Reviewerly/exHarmony huggingface dataset:
Text Steganalysis Based on Capsule Network with Dynamic Routing

With the growth of natural language processing technology, coverless text steganography has attracted the attention of a large number of researchers. Most existing text steganalysis methods are based on traditional neural network to extract and analyze the semantic features of automatically generated steganographic text. However, due to the limitation of traditional neural networks to preserve subtle features, these methods cannot obtain satisfactory results when detecting the differences between steganographic text with low embedding rate and natural text. This paper demonstrates that using a capsule network to detect whether the natural text contains secret information and gets robust and accurate performance. The capsule network extracts and preserves the sematic features of text, analyzes the subtle differences between steganographic text and natural text. To strengthen the generalization of the method, we choose word2vec to vectorize text and use steganographic text generated based on RNN and variable-length coding as the data set for experiments. Experimental results show that detection accuracy of our method can achieve 92% in steganographic text with the low embedding rate (1–3 bit/word), which is about 7% higher than that based on other neural networks; in high embedding rate (4–5 bit/word), the detection accuracy can reach more than 94%.
Joint multilabel classification and feature selection based on deep canonical correlation analysis

Summary In recent years, multilabel learning has been applied to a lot of application areas and is yet a challenging task. In multilabel learning, an instance often belongs to multiple class labels simultaneously. The labels usually have correlations with others, and mining label correlations is helpful to enhance the multilabel classification performance. Aiming at increasing the accuracy of prediction, Label embedding (LE) is an important technique, and conducive to extracting label information for multilabel learning. In this paper, we present a novel multilabel learning approach via exploiting label correlations, which can be naturally extended to tackle feature selection problem. First, to obtain the discriminative features shared by all labels, the proposed algorithm learns a latent space by employing deep canonical correlation analysis. Then we exploit label correlations by enforcing predictions on similar labels to be similar, thereby improving the prediction performance. Results on several multiple datasets illustrate that the proposed algorithm has the advantages on multilabel classification and feature selection.
Challenges and Issues in Risk Assessment in Modern Maritime Systems

In this paper, we present challenges and risks concerning cyber security in a supply chain environment. In particular, we focus on the MITIGATE (Multidimensional, IntegraTed, rIsk assessment framework and dynamic, collaborative risk manaGement tools for critical information infrAstrucTrurEs) Supply Chain Risk Assessment methodology, which is in compliance with ISO28001 and can be applied in order to assess the security risks of all the organizations involved in a supply chain. To validate the MITIGATE approach, we provide use cases based on real-life maritime scenarios and real-world data collection. To this end, a number of best practices in the form of guidelines for a successful application of the MITIGATE risk management system in supply chain environments are presented. The main advantages of the Mitigate Risk Assessment approach over existing maritime initiatives and efforts are also highlighted.
Saliency Detection for Hyperspectral Images via Sparse-Non Negative-Matrix-Factorization and novel Distance Measures

Saliency detection is a very active area in computer vision. When hyperspectral images are analyzed, a big amount of data need to be processed. Hence, dimensionality reduction techniques are used to highlight salient pixels allowing us to neglect redundant features. We propose a bottom-up approach based on two main ingredients: sparse non negative matrix factorization (SNMF) and spatial and spectral distances between the input image and the reconstructed one. In particular, we use both well known and novel distance functions. The method is validated on both hyperspectral and multispectral images.
An Extensive Survey on Consensus Mechanisms for Blockchain Technology

Blockchain technology is a cryptographic technique that enables users to maintain their data in a decentral way with a non-editable form without an arbitrator. Originally motivated from the underlying platform of bitcoin cryptocurrency, researchers found blockchain to be very useful in many application domains, including smart contracts, insurance, banking, finance, and many other sectors. As multiple stakeholders try to add a block (containing the data or transactions) into the chain, there is a need for a mechanism to come to an agreement on which stakeholder’s block should be added. Such a mechanism is known as consensus mechanism which is a primary factor of significance because it decides the correctness of data to be added as well as trustworthiness of the node which is attempting to add the block. An efficient consensus mechanism achieves security, higher precision, and better performance. In this research, we aim to study various consensus mechanisms in blockchain technology with their strengths and challenges. We discuss factors affecting the consensus mechanism and provide our suggestions for drafting an effective consensus mechanism.
Blockchain-based Data Traceability Platform Architecture for Supply Chain Management

