Reviewerly/exHarmony huggingface dataset:
A Contextual Semantic-Based Approach for Domain-Centric Lexicon Expansion

This paper presents a contextual semantic-based approach for expansion of an initial lexicon containing domain-centric seed words. Starting with a small lexicon containing some domain-centric seed words, the proposed approach models text corpus as a weighted word-graph, where the initial weight of a node (word) represents the contextual semantic-based association between the node and the target domain, and the weight of an edge represents the co-occurrence frequency of the respective nodes. The semantic-based association between a node and the target domain is calculated as a function of three contextual semantic-based association metrics. Thereafter, a random walk-based modified PageRank algorithm is applied on the weighted graph to rank and select the most relevant terms for domain-centric lexicon expansion. The proposed approach is evaluated over five datasets, and found to perform significantly better than three baselines and three state-of-the-art approaches.
Performances Improvement of Chaos-Based Robust Digital Image Watermarking using Advanced Techniques

This paper presents a research study leading to a robust chaos-based scheme for digital image watermarking applications in the transform domain. The steps undergone include combining, in a repeating process, three performant conventional mathematical transforms of Direct Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and variants with three different state-of-the-art advanced techniques namely Discrete Curvelet Transform (DCuT), Discrete Radon Transform (DRaT) and Finite Ridgelet Transform (FRT). Results have shown a neat improvements in watermarking imperceptibility and robustness each time especially after including Singular Value Decomposition (SVD) which has enhanced embedding capability as well. Methods evaluation has followed an exhaustive series of performed tests that applied a range of evaluation metrics for imperceptibility. In addition, Robustness of the final scheme has also been confirmed by applying a variety of attacks on the resulting images. Finally, security aspect has also been improved after a comparative implementations of popular four chaotic models namely Arnold Cat Map (ACM), Lorenz Cat Map (LCM), Rossler Cat Map (RCM) and Genesio-Tesi Cat Map (GTCM) respectively producing secure images encryption levels. All Implementations have been validated using a sufficent number of color image from a large dataset.
Dual Image-Based Dictionary Encoded Data Hiding in Spatial Domain

In the modern digital era, the privacy of personal communication is a serious concern to all netizens. A better way to preserve privacy could be to hide the secret message inside some innocent looking digital object such as image, audio, video, etc., which is known as steganography. A new steganographic scheme using a reference image along with the cover image has been proposed in this article. It enhances the robustness and security by increasing the obscurity of the hidden message. It also employs an additional dictionary-based encoding module to increase the hiding capacity as well as security. Experiments show that bit changes in the reference image are very few and undetectable to human perception, it also evades common statistical tests. Evaluation of standard quality parameters such as MSE, PSNR, UIQI, SSIM along with chi-squared statistics based embedding probability testing has been performed. When dictionary-based encoding is applied it further improves the quality parameters.
Multi-Task Learning Based End-to-End Speaker Recognition

Recently, there has been an increasing interest in end-to-end speaker recognition that directly take raw speech waveform as input without any hand-crafted features such as FBANK and MFCC. SincNet is a recently developed novel convolutional neural network (CNN) architecture in which the filters in the first convolutional layer are set to band-pass filters (sinc functions). Experiments show that SincNet achieves a significant decrease in frame error rate (FER) than traditional CNNs and DNNs.
Connectivity! Connectivity! Connectivity! May You Be More Connected Than Ever!!

Presents the introductory editorial for this issue of the publication.
Identification of Plasmodium falciparum Stages Using Support Vector Machine Method

One of the health problems of global concern is Malaria. Malaria is still a concern of the World Health Organization (WHO). Malaria is a deadly disease caused by Plasmodium parasites. Plasmodium carried by the Anopheles mosquito infects human red blood cells. Image processing techniques are applied to identify malaria parasites in human blood through observed microscopic thin blood smear images. This study aimed to develop a system that can identify the stages of Plasmodium falciparum malaria parasite, i.e Trophozoite and Skizon by using the GLCM (Gray Level Co-occurrence Matrix) was to analyze parasite texture and morphological feature extraction was to analyse morphology of parasites. Additionally, we also use A Support Vector Machine (SVM) to classify all extracted features. The finding result by using SVM has achieved 91,67% accuracy for detecting the two stages of P. falciparum.
Multimodal Medical Images Registration Using Biogeography-Based Optimization Algorithm

