Reviewerly/exHarmony huggingface dataset:
An Efficient Image Steganography Approach over Wireless Communication System

Abstract This paper presents a robust color image steganography approach for image communication over wireless communication systems. The objective of this approach is to hide three color images in one color cover image to increase the capacity of hiding as most previously published steganography approaches suffer from a capacity problem. Moreover, the investigation of wireless communication of steganography images is presented in this paper to study the sensitivity of extraction of hidden images to the channel degradation effects, which is not studied appropriately in the literature. The proposed approach depends on the Discrete Cosine and Discrete Wavelet transform. The cover image is first transformed to luminance and chrominance components for embedding the images to be hidden. The secret images are encrypted by chaotic Baker map, which is a good representative of the family of permutation-based algorithms, which tolerate the channel degradations better. The investigated wireless communication system is the Orthogonal Frequency Division Multiplexing system with channel equalization. The simulation results reveal the success of the proposed work for robust image communication.
Factors Affecting Software Development Productivity

The competitiveness has demanded from the software industry shorter delivery times for its products resulting in optimized life cycles, generating a need to increase its performance to maintain competitiveness in the markets where they operate. This context has made productivity study so fundamental that organizations not only evaluate their performance, but also provide means to improve it. The main goal of this paper is to investigate which factors affect productivity in software development projects and in open-source projects. In this work a Systematic Literature Review (SLR) was carried out in order to answer the research questions and a survey with practitioners community about their perception in relation to the factors of the productivity of the team. This empirical study led to the discovery of interesting factors that show how the different factors do (or do not) affect productivity. It was also found out that some factors appear to allow independence and responsibility of team, while others appear to cause a better distribution of tasks. The results show how factors such as people, product, organization, investment in technology, lack of contractual relations and engagement of open-source project contributors influence productivity.
Semantic segmentation of panoramic images using a synthetic dataset

Panoramic images have advantages in information capacity and scene stability due to their large field of view (FoV). In this paper, we propose a method to synthesize a new dataset of panoramic image. We managed to stitch the images taken from different directions into panoramic images, together with their labeled images, to yield the panoramic semantic segmentation dataset denominated as SYNTHIA-PANO. For the purpose of finding out the effect of using panoramic images as training dataset, we designed and performed a comprehensive set of experiments. Experimental results show that using panoramic images as training data is beneficial to the segmentation result. In addition, it has been shown that by using panoramic images with a 180 degree FoV as training data the model has better performance. Furthermore, the model trained with panoramic images also has a better capacity to resist the image distortion. Our codes and SYNTHIA-PANO dataset are available: https://github.com/Francis515/SYNTHIA-PANO.
Multi–Dimensional Wireless Signal Identification Based on Support Vector Machines

Radio air interface identification provides necessary information for dynamically and efficiently exploiting the wireless radio frequency spectrum. In this study, a general machine learning framework is proposed for Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), and Long Term Evolution (LTE) signal identification by utilizing the outputs of the spectral correlation function (SCF), fast Fourier Transform (FFT), auto-correlation function (ACF), and power spectral density (PSD) as the training inputs for the support vector machines (SVMs). In order to show the robustness and practicality of the proposed method, the performance of the classifier is investigated with respect to different fading channels by using simulation data. Various over-the-air real- world measurements are taken to show that wireless signals can be successfully distinguished from each other without any prior information while accounting for a comprehensive set of parameters such as different kernel types, number of in-phase/quadrature (I/Q) samples, training set size, or signal-to-noise ratio (SNR) values. Furthermore, the performance of the proposed classifier is compared to the existing well-known deep learning (DL) networks. The comparative performance of the proposed method is also quantified by classification confusion matrices and Precision/Recall/F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -scores. It is shown that the investigated system can be also utilized for spectrum sensing and its performance is also compared with that of cyclostationary feature detection spectrum sensing.
R³ Adversarial Network for Cross Model Face Recognition