With the rapid development of economic globalization, cooperation between countries, between enterprises, has become a key factor whether country and enterprises can make great economic progress. In these cooperation processes, it is necessary to trace the source of business data or log data for auditing and accountability. However, multi-party enterprises participating in cooperation often do not trust each other, and the separate accounting of the enterprises leads to isolated islands of information, which makes it difficult to trace the entire life cycle of the data. Therefore, there is an urgent need for a mechanism that can establish distributed trustworthiness among multiparty organizations that do not trust each other, and provide a tamper-resistant data storage mechanism to achieve credible traceability of data. This work proposes a data traceability platform architecture design plan for supply chain management based on the multi-disciplinary knowledge and technology of the Fabric Alliance chain architecture, perceptual identification technology, and cryptographic knowledge. At the end of the paper, the characteristics and shortcomings of data traceability of this scheme are evaluated.
CaIAuth: Context-Aware Implicit Authentication When the Screen Is Awake

Relieving users from the burden of remembering and inputting authentication information explicitly, such as passwords/PINs and lock patterns, implicit authentication mechanisms have gained an increasing concern. When providing authentication, the existing implicit methods only depend on a specific behavior, such as typing on the screen, performing gestures, or taking a walk. However, in real applications, a user's behavioral characteristics are also decided by the context where behavior is performed. Thus, those existing methods show limited authentication accuracy and usability. To address these issues, we propose CaIAuth, a reliable context-aware implicit authentication framework, which profiles users' behavior and context characteristics in a holistic fashion. It observes the states of context-sensing entities for different smartphone usage patterns and builds a context-aware model to distinguish between legitimate users and illegal ones. We conducted extensive experiments to evaluate system performance with a large data set collected from 142 subjects. The experimental results show that our system achieves a low equal error rate (EER) (e.g., less 7%) and is resilient against common threats, including zero-effect attack and mimicry attack. In addition, CaIAuth achieves a low authentication delay and overhead.
Possibilities of application of augmented reality in different branches of education

Augmented reality has a great impact on the student in the presentation of educational material: objects of augmented reality affect the development of facial expressions, attention, stimulate thinking, and increase the level of understanding of information. Its implementation in various spheres has indisputable advantages: realism, clarity, application in many industries, information completeness and interactivity. That is why the study presents the possibilities of using augmented reality in the study of mathematics, anatomy, physics, chemistry, architecture, as well as in other fields. The comparison of domestic and foreign proposals for augmented reality is presented. The use of augmented reality in various fields (technology, entertainment, science and medicine, education, games, etc.) should be well thought out and pedagogically appropriate. That is why in the future it is planned to conduct research on the feasibility of using augmented reality and to develop elements of augmented reality accordingly.
Facial Ethnicity Recognition Based on Transfer Learning from Deep Convolutional Networks

With the development of deep learning, computer face recognition has made significant progress. However, face ethnic characteristics information is rarely used in face recognition technology. The research of facial ethnicity recognition had not only been directly applied in daily life, but also avoided racial effects and improved model generalization performance. In the paper, we proposed a Chinese facial ethnicity recognition (CFER) model based on transfer learning from deep convolution networks. First, we collected 5 Chinese ethnic groups to build a face dataset containing ethnicity information; then we have applied CFER to recognize Chinese ethnicity characteristics and 10-fold cross validation method to estimate mainly the accuracy rate of the model. The average recognition rate of the model is 80.5%, meanwhile, the model also has good generalization performance. It's proved that deep learning method is feasible for facial ethnicity recognition.
Using Remote Controlled Speech Agents to Explore Music Experience in Context

It can be difficult for user researchers to explore how people might interact with interactive systems in everyday contexts; time and space limitations make it hard to be present everywhere that technology is used. Digital music services are one domain where designing for context is important given the myriad places people listen to music. One novel method to help design researchers embed themselves in everyday contexts is through remote-controlled speech agents. This paper describes a practitioner-centered case study of music service interaction researchers using a remote-controlled speech agent, called DJ Bot, to explore people's music interaction in the car and the home. DJ Bot allowed the team to conduct remote user research and contextual inquiry and to quickly explore new interactions. However, challenges using a remote speech-agent arose when adapting DJ Bot from the constrained environment of the car to the unconstrained home environment.
Delineation of stratigraphic pattern using combined application of wavelet-Fourier transform and fractal dimension: A case study over Cambay Basin, India

