informed decision. Most of the early studies focused on the binary classification of “positive” and “negative” to predict an overall sentiment of a review, which reduced the effectiveness in decision making towards the entities. Even though earlier studies on SA are centered on aspect extraction, there are limited works that effectively combine aspect extraction with emotional affects such as surprise, sadness and happiness, which consequently leads to incomplete meaning of a content. In this paper, we investigate a problem involving aspects in relation to emotional affects. To propose a novel supervised approach for the aspect extraction with their associated emotion classes in sentiment analysis (SA), we conducted an analysis of some prominent ABSA approaches. This research work will lead to a more accurate results of SA and could assist both novice and prominent researchers in comprehending the relationship between aspects and emotions in SA considering its sematic role in business intelligence.
Palette: Towards Multi-source Model Selection and Ensemble for Reuse

The success of deep learning presents exciting opportunities for reusing pre-trained models from source domains to solve a target task with possibly limited training data. While various pre-trained models have been developed and become available, there is no principled way to select appropriate models for reuse. Although it is suggested that source and target tasks should be sufficiently similar, the calculation of task relevance usually requires extra storage of source training data and great efforts from domain experts, which is impractical in many applications. In this paper, we study the Multi-source Model Selection and Ensemble (MSMSE) problem. Given a collection of source models, we aim to select a subset of source models and develop an ensemble model that achieves the best performance for a target task. An ensemble of multiple models enables a boarder utilization of various underlying source knowledge and leads to better generalization ability. To this end, we present Palette, a generic framework that first selects potentially well-performed models from a source model pool, and then builds an ensemble with refinement. We introduce different model selection strategies that combine multi-armed bandits with adaptive resource allocation and Bayesian optimization techniques to accelerate the selection process. Extensive experimental results validate the effectiveness and efficiency of Palette and our model selection strategies.
Predict the Performance Analysis of Supervised Learning Techniques Using Heart Disease Database

In this work, Prediction of heart attack is very uncontrolled problem faced by doctor abnormally in every hospitals they have to make a killing review feedbacks from their patients. The ability of the sentiment analysis is needful work for machine to get results in the form of feedback i.e. positive or Negative feedback. Machine learning techniques assess necessary job in this area and in this zone of research work to construct a product this can assists the machine learning computations to input the option choice with respect to the fore cast and assess the expectation. Heart disease commonly occurred disease and it is the critical speculation for unexpectedly death these days. Nearest Neighbour (KNN) featureless is the most well-known, viable and productive calculation utilized for acknowledgment. In this paper, evaluate heart disease occurrences prediction by using machine learning strategies. Which will gives comfortable heterogeneous forecast model and it aid to discover best symptomatic for medicinal services framework for early prediction of symptomatic heart disease.
A study of different compression algorithms for multispectral images

Remote Sensing satellites acquire information about an area by analyzing the transmitted and reflected radio waves. The Earth Imaging satellites capture high resolution images which occupy more storage space on-board and consume a lot of bandwidth for downlink transmission. Multispectral sensors represent information of the images in multiple bands which are typically less than 15. The multispectral image compression algorithms aim to reduce the size of the images while preserving their quality. This paper involves a study of various algorithms used for compression of multispectral imagery.
State Estimation Model Reduction Through the Manifold Boundary Approximation Method

This paper presents a procedure for estimating the systems state when considerable Information and Communication Technology (ICT) component outages occur, leaving entire system areas unobservable. For this task, a novel method for analyzing system observability is proposed based on the Manifold Boundary Approximation Method (MBAM). By utilizing information geometry, MBAM analyzes boundaries of models in data space, thus detecting unidentifiable system parameters and states based on available data. This approach extends local, matrix-based methods to a global perspective, making it capable of detecting both structurally unidentifiable parameters as well as practically unidentifiable parameters (i.e., identifiable with low accuracy). Beyond partitioning identifiable/unidentifiable states, MBAM also reduces the model to remove reference to the unidentifiable state variables. To test this procedure, cyber-physical system (CPS) simulation environments are constructed by co-simulating the physical and cyber system layers.
Radar Maneuvering Target Tracking Based on LSTM Network

The nonlinear maneuvering target tracking problem is a state estimation problem in the case of system model mutation. The traditional multiple models method based on model switching has the practical problem of model mismatch, and the statistical accuracy is also limited. In this paper, a tracking scheme based on recurrent neural network structure is proposed. The implementation of this scheme is to extract conditional probability relations from a large number of training data through LSTM network, and apply it to continuous observation data, and finally get the state estimation results. Simulation results show that, compared with other common methods, this method can obtain more stable and accurate estimation effect in a shorter time, and is more anti-sensitive to target maneuvering.
Energy-Efficient and Adversarially Robust Machine Learning with Selective Dynamic Band Filtering