In this paper, we raise a new problem, namely cross model face recognition (CMFR), which has considerable economic and social significance. The core of this problem is to make features extracted from different models comparable. However, the diversity, mainly caused by different application scenarios, frequent version updating, and all sorts of service platforms, obstructs interaction among different models and poses a great challenge. To solve this problem, from the perspective of Bayesian modelling, we propose R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Adversarial Network (R3AN) which consists of three paths: Reconstruction, Representation and Regression. We also introduce adversarial learning into the reconstruction path for better performance. Comprehensive experiments on public datasets demonstrate the feasibility of interaction among different models with the proposed framework. When updating the gallery, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> AN conducts the feature transformation nearly 10 times faster than ResNet-101. Meanwhile, the transformed feature distribution is very close to that of target model, and its error rate is incredibly reduced by approximately 75% compared with a naive transformation model. Furthermore, we show that face feature can be deciphered into original face image roughly by the reconstruction path, which may give valuable hints for improving the original face recognition models.
Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images

Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images.
Deep layer guided network for salient object detection

Recently salient object detection with convolutional neural networks has made great progress. More and more methods design more complex networks to integrate the features of each stage from backbone extractor. Considering that global information and spatial details of an image are better captured by features of deep layers and shallow layers of CNN respectively, deep layer guided network in which global information from deep layers is progressively transmitted to shallow layers in a guided manner is proposed. Hybrid feature enhancement block receives the feature maps of adjacent stages, and outputs enhanced feature maps which reduce the loss of spatial details and reduce the impact of varying in shape, scale and position of object. Discriminative feature block highlights the consistency in different stages from channel and spatial dimensions. Optimized discriminative features focus more on channels which show higher response to salient objects and positions consistent with the foreground in the saliency map from higher stage. Saliency inference block optimizes feature by merging with adjacent layer feature further. Thus feature becomes more enhanced, discriminative and refined in a high-to-low manner. Experimental results on five public datasets show that our salient object detection method reaches state-of-the-art performance under evaluation metrics.
Capacity of Single-Server Single-Message Private Information Retrieval with Private Coded Side Information

We study the problem of single-server single-message Private Information Retrieval with Private Coded Side Information (PIR-PCSI). In this problem, there is a server that stores a database, and a user who knows a random linear combination of a random subset of messages in the database. The number of messages contributing to the user's side information is known to the server a priori, whereas the indices and the coefficients of these messages are unknown to the server a priori. The user wants to retrieve a message from the server, while protecting the identities of both the demand message and the side information messages. Depending on whether the demand is part of the coded side information or not, we consider two different models for the problem. For the model in which the demand does not contribute to the side information, we prove a lower bound on the minimum download cost for all (linear and non-linear) PIR schemes; and for the model wherein the demand is one of the messages contributing to the side information, we prove a lower bound for all scalar-linear PIR protocols. In addition, we propose novel PIR protocols that achieve these lower bounds.
Automatic Generation of Game Content using a Graph-based Wave Function Collapse Algorithm

This paper describes graph-based Wave Function Collapse algorithm for procedural content generation. The goal of this system is to enable a game designer to procedurally create key content elements in the game level through simple association rule input. To do this, we propose a graph-based data structure that can be easily integrated with a navigation mesh data structure in a three-dimensional world. With our system, if the user inputs the minimum association rule, it is possible to effectively perform procedural content generation in the three-dimensional world. The experimental results show that the Wave Function Collapse algorithm, which is a texture synthesis algorithm, can be extended to a non-grid shape with high controllability and scalability.
Multi-criterion mammographic risk analysis supported with multi-label fuzzy-rough feature selection

Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential robust solution.Computer aided risk analysis of breast cancer typically works with a set of extracted mammographic features which may contain significant redundancy and noise, thereby requiring technical developments to improve runtime performance in both computational efficiency and classification accuracy.Use of advanced feature selection methods based on multiple diagnosis criteria may lead to improved results for mammographic risk analysis.An approach for multi-criterion based mammographic risk analysis is proposed, by adapting the recently developed multi-label fuzzy-rough feature selection mechanism.A system for multi-criterion mammographic risk analysis is implemented with the aid of multi-label fuzzy-rough feature selection and its performance is positively verified experimentally, in comparison with representative popular mechanisms.The novel approach for mammographic risk analysis based on multiple criteria helps improve classification accuracy using selected informative features, without suffering from the redundancy caused by such complex criteria, with the implemented system demonstrating practical efficacy.
Blockchain Technology Use Cases

Blockchain recently received an increasing attention from the industrial and research fields as well as a wide coverage from the media, as it enables the creation of a shared (worldwide) ledger maintained on a peer-to-peer basis, where autonomous programs (the smart contracts) can be executed. From the technological point of view, the blockchain is still far from a mainstream adoption, mainly due to scalability issues and because the interaction with it is still complex and requires technical skills. Given blockchain’s (and smart contracts’) advantages and technical limitations, it is difficult, for a company, to decide whether to invest in this technology or not, and to identify potential successful use cases. The objective of this chapter is to provide the reader with practical information to perform this choice. In particular, the chapter first provides a glossary of blockchain-related terms and then shows existing and under development use cases belonging to different sectors. Presented use cases are then discussed, to identify which sectors could benefit the most from blockchain and smart contracts, highlighting the most promising use cases in which this technology could be disrupting, and underlying the ones where advantages of relying on it are only marginal.
Cascaded Face Sketch Synthesis Under Various Illuminations

Face sketch synthesis from a photo is of significant importance in digital entertainment. An intelligent face sketch synthesis system requires a strong robustness to lighting variations. Under uncontrolled lighting conditions in real-world settings, such a system will perform consistently well and have little restriction on the lighting conditions. However, previous face sketch synthesis methods tend to synthesize sketches under well-controlled lighting conditions. These methods are sensitive to lighting variations and produce unsatisfactory results when the lighting condition varies. In this paper, we propose a novel cascaded face sketch synthesis framework composed of a multiple feature generator and a cascaded low-rank representation. The multiple feature generator not only produces a generated sketch feature consistent with an artist's drawing style but also extracts a photo feature that is robust to various illuminations. Both features ensure that given a photo patch, the optimal sketch candidates can be selected from the database. The cascaded low-rank representation enables a gradual reduction in the gap between the synthesized face sketch and the corresponding artistdrawn sketch. Experimental results illustrate that the proposed cascaded framework generates realistic sketches on par with the current methods on the Chinese University of Hong Kong face sketch database under well-controlled illuminations. Moreover, this framework exhibits greatly improved performance compared to these methods on the extended Chinese University of Hong Kong face sketch database and Chinese celebrity face photos from the web under different illuminations. We argue that this framework paves a novel way for the implementation of computer-aided optical systems that are of essential importance in both face sketch synthesis and optical imaging.
Proactive mental fatigue detection of traffic control operators using bagged trees and gaze-bin analysis

Most of existing eye movement-based fatigue detectors utilize statistical analysis of fixations, saccades, and blinks as inputs. Nevertheless, these parameters require long recording time and heavily depend on eye trackers. In an effort to facilitate proactive detection of mental fatigue, we introduced a complemental fatigue indicator, named gaze-bin analysis, which simply presents the eye-tracking data with histograms. A method which engaged the gaze-bin analysis as inputs of semisupervised bagged trees was developed. A case study in a vessel traffic service center demonstrated that this approach can alleviate the burden of manual labeling as well as improve the performance of fatigue detection model. In addition, the results show that the approach can achieve an excellent accuracy of 89%, which outperformed other methods. In general, this study provided a complemental indicator for detecting mental fatigue as well as enabled the application of a low sampling rate eye tracker in the traffic control center.
Designing an Emotionally Realistic Chatbot Framework to Enhance Its Believability with AIML and Information States