Recognition of stratigraphic patterns or boundaries of any formation through well log data is a very decisive and critical part of hydrocarbon exploration in the petroleum industry. In the conventional method, the stratigraphic boundaries are not clearly distinguishable due to interference of low and high frequency noise in well log data. The Fourier transform and wavelet transform are spectrum based filtering method to delineate the stratigraphic boundaries using well log data, but it has some disadvantage of introducing the undesirable component of noise from log data into result. In the present study, we used the combined application of wavelet transform and Fourier transform to demarcate the stratigraphic boundaries. This two-fold frequency filtering operation is applied to natural gamma ray log data. Apart from this, we also demarcated these boundaries by wavelet based fractal dimension value and made a comparison with the previous method. We applied the proposed method on the natural gamma-ray log because it is sensitive to only the rock matrix of formation and indicates lithology better than other logs. In a comparative study of the result of both methods, it is clear that the wavelet-Fourier method fails to identify some boundaries in cases of the thin layers while wavelet based fractal of logging signal (WBFA) is able to resolve all boundaries and well correlated with the boundary identified by the conventional method. We have tested and validate the method on three synthetic signals including a thin layer in one signal and then demonstrated to the natural gamma ray well log data of the Limbodara oil field, Cambay basin.
Digital Image Correlation of Forescatter Detector Images for Simultaneous Strain and Orientation Mapping

Improved plasticity models require simultaneous experimental local strain and microstructural evolution data. Microscopy tools, such as electron backscatter diffraction (EBSD), that can monitor transformation at the relevant length-scale, are often incompatible with digital image correlation (DIC) techniques required to determine local deformation. In this paper, the viability of forescatter detector (FSD) images as the basis for the DIC study is investigated. Standard FSD and an integrated EBSD/FSD approach (Pattern Region of Interest Analysis System: PRIAS™) are analyzed. Simultaneous strain and microstructure maps are obtained for tensile deformation of Q&P 1180 steel up to ~14% strain. Tests on an undeformed sample that is simply shifted indicate a standard deviation of error in strain of around 0.4% without additional complications from a deformed surface. The method resolves strain bands at ~2 μm spacing but does not provide significant sub-grain strain resolution. Similar resolution was obtained for mechanically polished and electropolished samples, despite electropolished surfaces presenting a smoother, simpler topography. While the resolution of the PRIAS approach depends upon the EBSD step size, the 80 nm step size used provides seemingly similar resolution as 8,000× (22.7 nm) FSD images. Surface feature evolution prevents DIC analysis across large strain steps (>6% strain), but restarting DIC, using an FSD reference image from an interim strain step, allows reasonable DIC across the stress–strain curve. Furthermore, the data are obtained easily and provide complementary information for EBSD analysis.
Visualizing Personal Rhythms

Visualizations of personal data in self-tracking systems can make even subtle shifts in mental and physical states observable, greatly influencing how health and wellness goals are set, pursued, and achieved. At the same time, recent work in data ethics cautions that standardized models can have unintended negative consequences for some user groups. Through collaborative design and critical visual analysis, this study contrasts conventional visualizations of personal data with the ways that vulnerable populations represent their lived experiences. Participants self-tracked to manage bipolar disorder, a mental illness characterized by severe and unpredictable mood changes. During design sessions, each created a series of timeline drawings depicting their experiences with mental health. Examples of adaptive and vernacular design, these images use both normative standards and individualized graphic modifications. Analysis shows that conventional visual encodings can support facets of self-assessment while also imposing problematic normative standards onto deeply personal experiences.
Analysis of Task Difficulty Sequences in a Simulation-Based POE Environment

Task difficulty (TD) reflects students' subjective judgement on the complexity of a task. We examine the task difficulty sequence data of 236 undergraduate students in a simulation-based Predict-Observe-Explain environment. The findings suggest that if students perceive the TDs as easy or hard, it may lead to poorer learning outcomes, while the medium or moderate TDs may result in better learning outcomes. In terms of TD transitions, difficulty level hard followed by a hard may lead to poorer learning outcomes. By contrast, difficulty level medium followed by a medium may lead to better learning outcomes. Understanding how task difficulties manifest over time and how they impact students' learning outcomes is useful, especially when designing for real-time educational interventions, where the difficulty of the tasks could be optimised for students. It can also help in designing and sequencing the tasks for the development of effective teaching strategies that can maximize students' learning.
Genetic-Programming-Based Architecture of Fuzzy Modeling: Towards Coping With High-Dimensional Data