Image registration plays an important role in medical analysis. Nowadays, it constitutes a very active research field due to the great improvement of the medical imaging technology. Metaheuristics based algorithms such as Genetic Algorithm (GA) , Particle Swarm Optimization (PSO) , Artificial Bee Colony (ABC) and Differential Evolution (DE) have been widely applied to medical image registration. Due to the success of the Biogeography-based Optimization (BBO) in the global optimization problems, we present in this paper an improved method for medical image registration using the Positron Emission Tomography (PET) and Magnetic Resonance (MR) modalities. Experimental results demonstrate that the proposed approach achieves high-precision and robust registration compared to the PSO algorithm.
Zero-Knowledge Proof System for Fully Anonymous Attribute Based Group Signatures from Lattices with VLR

Signature schemes with Verifier-Local Revocation (VLR) fail to achieve stronger anonymity notion, full-anonymity. In full-anonymity, it is free to corrupt the secret signing keys. Secret signing keys of VLR schemes consist of tokens which can be used to identify the users. Thus VLR schemes restrict corrupting secret signing keys. VLR schemes can achieve full-anonymity by separating tokens from secret signing keys. However, separation of tokens gives space to signers to replace tokens with fake values. Generating signatures with fake tokens can be prevented with a suitable proof system. This paper proposes a new zero-knowledge protocol to support provers to convince verifiers, that attributes used for creating the signature are valid and have naive tokens. Moreover, this paper offers a new Attribute-Based Group Signature (ABGS) scheme, that uses the proposed protocol and achieves full anonymity.
HCC Recognition Within Ultrasound Images Employing Advanced Textural Features with Deep Learning Techniques

HCC (Hepatocellular carcinoma) is the liver cancer form that is the most often met. The actual standard for a highly accurate diagnosis of HCC is the biopsy, this being, however, invasive, as well as dangerous. We develop computerized methods, based on advanced image processing, in order to perform automatic, respectively computer aided diagnosis of HCC based on ultrasound images. In this research paper, we analyze the role of several advanced texture analysis specific methods, combined with powerful conventional classifiers, respectively deep learning techniques, for HCC automatic recognition. Regarding the deep learning methods, we focus on Stacked Denoising Autoencoders (SAE), so we study its impact upon the performance of HCC computerized diagnosis. In our experiments, we compare the HCC tumor with both the cirrhotic liver parenchyma on which it evolved and with the hemangioma tumor, the obtained accuracy being around 85%. The actual results are compared, as well, with the previously obtained results in the literature.
Image Splicing Localization Using Superpixel Segmentation and Noise Level Estimation

With the development of computer and artificial intelligence technology, the authenticity of digital images has been seriously challenged. How to judge the authenticity of digital images has become an important research direction. At present, splicing is one of the most common image tampered methods. Different sources of images have different noise levels, and this paper proposes a scheme to locate the image splicing areas by detecting the inconsistency of the local noise level of the image. Firstly, the image to be detected is segmented into pixel blocks with similar features by using SLIC (Simple Linear Iterative Clustering) superpixel segmentation algorithm. Secondly, the noise level estimation method based on PCA (Principal Component Analysis) is used to calculate the local noise level of each image block. Finally, using three clustering algorithms to cluster the results of the estimated noise level, and the splicing areas of the image is located according to the clustering results. The results show that the proposed scheme in this paper can effectively locate the splicing areas and retain more edge information of the detected areas.
New Non-Binary Stabilizer Quantum Codes Derived from Quasi-Negacyclic Codes

Constructions of quantum error-correcting codes with good properties utilizing classical codes is an important task for quantum computing and quantum information. In this paper, we employ a class of one-generator quasi-negacyclic codes and provide a sufficient condition for symplectic self-orthogonality. As the computational results, some good non-binary stabilizer quantum codes are constructed and their parameters are determined.
ViSSa: Recognizing the appropriateness of videos on social media with on-demand crowdsourcing