Chatbot program has been empirically proven help to improve the engagement with users. Moreover, the implementation of a chat-bot program in the industry helps the company to reduce their operational costs in engaging with their customers and employees. There are still quite a number of problems existed in order to build a human-like chatbot program. Understanding a natural conversation and replying back to the conversation the interlocutors, keeping the conversation flowing naturally is a cumbersome task for a computer. This research aims to design an emotionally realistic chatbot system to enhance the believability of the chatbot using Artificial Intelligence Markup Language (AIML) and Information State. The results show that there is a statistically significant improvement to the chatbot believability in the system that has emotions variables induced compare to the one without emotions. Moreover, 63,33% of the respondents perceived Aero and Iris as two different individuals. The future work of this research is to deploy and have an exploration of the chatbot system to other cases.
A Lightweight Blockchain Based Cybersecurity for IoT environments

Recently, Blockchain (BC) attracts a lot of research's to study their salient features in order to adopt it into various domains. BC promise to address security and privacy preservation issues in a distributed manner and eliminating the centralized concept. However, Internet of Thing (IoT) environments is constricted resources with limited capabilities in terms of; computation, storage, and energy that deter to adopt BC in a straightforward manner due to its intensive computational requirements and bandwidth consumption overhead. To address those issues, this paper introduces a Lightweight Blockchain based Cybersecurity (LBC) for IoT environments. Unlike the Proof of Work (PoS) and Proof of Stake (PoS) in Bitcoins, we aim to mitigate the heavy computational cost that required in a consensus algorithm to meet the IoT requirements. To provide scalability feature to the proposed scheme, Edge Block Manager (EBM) and Aggregation Block Managers (ABM) have been introduced; where EBM aims to overcome the limited capabilities of local IoT resources and manages local BC in centralized manner. While ABM constitute of numerous of EBM to manages the public BC in a distributed manner. Our novel proposed scheme can preserve high throughput and low latency. Obviously, waiting period, verification, and block appending period have been mitigated. Security analysis shows that the proposed scheme is resisted against typical attacks.
Extended Dissipativity and Control Synthesis of Interval Type-2 Fuzzy Systems via Line-Integral Lyapunov Function

This article addresses the problems of the stability and extended dissipativity analysis and control synthesis for interval type-2 fuzzy systems. A sufficient condition of asymptotic stability and extended dissipativity of the systems under consideration is established by using line-integral Lyapunov function. This condition obtained is more general than the one which is based on quadratic Lyapunov function. Calculating the control gains in light of the obtained condition is more complicated and challenging, since the matrix inequalities in the condition are of nonlinear form with respect to some matrix variables. Also, change of variable as well as the existing matrix decoupling approach cannot be used directly to handle the nonlinear problem. By utilizing cone complementarity linearization algorithm and a new matrix decoupling method, the state feedback controller can be developed by converting the nonlinear matrix inequalities into a quadratic optimization problem with linear inequality constraints. Four simulation examples are provided to show the effectiveness of the proposed approach.
Convolutional Neural Network Architecture Design by the Tree Growth Algorithm Framework

This paper presents tree growth algorithm framework for designing convolutional neural network architecture. Convolutional neural networks are a special class of deep neural networks that typically consist of several convolution, pooling and fully connected layers. Convolutional neural networks have proved to be a robust method for tackling various image classification tasks. One of the most important challenges from this domain is to find the network architecture that has the best performance for the specific application. The performance of the network depends on the set of hyper-parameter values such as the number of convolutional and dense layers, the number of kernels per layer and kernel size. Optimization of hyperparameters was performed by novel tree growth algorithm that belongs to the group of swarm intelligence metaheuristics. The robustness, performance and solutions quality of the proposed framework was validated against the well-known MNIST dataset. Conducted comparative analysis demonstrated that the proposed frameworks obtains promising results in this domain.