The popularity of neural networks is increasing day by day. Traditional machine learning solutions, such as image recognition, object detection, are being replaced by deep learning solutions because of their vigorous performance in computer vision. Despite their superior performance in these applications, neural networks are prone to adversarial attacks. The adversarial attack is the process of using adversarial samples as an input to the neural network which causes the network to misclassify, eventually degrading overall performance. Thus, it becomes very important to maintain their robustness by identifying, analyzing, and eliminating the cause of their vulnerability. In this paper, we introduce a technique to determine the most sensitive frequency band of input samples and filter the noise from this band to shield the network against adversarial attacks. First, we decompose the input sample into four different frequency components and then, identify the sensitive component by measuring the change in behavior of the pre-trained network on normal frequency band and that on frequency band with added noise (frequency band of an adversary). Next, we exploit this vulnerable component to assist the network in tackling the adversaries through noise filtering. Thereby, enhancing the neural networks? performance and defending against the adversarial attack. The low-frequency component was the most vulnerable and mitigating the noise from this band significantly improved the accuracy of Convolutional Neural Networks (CNN) along with that of state-of-art networks against adversarial attacks such as Fast Gradient Sign Method (FGSM), DeepFool (DF), and other techniques. The proposed technique showed performance enhancement from 85% to 95% classification accuracy for ResNet50.
The difficulty of being moral

A family of Markov blankets, if learned purely as a set of subsets of variables, may not be consistent with any Bayesian network. This inconsistency may have a negative impact on Markov blanket-based structure learning. In this paper, we propose checking Markov blanket consistency using graph morality. We define an alternative concept of moral graph – weak recursive simpliciality – without relying on Bayesian networks. Although it is NP-complete to decide if an undirected graph in general is moral, we propose linear and quadratic time algorithms for deciding morality for maximum degree 3 and 4 graphs respectively. In addition, we prove that the problem remains NP-complete for graphs with maximum degree higher than 4, hence there are no remaining unknown complexities for this kind of problem.
Graph NLU enabled question answering system

With a huge amount of information being stored as structured data, there is an increasing need for retrieving exact answers to questions from tables. Answering natural language questions on structured data usually involves semantic parsing of query to a machine understandable format which is then used to retrieve information from the database. Training semantic parsers for domain specific tasks is a tedious job and does not guarantee accurate results. In this paper, we used conversational analytics tool to create the user interface and to get the required entities and intents from the query thus avoiding the traditional semantic parsing approach. We then make use of Knowledge Graph for querying in structured data domain. Knowledge graphs can be easily leveraged for question answering systems, to use them as the database. We extract appropriate answers for different types of queries which have been illustrated in the Results section.
SISA: Securing Images by Selective Alteration

With an increase in mobile and camera devices' popularity, digital content in the form of images has increased drastically. As personal life is being continuously documented in pictures, the risk of losing it to eavesdroppers is a matter of grave concern. Secondary storage is the most preferred medium for the storage of personal and other images. Our work is concerned with the security of such images. While encryption is the best way to ensure image security, full encryption and decryption is a computationally-intensive process. Moreover, as cameras are getting better every day, image quality, and thus, the pixel density has increased considerably. The increased pixel density makes encryption and decryption more expensive. We, therefore, delve into selective encryption and selective blurring based on the region of interest. Instead of encrypting or blurring the entire photograph, we only encode selected regions of the image. We present a comparative analysis of the partial and full encryption of the photos. This kind of encoding will help us lower the encryption overhead without compromising security. The applications utilizing this technique will become more usable due to the reduction in the decryption time. Additionally, blurred images being more readable than encrypted ones, allowed us to define the level of security. We leverage the machine learning algorithms like Mask-RCNN (Region-based convolutional neural network) and YOLO (You Only Look Once) to select the region of interest. These algorithms have set new benchmarks for object recognition. We develop an end to end system to demonstrate our idea of selective encryption.
EM-based Radar Signal Processing and Tracking

Maximizing the achievable SNR benefits the measurement's precision that is affected by thermal noise, and track precision is enhanced given precise, unbiased, range measurements. While the radar is tracking the kinematic state (i.e., position, velocity, and acceleration) of the target, optimal signal processing requires knowledge of the target's signature, range rate, and radial acceleration. Increasing the SNR of individual measurements by enhanced signal processing results in reduced settling time for the track filter and less valuable radar resources required to achieve a specified track quality. Moving targets introduce range-walk (RW), which degrades coherent processing leading to a reduction in SNR and a blurring of the point target response in range-Doppler (RD), resulting in degraded range estimates to the Tracker. Traditional radar architectures require excessive iterations of the track loop with the associated measurements to achieve a specified track quality threshold. In this paper, the Expectation-Maximization (EM) algorithm, an iterative and inference approach, provides improved parameter estimation and SNR by employing a Kalman filter within the Signal Processor to account for changes in the target's kinematics. The improved state estimates are used in the Signal Processor to achieved enhanced RW correction.
A Comparison Study of Using Linear and Nonlinear Classifiers on Object Recognition Based on Inception Convolutional Neural Networks with Different Numbers of Inception-Block