The recent significant growth of social media has brought the attention of researchers toward monitoring the enormous amount of streaming data using real-time approaches. This data may appear in different forms like streaming text, images, audio, videos, etc. In this paper, we address the problem of deciding the appropriateness of streaming videos with the help of on-demand crowdsourcing. We propose a novel crowd-powered model ViSSa, which is an open crowdsourcing platform that helps to automatically detect appropriateness of the videos getting uploaded online through employing the viewers of existing videos. The proposed model presents a unique approach of not only identifying unsafe videos but also detecting the portion of inappropriateness (in terms of platform’s vulnerabilities). Our experiments with 47 crowd contributors demonstrate the effectiveness of the proposed approach. On the designed ViSSa platform, 18 safe videos are initially posted. After getting access, 20 new videos are added by different users. These videos are assessed (and marked as safe or unsafe) by users and finally with judgment analysis a consensus judgment is obtained. The approach detects the unsafe videos with high accuracy (95%) and point out the portion of inappropriateness. Interestingly, changing the mode of video segment allocation (homogeneous and heterogeneous) is found to have a significant impact on the viewers’ feedback. However, the proposed approach performs consistently well in different modes of viewing (with varying diversity of opinions), and with any arbitrary video size and type. The users are found to be motivated by their sense of responsibility. This paper also highlights the importance of identifying spammers through such models.
A New Model in Firefighting Theory

Continuous and discrete models [1, 5] for firefighting problems are well-studied in Theoretical Computer Science. We introduce a new, discrete, and more general framework based on a hexagonal cell graph to study firefighting problems in varied terrains. We present three different firefighting problems in the context of this model; for two of which, we provide efficient polynomial time algorithms and for the third, we show NP-completeness. We also discuss possible extensions of the model and their implications on the computational complexity.
Morphology Reconstruction of Obstructed Coronary Artery in Angiographic Images

In cardiac arterial interventional therapy, coronary angiograms provides key information to physicians for treatment strategy selection. However, it is hard to extract the information about arteries with CTO lesion in absence of contrast opacification and visualization of the CTO coronary arteries in angiograms. In this paper, we present an algorithm, which can predict the extension direction of artery with CTO and reconstruct the morphology of blocked artery in coronary angiograms. First, our algorithm segment the CTO artery angiogram and extract the artery skeleton. Second, an iterative approach is preformed to reconstruct the skeleton of blocked artery. Finally, our algorithm generates the simulated postoperative angiogram according to the skeleton image. The results demonstrate that automatic morphology reconstructing of CTO coronary artery with high accuracy and reliability is feasible.
Would the Patch Be Quickly Merged?

Code review is one of the most time-consuming and costly activities in modern software development. For the code submissions that can not be accepted by reviewers, developers need to re-modify the code again. Developers desire to minimize the time-cost that spends in the code review process. In some cases, a submission might be submitted many times and still not be accepted. The number of review times has serious implications for defect repairs and the progress of development. Therefore, a few recent studies focused on discussing factors that effect submission acceptance, while these prior studies did not try to predict submission acceptance or the number of review times. In this paper, we propose a novel method to predict the time-cost in code review before a submission is accepted. Our approach uses a number of features, including review meta-features, code modifying features and code coupling features, to better reflect code changes and review process. To examine the benefits of our method, we perform experiments on two large open source projects, namely Eclipse and OpenDaylight. Our results show that the proposed approach in the problem of predicting submission acceptance achieves an accuracy of 79.72%, 80.03% for Eclipse and OpenDaylight, respectively. For the prediction of review times ranges, our method achieves an accuracy of 66.42% and 60.42% for Eclipse and OpenDaylight, respectively.
Rubik’s Cube Encryption for Securing Cloud Stored Data

Cloud storage is an efficient solution for storage needs of organizations and individuals. But the data outsourcing and data transfer using the same network makes the cloud data more sensitive for attacks. Hence providing security to the cloud data while in storage or transit requires a prime concern. The denial of access to a third party also must be ensured for the confidentiality of stored data. In this paper, Rubik’s cube based encryption with data spitting technique is introduced to securely storing and transferring cloud stored medical records. And the role of the third party auditor also limited to verifying the user only. The multiple number of encryption and denial of access to the third party ensure that the data is protected in the cloud.
Nurses’ work practices in design: managing the complexity of pain

Purpose The purpose of this study is to understand the activities in nurses’ work practices in relation to the design process of a self-monitoring application. Design/methodology/approach A design ethnographic approach was applied in this study. Findings To solve the problem of translating highly qualitative phenomena, such as pain, into the particular abstract features of a self-monitoring application, design participants had to balance these two aspects by managing complexity. In turn, the nurses’ work practices have changed because it now involves a new activity based on a different logic than the nurses’ traditional work practices. Originality/value This study describes a new activity included in nurses’ work practices when the nurses became part of a design process. This study introduces a novel way on how to gain a deeper understanding of existing professional practice through a detailed study of activities taking place in a design process. This study explores the possible implications for nurses’ professional practices when they participate in a self-monitoring application design process.
The models and structure of neural networks