This article is concerned with the development of fuzzy models realized with the aid of genetic programming (GP). The proposed architecture employs GP to form fuzzy logic expressions involving logic operators and information granules (fuzzy sets) located in the input space, used to predict information granules located in the output space. We propose an architecture realizing logic processing, with the structural optimization of the model accomplished by a multitree genetic programming and the parametric optimization completed by gradient-based learning. The granulation of information used in this architecture is developed using the Fuzzy C-means clustering algorithm. The novelty of this study is two-fold: 1) it comes with the flexibility of the logic-oriented structure of fuzzy models, and 2) our architecture is designed to handle high-dimensional data by alleviating the detrimental effect of distance concentration hampering the effectiveness of standard Takagi-Sugeno-Kang fuzzy rule-based models. The article is illustrated through some experiments that provide a detailed insight into the performance of the fuzzy models. A comprehensive comparative analysis is also covered.
LockChain Technology as One Source of Truth for Cyber, Information Security and Privacy

Implementing and maintaining Information Security (IS) in a digitized ecosystem is cumbersome. Multiple complex frameworks and models are used to implement IS, but these are perceived as hard to implement and maintain in digitized dynamic value chains and platforms. Most companies still use spreadsheets to design, direct and monitor their information security function and demonstrate their compliance. Regulators too use spreadsheets for supervision. This paper reflects on longitudinal Design Science Research (DSR) on IS and describes the design and engineering of an artefact architecture, coined as LockChain, which can emancipate boards from silo-based spreadsheet management and improve their visibility, control and assurance via integrated dash-boarding and a reporting tool. LockChain is not a traditional Information Security Management System (ISMS) but is used for the design and specification of information security requirements and measures and privacy requirements. We elaborate "Why" we used Design Science Research into valorisation of the concept of LockChain, we explain "What" we have established in terms of the technology of LockChain and "How" it is applied and the added value LockChain brings for companies on cost savings, Security and Privacy by Design engineering culture and Digital Assurance.
Dynamic neutrosophic cognitive map with improved cuckoo search algorithm (DNCM-ICSA) and ensemble classifier for rheumatoid arthritis (RA) disease

Rheumatoid Arthritis (RA) falls under the group of chronic autoimmune diseases, which affects the joints and muscles, and can lead to considerable damage to the joint structure and their functionality. RA diagnosis much in early stages is quite critical in stopping the progression of the disease. In this technical work, Dynamic Neutrosophic Cognitive Map with Improved Cuckoo Search Algorithm (DNCM-ICSA) with ensemble classifier is introduced for obtaining the gene expression profile, which distinguishes between the persons affected with RA and probable subjects of control. There are four important steps involved in this work, which include data preprocessing, feature selection, prediction and classification. The initial phase of the work comprises of data preprocessing, and second phase of the work comprises of gene selection process with T-test, chi-squared test, relief-F and Minimum Redundancy Maximum Relevance (mRMR). Next, the disease prediction is carried out using the ensemble mechanism, which increases the prediction accuracy. The ensemble mechanism integrates the process of Adaptive Neuro Fuzzy Inference System (ANFIS) and Deep Neural Networks (DNNs). The ensemble mechanism of classifiers is a group of classifiers whose decisions individually are integrated generally by weighted means for the classification of new RA examples. The disease of the patients may be avoided from getting to the severe stages. At last, DNCM-ICSA algorithm is utilized for gene classification. The results of the newly introduced classifier are analysed in terms of the metrics including precision, recall, F-measure and accuracy.
Exploring Hybrid Virtual-Physical Homes

There has been a great deal of research regarding the ways that people conceptualize and interact with places they call "home." This work has been extended to virtual reality primarily in the context of design tools. Here, we explore the idea of a hybrid virtual-physical home that uses virtual space to supplement an existing physical home. We identify variation in the spatial context of place attachment to homes. A user study (N = 25) revealed the expectations and needs these groups have for virtual home spaces, including security, familiarity, and community. We prototyped and playtested three virtual rooms, and identified design features that supported feelings of home. Finally, we present challenges to designing home-like VR environments that can shape future research in this area. As virtual reality grows increasingly accessible, these findings will inform the design of virtual spaces.
Exploring Automated Q&amp;A Support System for Maternal and Child Health in Rural India

Providing effective and efficient maternal and child healthcare is a challenge across the world. The challenge is even higher in developing countries, like India, where the doctor-to-patient ratio does not meet WHO prescribed numbers. Inadequate access to medical experts means that even simple queries about healthcare are not answered. In this work, we present our initial findings of using state-of-the-art Q&A systems, like IBM Watson, Rasa, and custom retrieval-based model, for answering simple queries related to maternal and child health. The query database, formed of short questions, is obtained from a real-world experiment where a medical expert answered the questions. The initial results are encouraging and indicate that further research in this area can help to answer a large number of queries thus reducing the work-load on a human medical expert.