This paper focuses on a comparison study of using linear and nonlinear classifiers on object recognition based upon inception convolutional neural networks (ICNNs) with different numbers of inception-block. Here, the linear and nonlinear classifiers respectively refer to linear support vector classifier (LSVC) and softmax ones. The ICNNs with different sizes of 3, 6, and 9 inception-block layers, namely 3-, 6- and 9 -ICNNs extract significant features; then, individually feed them to SM and LSVC for further classification. The ICNN-SM and ICNN-LSVC, with 2-auxiliary networks are experimented. 256-resized based on unequal-sized images of Oxford-17 and Oxford-102 flower datasets, respectively having 17 and 102 classes of flower species are tested here. Recognition evaluations rely on F1 scores and accuracy rates, averaged over 10-fold cross validation to ensure unbiased experimented results. The results indicate the best F1 and accuracy scores are mostly generated by 6-ICNN based on SM and LSVC classifiers for overall results. For Oxford-17 having smaller size of dataset and classes, SM yields 18.01%, 17.21% and 16.44%better performance than LSVC for 3-, 6- and 9 -ICNN respectively; whereas for Oxford-102, a bit few differences between those classifiers are exhibited.
Medical Image Denoising Using Two-Stage Iterative Down-Up CNN and SURF Features

Abstract This paper proposes a Two-Stage Deep Iterative Down-Up Convolutional Neural Network for denoising the medical images, which more often increase and decrease the feature map resolution. The function, Speeded Up Robust Features (SURF) is employed in the proposed system to handle the problem of gradient vanishing. The SURF is a patented local feature detector and descriptor. Experiment is conducted using several different medical noise images like Computed Tomography (CT) and Ultrasound Image and found that the proposed system outperforms the current state-of-the-art consistently in image denoising methodologies.
EOG Signal Compression Using Turning Point Algorithm

Electrooculography is one way to measure the electrical activity of the eyes. An electrooculogram (EOG) is the graphical representation of the electrical signal generated from eye movement. The different applications developed in the past decade based on these signals require a large amount of data to store and transmit, so compression is necessary. This paper presents a study conducted to compress EOGs using the turning point (TP) algorithm. For this purpose, electrical signals were acquired using a BlueGain EOG device, and the algorithm was implemented using MATLAB software. From this algorithm the signal was compressed and later reconstructed. The performance of the algorithm was analyzed using three parameters: compression ratio (CR), percent root-mean-square difference (PRD), and compression performance (CP). The experimental results, conducted over seven iterations of the algorithm, showed that the TP algorithm produced a fixed CR of 2:1 and a PRD of 2.9131 in its first iteration.
Object Tracking in Satellite Videos Based on Improved Correlation Filters

With the continuous progress of the society, video satellite has been paid more and more attention. As a new type of earth observation satellite, it can observe the certain area and obtain more and more dynamic information than traditional satellites. In this paper, we propose a novel algorithm based on the improved kernel correlation filters (KCF) to track the object in satellite videos. The improvements are as follows: 1) fusing the different features of the object, whose purpose is to describe the object information more effectively and reduce the impact of moving scene changes in object tracking and 2) proposing a motion position compensation algorithm through combines Kalman filter and motion trajectory. Its purpose is to improve the effectiveness of object tracking and avoid tracking error when used alone. What is more important is that it can also solve the problem of tracking failure when the object is partially or completely occluded and 3) extracting the local object region for normalized cross-correlation matching, and its function is to improve the success rate and accuracy of object tracking. The experimental results show that our algorithm can more effectively track the moving object in satellite video with high accuracy.
Correlated Wasserstein Autoencoder for Implicit Data Recommendation

Recommender systems for implicit data, e.g., browsing data, have attracted more and more research efforts. Most existing approaches assume the implicit data is i.i.d. which ignores the fact that the real-world data is generally correlated with each other. To cope with this issue, this paper proposes the correlated Wasserstein autoencoders (CWAEs) model to capture data correlation to enhance recommendation peformance. Particularly in the proposed approach, we first formulate correlated data via an undirected acyclic graph and then generalize the undirected acyclic graph to an acyclic graph by averaging all its' maximum acyclic subgraphs. To further enhance model performance, we introduce negative sampling strategy. Experiments are evaluated on Epinions dataset. The widely adopted evaluation criteria, i.e., CRR and NCRR, are adopted to evaluate both baseline models and our proposed approach. Experimental results have demonstrated the superiority of the proposed models.