Neural networks are a kind of artificial intelligence machine learning technology which simulates the human brain. They are the basis of deep learning, the hottest research direction at present. The neural network in the human brain is a very complex organization. There are an estimated 100 billion neurons in the adult brain. Learning neural network can not only master a powerful machine learning method, but also better understand deep learning technology. This chapter introduces some typical neural network structures, including the ridgelet neural network, contourlet neural network, convolutional neural network, recurrent artificial neural network, generative adversarial nets, autoencoder, and restricted Boltzmann machine and deep belief network.
A Novel Strategy to Adjust Feature Weights for Data Classification

Most feature weighted algorithms do not take into account the correlation between features, so the redundancy with interference has a negative impact on the final classification results. In order to solve this problem, a feature weighting strategy is proposed considering correlation between features based on mutual information. The strategy introduces the correlation coefficient of the inter feature as an index to measure the redundancy, and calculates the penalty factor of each feature to deal with the corresponding weight gained by mutual information, which reflects the real contribution of the feature to the classification as much as possible. Compared with several different algorithms on several datasets, the new strategy has better robustness and generalization ability.
A Potential Analysis of Cognitive Assistance Systems in Production Areas

A cognitive assistance system supports the person in information gathering and preparation, decision-making, as well as performing work tasks and coordinating people, machines, processes and products [1]. At present, a large number of cyber physical systems are coming onto the market, which also find their way into today’s factories. This paper discusses which technologies are interesting today for research and companies. A short study was used to reflect the current use of cognitive assistance systems in the production environment and to determine the prospects for the future.
Sentence-Level Sign Language Recognition Using RF signals

Sign language recognition is emerging as a vital component of our smart life. In addition, commercial RFID shall become a popular technology for sign language recognition. As we all know, there are 70 million deaf people using sign language as their first language and sign language can facilitate communication with deaf people. However, most of the researches are isolated word recognition. There is few researches about sentence-level sign language recognition. More importantly, they are limited and it is difficult to achieve the desired results of realworld applications. So this paper introduces the first sentence-level sign language recognition system based on RFID. It mainly collects the phase sequence of signals received by commercial RFID device. We obtain relatively pure phase characteristics and present a method to carry out sign language segmentation. Effective feature extraction and classifier selection are crucial to sign language recognition. By evaluating our system in real-word environment, we fill in the gaps between corresponding low-cost sentence-level sign language recognition. We implement and evaluate through extensive experiments and the average accuracy of the method are 96% and 98.11% in different multipath scenarios. The results show that our method has high recognition accuracy and robustness.
Multi-Scale Deep Convolutional Nets with Attention Model and Conditional Random Fields for Semantic Image Segmentation

Although Convolutional Neural Networks are effective visual models that generate hierarchies of features, there still exist some shortcomings in the application of Deep Convolutional Neural Networks to semantic image segmentation. In this work, our algorithm incorporates multi-scale atrous convolution, attention model and Conditional Random Fields to tackle this problem. Firstly, our method replaces deconvolutional layers with atrous convolutional layers to avoid reducing feature resolution when the Deep Convolutional Neural Networks is employed in a fully convolutional fashion. Secondly, multi-scale architecture and attention model are used to extract the existence of features at multiple scales. Thirdly, we use Conditional Random Fields to prevent the built-in invariance of Deep Convolutional Neural Networks reducing localization accuracy. Moreover, our network completely integrates Conditional Random Fields modelling with Deep Convolutional Neural Networks, making it possible to train the deep network end-to-end. In this paper, our method is used to the matters of semantic image segmentation and is demonstrated the effectiveness of our model with experiments on PASCAL VOC 2012.
System Analysis of the Implementation of the Virtual Forms Human-computer Interaction in the Higher Education Sector

Virtual forms of interactions within educational activities in the information society make it possible to combine the resources of different organizations and support the communications between universities from remote regions with the federal center at minimal cost. Combining resources of a number of universities makes it possible to fully employ the capacity of higher education sector, to debug multichannel mechanisms of direct and indirect communication in the educational environment, and to meet the challenge of the methodological support of the new forms of education. In this study, the education is considered as a form of intangible production, the employment of the resources of higher education sector in an information-intensive, cognitive activities. It is significant that the involvement of the complementary resources of higher education brings about a number of synergies, while optimizing resource-use, providing communications that are important for coordination the advanced training in business sector. The article proposes a conceptual approach to the development of a virtual consortium system as a new form of communications in higher-education sector, analyzes the forms of its implementation are, and emphasize the differences between a virtual and distance learning.